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@Article{bansal07,
  author      = {Bansal, Shweta and Grenfell, Bryan T. and Meyers, Lauren Ancel},
  title       = {When individual behaviour matters: homogeneous and network models in epidemiology.},
  journal     = {J R Soc Interface},
  year        = {2007},
  volume      = {4},
  number      = {16},
  pages       = {879--891},
  month       = {Oct},
  abstract    = {Heterogeneity in host contact patterns profoundly shapes population-level disease dynamics. Many epidemiological models make simplifying assumptions about the patterns of disease-causing interactions among hosts. In particular, homogeneous-mixing models assume that all hosts have identical rates of disease-causing contacts. In recent years, several network-based approaches have been developed to explicitly model heterogeneity in host contact patterns. Here, we use a network perspective to quantify the extent to which real populations depart from the homogeneous-mixing assumption, in terms of both the underlying network structure and the resulting epidemiological dynamics. We find that human contact patterns are indeed more heterogeneous than assumed by homogeneous-mixing models, but are not as variable as some have speculated. We then evaluate a variety of methodologies for incorporating contact heterogeneity, including network-based models and several modifications to the simple SIR compartmental model. We conclude that the homogeneous-mixing compartmental model is appropriate when host populations are nearly homogeneous, and can be modified effectively for a few classes of non-homogeneous networks. In general, however, network models are more intuitive and accurate for predicting disease spread through heterogeneous host populations.},
  doi         = {10.1098/rsif.2007.1100},
  institution = {Computational and Applied Mathematics, Institute for Computational Engineering and Sciences, University of Texas at Austin, 1 University Station, C0200, Austin, TX 78712, USA.},
  keywords    = {Disease Transmission, Infectious; Epidemiologic Methods; Humans; Models, Biological; Models, Statistical},
  language    = {eng},
  medline-pst = {ppublish},
  pii         = {080KX13732480384},
  pmc         = {PMC2394553},
  pmid        = {17640863},
  url         = {http://dx.doi.org/10.1098/rsif.2007.1100},
}

@Article{bansal10,
  author      = {Bansal, Shweta and Read, Jonathan and Pourbohloul, Babak and Meyers, Lauren Ancel},
  title       = {The dynamic nature of contact networks in infectious disease epidemiology.},
  journal     = {J Biol Dyn},
  year        = {2010},
  volume      = {4},
  number      = {5},
  pages       = {478--489},
  month       = {Sep},
  abstract    = {Although contact network models have yielded important insights into infectious disease transmission and control throughout the last decade, researchers have just begun to explore the dynamic nature of contact patterns and their epidemiological significance. Most network models have assumed that contacts are static through time. Developing more realistic models of the social interactions that underlie the spread of infectious diseases thus remains an important challenge for both data gatherers and modelers. In this article, we review some recent data-driven and process-driven approaches that capture the dynamics of human contact, and discuss future challenges for the field.},
  doi         = {10.1080/17513758.2010.503376},
  institution = {Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA. shweta@sbansal.com},
  keywords    = {Communicable Diseases, epidemiology; Humans; Interpersonal Relations; Models, Biological; Statistics as Topic},
  language    = {eng},
  medline-pst = {ppublish},
  pmid        = {22877143},
  url         = {http://dx.doi.org/10.1080/17513758.2010.503376},
}

@Article{bansal12,
  author      = {Bansal, Shweta and Meyers, Lauren Ancel},
  title       = {The impact of past epidemics on future disease dynamics.},
  journal     = {J Theor Biol},
  year        = {2012},
  volume      = {309},
  pages       = {176--184},
  month       = {Sep},
  abstract    = {Many pathogens spread primarily via direct contact between infected and susceptible hosts. Thus, the patterns of contacts or contact network of a population fundamentally shape the course of epidemics. While there is a robust and growing theory for the dynamics of single epidemics in networks, we know little about the impacts of network structure on long-term epidemic or endemic transmission. For seasonal diseases like influenza, pathogens repeatedly return to populations with complex and changing patterns of susceptibility and immunity acquired through prior infection. Here, we develop two mathematical approaches for modeling consecutive seasonal outbreaks of a partially-immunizing infection in a population with contact heterogeneity. Using methods from percolation theory we consider both leaky immunity, where all previously infected individuals gain partial immunity, and polarized immunity, where a fraction of previously infected individuals are fully immune. By restructuring the epidemiologically active portion of their host population, such diseases limit the potential of future outbreaks. We speculate that these dynamics can result in evolutionary pressure to increase infectiousness.},
  doi         = {10.1016/j.jtbi.2012.06.012},
  institution = {Center for Infectious Disease Dynamics, The Pennsylvania State University, 208 Mueller Lab, University Park, PA 16802, USA. shweta@sbansal.com},
  keywords    = {Disease; Epidemics; Immune Evasion, immunology; Immunity, immunology; Models, Biological; Seasons},
  language    = {eng},
  medline-pst = {ppublish},
  pii         = {S0022-5193(12)00299-8},
  pmid        = {22721993},
  url         = {http://dx.doi.org/10.1016/j.jtbi.2012.06.012},
}


@Article{barton14,
  Title                    = {Subtype diversity and reassortment potential for co-circulating avian influenza viruses at a diversity hot spot.},
  Author                   = {Barton, Heather D. and Rohani, Pejman and Stallknecht, David E. and Brown, Justin and Drake, John M.},
  Journal                  = {J Anim Ecol},
  Year                     = {2014},

  Month                    = {May},
  Number                   = {3},
  Pages                    = {566--575},
  Volume                   = {83},

  Abstract                 = {Biological diversity has long been used to measure ecological health. While evidence exists from many ecosystems that declines in host biodiversity may lead to greater risk of disease emergence, the role of pathogen diversity in the emergence process remains poorly understood. Particularly, because a more diverse pool of pathogen types provides more ways in which evolutionary innovations may arise, we suggest that host-pathogen systems with high pathogen diversity are more prone to disease emergence than systems with relatively homogeneous pathogen communities. We call this prediction the diversity-emergence hypothesis. To show how this hypothesis could be tested, we studied a system comprised of North American shorebirds and their associated low-pathogenicity avian influenza (LPAI) viruses. These viruses are important as a potential source of genetic innovations in influenza. A theoretical contribution of this study is an expression predicting the rate of viral subtype reassortment to be proportional to both prevalence and Simpson's Index, a formula that has been used traditionally to quantify biodiversity. We then estimated prevalence and subtype diversity in host species at Delaware Bay, a North American AIV hotspot, and used our model to extrapolate from these data. We estimated that 4 to 39 virus subtypes circulated at Delaware Bay each year between 2000 and 2008, and that surveillance coverage (percentage of co-circulating subtypes collected) at Delaware Bay is only about 63.0\%. Simpson's Index in the same period varied more than fourfold from 0.22 to 0.93. These measurements together with the model provide an indirect, model-based estimate of the reassortment rate. A proper test of the diversity-emergence hypothesis would require these results to be joined to independent and reliable estimates of reassortment, perhaps obtained through molecular surveillance. These results suggest both that subtype diversity (and therefore reassortment) varies from year to year and that several subtypes contributing to reassortment are going undetected. The similarity between these results and more detailed studies of one host, ruddy turnstone (Arenaria interpres), further suggests that this species may be the primary host for influenza reassortment at Delaware Bay. Biological diversity has long been quantified using Simpson's Index. Our model links this formula to a mechanistic account of reassortment in multipathogen systems in the form of subtype diversity at Delaware Bay, USA. As a theory of how pathogen diversity may influence the evolution of novel pathogens, this work is a contribution to the larger project of understanding the connections between biodiversity and disease.},
  Doi                      = {10.1111/1365-2656.12167},
  Institution              = {Odum School of Ecology, University of Georgia, Athens, GA, 30602, USA.},
  Keywords                 = {Animal Migration; Animals; Charadriiformes, virology; Delaware, epidemiology; Influenza A virus, classification/genetics/physiology; Influenza in Birds, epidemiology/virology; Models, Biological; New Jersey, epidemiology; Prevalence; Reassortant Viruses; Species Specificity},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Pmc                      = {PMC4000580},
  Pmid                     = {24164627},
  Url                      = {http://dx.doi.org/10.1111/1365-2656.12167}
}

@Article{begon02,
  author      = {Begon, M. and Bennett, M. and Bowers, R. G. and French, N. P. and Hazel, S. M. and Turner, J.},
  title       = {A clarification of transmission terms in host-microparasite models: numbers, densities and areas.},
  journal     = {Epidemiol Infect},
  year        = {2002},
  volume      = {129},
  number      = {1},
  pages       = {147--153},
  month       = {Aug},
  abstract    = {Transmission is the driving force in the dynamics of any infectious disease. A crucial element in understanding disease dynamics, therefore, is the 'transmission term' describing the rate at which susceptible hosts are 'converted' into infected hosts by their contact with infectious material. Recently, the conventional form of this term has been increasingly questioned, and new terminologies and conventions have been proposed. Here, therefore, we review the derivation of transmission terms, explain the basis of confusion, and provide clarification. The root of the problem has been a failure to include explicit consideration of the area occupied by a host population, alongside both the number of infectious hosts and their density within the population. We argue that the terms 'density-dependent transmission' and 'frequency-dependent transmission' remain valid and useful (though a 'fuller' transmission term for the former is identified), but that the terms 'mass action', 'true mass action' and 'pseudo mass action' are all unhelpful and should be dropped. Also, contrary to what has often been assumed, the distinction between homogeneous and heterogeneous mixing in a host population is orthogonal to the distinction between density- and frequency-dependent transmission modes.},
  keywords    = {Disease Transmission, Infectious; Humans; Models, Biological},
  language    = {eng},
  medline-pst = {ppublish},
  owner       = {andreas},
  pmid        = {12211582},
  timestamp   = {2016.09.01},
}

@Article{breban09,
  Title                    = {The role of environmental transmission in recurrent avian influenza epidemics.},
  Author                   = {Breban, Romulus and Drake, John M. and Stallknecht, David E. and Rohani, Pejman},
  Journal                  = {PLoS Comput Biol},
  Year                     = {2009},

  Month                    = {Apr},
  Number                   = {4},
  Pages                    = {e1000346},
  Volume                   = {5},
  Abstract                 = {Avian influenza virus (AIV) persists in North American wild waterfowl, exhibiting major outbreaks every 2-4 years. Attempts to explain the patterns of periodicity and persistence using simple direct transmission models are unsuccessful. Motivated by empirical evidence, we examine the contribution of an overlooked AIV transmission mode: environmental transmission. It is known that infectious birds shed large concentrations of virions in the environment, where virions may persist for a long time. We thus propose that, in addition to direct fecal/oral transmission, birds may become infected by ingesting virions that have long persisted in the environment. We design a new host-pathogen model that combines within-season transmission dynamics, between-season migration and reproduction, and environmental variation. Analysis of the model yields three major results. First, environmental transmission provides a persistence mechanism within small communities where epidemics cannot be sustained by direct transmission only (i.e., communities smaller than the critical community size). Second, environmental transmission offers a parsimonious explanation of the 2-4 year periodicity of avian influenza epidemics. Third, very low levels of environmental transmission (i.e., few cases per year) are sufficient for avian influenza to persist in populations where it would otherwise vanish.},
  Doi                      = {10.1371/journal.pcbi.1000346},
  Institution              = {Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America. breban@gmail.com},
  Keywords                 = {Animals; Birds; Disease Outbreaks, veterinary; Environmental Exposure; Influenza A virus, pathogenicity/physiology; Influenza in Birds, transmission/virology; Models, Biological},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Pmc                      = {PMC2660440},
  Pmid                     = {19360126},
  Url                      = {http://dx.doi.org/10.1371/journal.pcbi.1000346}
}

@Article{breban10,
  Title                    = {A general multi-strain model with environmental transmission: invasion conditions for the disease-free and endemic states.},
  Author                   = {Breban, Romulus and Drake, John M. and Rohani, Pejman},
  Journal                  = {J Theor Biol},
  Year                     = {2010},

  Month                    = {Jun},
  Number                   = {3},
  Pages                    = {729--736},
  Volume                   = {264},
  Abstract                 = {Although many infectious diseases of humans and wildlife are transmitted via an environmental reservoir, the theory of environmental transmission remains poorly elaborated. Here we introduce an SIR-type multi-strain disease transmission model with perfect cross immunity where environmental transmission is broadly defined by three axioms. We establish the conditions under which a multi-strain endemic state is invaded by another strain which is both directly and environmentally transmitted. We discuss explicit forms for environmental transmission terms and apply our newly derived invasion conditions to a two-strain system. Then, we consider the case of two strains with matching basic reproduction numbers (i.e., R(0)), one directly transmitted only and the other both directly and environmentally transmitted, invading each other's endemic state. We find that the strain which is only directly transmitted can invade the endemic state of the strain with mixed transmission. However, the endemic state of the first strain is neutrally stable to invasion by the second strain. Thus, our results suggest that environmental transmission makes the endemic state less resistant to invasion.},
  Doi                      = {10.1016/j.jtbi.2010.03.005},
  Institution              = {Odum School of Ecology, University of Georgia, Athens, GA 30602, USA. romulus.breban@pasteur.fr},
  Keywords                 = {Algorithms; Animals; Communicable Diseases, epidemiology/transmission; Disease Reservoirs; Endemic Diseases, prevention /&/ control; Humans; Models, Theoretical; Population Dynamics},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Pii                      = {S0022-5193(10)00128-1},
  Pmid                     = {20211630},
  Url                      = {http://dx.doi.org/10.1016/j.jtbi.2010.03.005}
}

@Article{brown10,
  Title                    = {Prevalence of antibodies to type a influenza virus in wild avian species using two serologic assays.},
  Author                   = {Brown, Justin D. and Luttrell, M Page and Berghaus, Roy D. and Kistler, Whitney and Keeler, Shamus P. and Howey, Andrea and Wilcox, Benjamin and Hall, Jeffrey and Niles, Larry and Dey, Amanda and Knutsen, Gregory and Fritz, Kristin and Stallknecht, David E.},
  Journal                  = {J Wildl Dis},
  Year                     = {2010},

  Month                    = {Jul},
  Number                   = {3},
  Pages                    = {896--911},
  Volume                   = {46},
  Abstract                 = {Serologic testing to detect antibodies to avian influenza (AI) virus has been an underused tool for the study of these viruses in wild bird populations, which traditionally has relied on virus isolation and reverse transcriptase-polymerase chain reaction (RT-PCR). In a preliminary study, a recently developed commercial blocking enzyme-linked immunosorbent assay (bELISA) had sensitivity and specificity estimates of 82\% and 100\%, respectively, for detection of antibodies to AI virus in multiple wild bird species after experimental infection. To further evaluate the efficacy of this commercial bELISA and the agar gel immunodiffusion (AGID) test for AI virus antibody detection in wild birds, we tested 2,249 serum samples collected from 62 wild bird species, representing 10 taxonomic orders. Overall, the bELISA detected 25.4\% positive samples, whereas the AGID test detected 14.8\%. At the species level, the bELISA detected as many or more positive serum samples than the AGID in all 62 avian species. The majority of positive samples, detected by both assays, were from species that use aquatic habitats, with the highest prevalence from species in the orders Anseriformes and Charadriiformes. Conversely, antibodies to AI virus were rarely detected in the terrestrial species. The serologic data yielded by both assays are consistent with the known epidemiology of AI virus in wild birds and published reports of host range based on virus isolation and RT-PCR. The results of this research are also consistent with the aforementioned study, which evaluated the performance of the bELISA and AGID test on experimental samples. Collectively, the data from these two studies indicate that the bELISA is a more sensitive serologic assay than the AGID test for detecting prior exposure to AI virus in wild birds. Based on these results, the bELISA is a reliable species-independent assay with potentially valuable applications for wild bird AI surveillance.},
  Doi                      = {10.7589/0090-3558-46.3.896},
  Institution              = {Department of Population Health, College of Veterinary Medicine, University of Georgia, Athens, GA 30602, USA. jubrown1mailto:@uga.edu},
  Keywords                 = {Animals; Animals, Wild, virology; Anseriformes, virology; Antibodies, Viral, blood; Birds; Charadriiformes, virology; Enzyme-Linked Immunosorbent Assay, standards/veterinary; Female; Immunodiffusion, standards/veterinary; Influenza A virus, classification/immunology; Influenza in Birds, epidemiology; Male; Reproducibility of Results; Sensitivity and Specificity; Seroepidemiologic Studies; Species Specificity},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Pii                      = {46/3/896},
  Pmid                     = {20688695},
  Url                      = {http://dx.doi.org/10.7589/0090-3558-46.3.896}
}

@Article{brown12,
  Title                    = {The consequences of climate change at an avian influenza 'hotspot'.},
  Author                   = {Brown, V. L. and Rohani, Pejman},
  Journal                  = {Biol Lett},
  Year                     = {2012},

  Month                    = {Dec},
  Number                   = {6},
  Pages                    = {1036--1039},
  Volume                   = {8},

  Abstract                 = {Avian influenza viruses (AIVs) pose significant danger to human health. A key step in managing this threat is understanding the maintenance of AIVs in wild birds, their natural reservoir. Ruddy turnstones (Arenaria interpres) are an atypical bird species in this regard, annually experiencing high AIV prevalence in only one location-Delaware Bay, USA, during their spring migration. While there, they congregate on beaches, attracted by the super-abundance of horseshoe crab eggs. A relationship between ruddy turnstone and horseshoe crab (Limulus polyphemus) population sizes has been established, with a declining horseshoe crab population linked to a corresponding drop in ruddy turnstone population sizes. The effect of this interaction on AIV prevalence in ruddy turnstones has also been addressed. Here, we employ a transmission model to investigate how the interaction between these two species is likely to be altered by climate change. We explore the consequences of this modified interaction on both ruddy turnstone population size and AIV prevalence and show that, if climate change leads to a large enough mismatch in species phenology, AIV prevalence in ruddy turnstones will increase even as their population size decreases.},
  Doi                      = {10.1098/rsbl.2012.0635},
  Institution              = {Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA. vlbrown@umich.edu},
  Keywords                 = {Animals; Charadriiformes, physiology/virology; Climate Change; Delaware, epidemiology; Horseshoe Crabs, physiology; Influenza in Birds, epidemiology/transmission; Models, Biological; Population Dynamics; Prevalence; Seasons},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Pii                      = {rsbl.2012.0635},
  Pmc                      = {PMC3497130},
  Pmid                     = {22933039},
  Url                      = {http://dx.doi.org/10.1098/rsbl.2012.0635}
}

@Article{brown13,
  Title                    = {Dissecting a wildlife disease hotspot: the impact of multiple host species, environmental transmission and seasonality in migration, breeding and mortality.},
  Author                   = {Brown, V. L. and Drake, J. M. and Stallknecht, D. E. and Brown, J. D. and Pedersen, K. and Rohani, P.},
  Journal                  = {J R Soc Interface},
  Year                     = {2013},

  Month                    = {Feb},
  Number                   = {79},
  Pages                    = {20120804},
  Volume                   = {10},

  Abstract                 = {Avian influenza viruses (AIVs) have been implicated in all human influenza pandemics in recent history. Despite this, surprisingly little is known about the mechanisms underlying the maintenance and spread of these viruses in their natural bird reservoirs. Surveillance has identified an AIV 'hotspot' in shorebirds at Delaware Bay, in which prevalence is estimated to exceed other monitored sites by an order of magnitude. To better understand the factors that create an AIV hotspot, we developed and parametrized a mechanistic transmission model to study the simultaneous epizootiological impacts of multi-species transmission, seasonal breeding, host migration and mixed transmission routes. We scrutinized our model to examine the potential for an AIV hotspot to serve as a 'gateway' for the spread of novel viruses into North America. Our findings identify the conditions under which a novel influenza virus, if introduced into the system, could successfully invade and proliferate.},
  Doi                      = {10.1098/rsif.2012.0804},
  Institution              = {Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA. vlbrown@umich.edu},
  Keywords                 = {Animal Migration; Animals; Animals, Wild, immunology/virology; Bird Diseases, epidemiology/transmission/virology; Charadriiformes; Delaware, epidemiology; Ducks; Influenza in Birds, epidemiology/transmission; Models, Biological; Prevalence; Seasons; Sexual Behavior, Animal, physiology; Species Specificity},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Pii                      = {rsif.2012.0804},
  Pmc                      = {PMC3565696},
  Pmid                     = {23173198},
  Url                      = {http://dx.doi.org/10.1098/rsif.2012.0804}
}

@Article{brown14,
  Title                    = {Neutrality, cross-immunity and subtype dominance in avian influenza viruses.},
  Author                   = {Brown, Vicki L. and Drake, John M. and Barton, Heather D. and Stallknecht, David E. and Brown, Justin D. and Rohani, Pejman},
  Journal                  = {PLoS One},
  Year                     = {2014},
  Number                   = {2},
  Pages                    = {e88817},
  Volume                   = {9},

  Abstract                 = {Avian influenza viruses (AIVs) are considered a threat for their potential to seed human influenza pandemics. Despite their acknowledged importance, there are significant unknowns regarding AIV transmission dynamics in their natural hosts, wild birds. Of particular interest is the difference in subtype dynamics between human and bird populations-in human populations, typically only two or three subtypes cocirculate, while avian populations are capable of simultaneously hosting a multitude of subtypes. One species in particular-ruddy turnstones (Arenaria interpres)--has been found to harbour a very wide range of AIV subtypes, which could make them a key player in the spread of new subtypes in wild bird populations. Very little is known about the mechanisms that drive subtype dynamics in this species, and here we address this gap in our knowledge. Taking advantage of two independent sources of data collected from ruddy turnstones in Delaware Bay, USA, we examine patterns of subtype diversity and dominance at this site. We compare these patterns to those produced by a stochastic, multi-strain transmission model to investigate possible mechanisms that are parsimonious with the observed subtype dynamics. We find, in agreement with earlier experimental work, that subtype differences are unnecessary to replicate the observed dynamics, and that neutrality alone is sufficient. We also evaluate the role of subtype cross-immunity and find that it is not necessary to generate patterns consistent with observations. This work offers new insights into the mechanisms behind subtype diversity and dominance in a species that has the potential to be a key player in AIV dynamics in wild bird populations.},
  Doi                      = {10.1371/journal.pone.0088817},
  Institution              = { Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, United States of America ; Center for the Study of Complex Systems, University of Michigan, Ann Arbor, Michigan, United States of America ; Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America.},
  Keywords                 = {Animals; Birds, immunology/virology; Delaware; Influenza A virus, immunology; Influenza in Birds, immunology/virology; Species Specificity},
  Language                 = {eng},
  Medline-pst              = {epublish},
  Pii                      = {PONE-D-12-39950},
  Pmc                      = {PMC3934864},
  Pmid                     = {24586401},
  Url                      = {http://dx.doi.org/10.1371/journal.pone.0088817}
}

@Article{diekmann10,
  Title                    = {The construction of next-generation matrices for compartmental epidemic models.},
  Author                   = {Diekmann, O. and Heesterbeek, J A P. and Roberts, M. G.},
  Journal                  = {J R Soc Interface},
  Year                     = {2010},

  Month                    = {Jun},
  Number                   = {47},
  Pages                    = {873--885},
  Volume                   = {7},

  Abstract                 = {The basic reproduction number (0) is arguably the most important quantity in infectious disease epidemiology. The next-generation matrix (NGM) is the natural basis for the definition and calculation of (0) where finitely many different categories of individuals are recognized. We clear up confusion that has been around in the literature concerning the construction of this matrix, specifically for the most frequently used so-called compartmental models. We present a detailed easy recipe for the construction of the NGM from basic ingredients derived directly from the specifications of the model. We show that two related matrices exist which we define to be the NGM with large domain and the NGM with small domain. The three matrices together reflect the range of possibilities encountered in the literature for the characterization of (0). We show how they are connected and how their construction follows from the basic model ingredients, and establish that they have the same non-zero eigenvalues, the largest of which is the basic reproduction number (0). Although we present formal recipes based on linear algebra, we encourage the construction of the NGM by way of direct epidemiological reasoning, using the clear interpretation of the elements of the NGM and of the model ingredients. We present a selection of examples as a practical guide to our methods. In the appendix we present an elementary but complete proof that (0) defined as the dominant eigenvalue of the NGM for compartmental systems and the Malthusian parameter r, the real-time exponential growth rate in the early phase of an outbreak, are connected by the properties that (0) > 1 if and only if r > 0, and (0) = 1 if and only if r = 0.},
  Doi                      = {10.1098/rsif.2009.0386},
  Institution              = {Department of Mathematics, Utrecht University, Budapestlaan 6, 3584 CD, Utrecht, The Netherlands.},
  Keywords                 = {Animals; Artifacts; Basic Reproduction Number; Disease Outbreaks, statistics /&/ numerical data; Humans; Population Dynamics},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Pii                      = {rsif.2009.0386},
  Pmc                      = {PMC2871801},
  Pmid                     = {19892718},
  Url                      = {http://dx.doi.org/10.1098/rsif.2009.0386}
}

@Article{galvani05,
  author      = {Galvani, Alison P. and May, Robert M.},
  title       = {Epidemiology: dimensions of superspreading.},
  journal     = {Nature},
  year        = {2005},
  volume      = {438},
  number      = {7066},
  pages       = {293--295},
  month       = {Nov},
  doi         = {10.1038/438293a},
  keywords    = {Contact Tracing; Disease Transmission, Infectious, statistics /&/ numerical data; Female; HIV Infections, epidemiology/transmission; Humans; Male; Models, Biological; Severe Acute Respiratory Syndrome, epidemiology/transmission},
  language    = {eng},
  medline-pst = {ppublish},
  owner       = {andreas},
  pii         = {438293a},
  pmid        = {16292292},
  timestamp   = {2016.10.10},
  url         = {http://dx.doi.org/10.1038/438293a},
}

@Article{handel07,
  author =      {Handel, Andreas and Longini, Jr, Ira M and Antia, Rustom},
  title =       {Neuraminidase inhibitor resistance in influenza: assessing the danger of its generation and spread.},
  journal =     {PLoS Comput Biol},
  year =        {2007},
  volume =      {3},
  number =      {12},
  pages =       {e240},
  month =       {Dec},
  abstract =    {Neuraminidase Inhibitors (NI) are currently the most effective drugs against influenza. Recent cases of NI resistance are a cause for concern. To assess the danger of NI resistance, a number of studies have reported the fraction of treated patients from which resistant strains could be isolated. Unfortunately, those results strongly depend on the details of the experimental protocol. Additionally, knowing the fraction of patients harboring resistance is not too useful by itself. Instead, we want to know how likely it is that an infected patient can generate a resistant infection in a secondary host, and how likely it is that the resistant strain subsequently spreads. While estimates for these parameters can often be obtained from epidemiological data, such data is lacking for NI resistance in influenza. Here, we use an approach that does not rely on epidemiological data. Instead, we combine data from influenza infections of human volunteers with a mathematical framework that allows estimation of the parameters that govern the initial generation and subsequent spread of resistance. We show how these parameters are influenced by changes in drug efficacy, timing of treatment, fitness of the resistant strain, and details of virus and immune system dynamics. Our study provides estimates for parameters that can be directly used in mathematical and computational models to study how NI usage might lead to the emergence and spread of resistance in the population. We find that the initial generation of resistant cases is most likely lower than the fraction of resistant cases reported. However, we also show that the results depend strongly on the details of the within-host dynamics of influenza infections, and most importantly, the role the immune system plays. Better knowledge of the quantitative dynamics of the immune response during influenza infections will be crucial to further improve the results.},
  doi =         {10.1371/journal.pcbi.0030240},
  institution = {Department of Biology, Emory University, Atlanta, Georgia, United States of America. ahandel@emory.edu},
  keywords =    {Antiviral Agents, therapeutic use; Computer Simulation; Disease Outbreaks, prevention /&/ control; Drug Resistance, Viral, immunology; Humans; Influenza A virus, physiology; Influenza, Human, drug therapy/epidemiology/immunology/transmission/virology; Models, Biological; Models, Immunological; Neuraminidase, antagonists /&/ inhibitors; Risk Assessment, methods; Risk Factors; Virus Replication, immunology},
  language =    {eng},
  medline-pst = {ppublish},
  pii =         {07-PLCB-RA-0281},
  pmc =         {PMC2134965},
  pmid =        {18069885},
  url =         {http://dx.doi.org/10.1371/journal.pcbi.0030240}
}

@Article{handel07a,
  author =      {Handel, Andreas and Longini, Jr, Ira M and Antia, Rustom},
  title =       {What is the best control strategy for multiple infectious disease outbreaks?},
  journal =     {Proc Biol Sci},
  year =        {2007},
  volume =      {274},
  number =      {1611},
  pages =       {833--837},
  month =       {Mar},
  abstract =    {Effective control of infectious disease outbreaks is an important public health goal. In a number of recent studies, it has been shown how different intervention measures like travel restrictions, school closures, treatment and prophylaxis might allow us to control outbreaks of diseases, such as SARS, pandemic influenza and others. In these studies, control of a single outbreak is considered. It is, however, not clear how one should handle a situation where multiple outbreaks are likely to occur. Here, we identify the best control strategy for such a situation. We further discuss ways in which such a strategy can be implemented to achieve additional public health objectives.},
  doi =         {10.1098/rspb.2006.0015},
  institution = {Department of Biology, Emory University, Atlanta, GA 30322, USA. ahandel@emory.edu},
  keywords =    {Communicable Disease Control; Computer Simulation; Decision Support Techniques; Disease Outbreaks, prevention /&/ control; Models, Biological},
  language =    {eng},
  medline-pst = {ppublish},
  pii =         {C7Q758640Q515247},
  pmc =         {PMC2093965},
  pmid =        {17251095},
  url =         {http://dx.doi.org/10.1098/rspb.2006.0015}
}

@Article{handel09,
  author =      {Handel, Andreas and Longini, Jr, Ira M and Antia, Rustom},
  title =       {Intervention strategies for an influenza pandemic taking into account secondary bacterial infections.},
  journal =     {Epidemics},
  year =        {2009},
  volume =      {1},
  number =      {3},
  pages =       {185--195},
  month =       {Sep},
  abstract =    {Influenza infections often predispose individuals to consecutive bacterial infections. Both during seasonal and pandemic influenza outbreaks, morbidity and mortality due to secondary bacterial infections can be substantial. With the help of a mathematical model, we investigate the potential impact of such bacterial infections during an influenza pandemic, and we analyze how antiviral and antibacterial treatment or prophylaxis affect morbidity and mortality. We consider different scenarios for the spread of bacteria, the emergence of antiviral resistance, and different levels of severity for influenza infections (1918-like and 2009-like). We find that while antibacterial intervention strategies are unlikely to play an important role in reducing the overall number of cases, such interventions can lead to a significant reduction in mortality and in the number of bacterial infections. Antibacterial interventions become even more important if one considers the--very likely--scenario that during a pandemic outbreak, influenza strains resistant to antivirals emerge. Overall, our study suggests that pandemic preparedness plans should consider intervention strategies based on antibacterial treatment or prophylaxis through drugs or vaccines as part of the overall control strategy. A major caveat for our results is the lack of data that would allow precise estimation of many of the model parameters. As our results show, this leads to very large uncertainty in model outcomes. As we discuss, precise assessment of the impact of antibacterial strategies during an influenza pandemic will require the collection of further data to better estimate key parameters, especially those related to the bacterial infections and the impact of antibacterial intervention strategies.},
  doi =         {10.1016/j.epidem.2009.09.001},
  institution = {Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA 30602, USA. ahandel@uga.edu},
  keywords =    {Anti-Bacterial Agents, therapeutic use; Antiviral Agents, therapeutic use; Bacteria, drug effects; Bacterial Infections, complications/drug therapy/epidemiology/prevention /&/ control; Computer Simulation; Drug Resistance, Viral; Humans; Influenza, Human, complications/drug therapy/epidemiology; Models, Biological; Orthomyxoviridae, drug effects; Pandemics, prevention /&/ control; United States, epidemiology},
  language =    {eng},
  medline-pst = {ppublish},
  pmc =         {PMC2796779},
  pmid =        {20161493},
  url =         {http://dx.doi.org/10.1016/j.epidem.2009.09.001}
}

@Article{handel09a,
  author      = {Handel, Andreas and Longini, Ira M and Antia, Rustom},
  title       = {Antiviral resistance and the control of pandemic influenza: the roles of stochasticity, evolution and model details.},
  journal     = {J Theor Biol},
  year        = {2009},
  volume      = {256},
  number      = {1},
  pages       = {117--125},
  month       = {Jan},
  abstract    = {Antiviral drugs, most notably the neuraminidase inhibitors, are an important component of control strategies aimed to prevent or limit any future influenza pandemic. The potential large-scale use of antiviral drugs brings with it the danger of drug resistance evolution. A number of recent studies have shown that the emergence of drug-resistant influenza could undermine the usefulness of antiviral drugs for the control of an epidemic or pandemic outbreak. While these studies have provided important insights, the inherently stochastic nature of resistance generation and spread, as well as the potential for ongoing evolution of the resistant strain have not been fully addressed. Here, we study a stochastic model of drug resistance emergence and consecutive evolution of the resistant strain in response to antiviral control during an influenza pandemic. We find that taking into consideration the ongoing evolution of the resistant strain does not increase the probability of resistance emergence; however, it increases the total number of infecteds if a resistant outbreak occurs. Our study further shows that taking stochasticity into account leads to results that can differ from deterministic models. Specifically, we find that rapid and strong control cannot only contain a drug sensitive outbreak, it can also prevent a resistant outbreak from occurring. We find that the best control strategy is early intervention heavily based on prophylaxis at a level that leads to outbreak containment. If containment is not possible, mitigation works best at intermediate levels of antiviral control. Finally, we show that the results are not very sensitive to the way resistance generation is modeled.},
  doi         = {10.1016/j.jtbi.2008.09.021},
  institution = {Department of Biology, Emory University, Atlanta, GA 30322, USA. andreas.handel@gmail.com},
  keywords    = {Antiviral Agents, therapeutic use; Biological Evolution; Disease Outbreaks, prevention /&/ control; Drug Resistance, Viral, genetics; Humans; Influenza A virus, drug effects/genetics; Influenza, Human, drug therapy/epidemiology/prevention /&/ control; Models, Biological; Mutation; Stochastic Processes},
  language    = {eng},
  pii         = {S0022-5193(08)00492-X},
  pmc         = {PMC2624577},
  pmid        = {18952105},
  url         = {http://dx.doi.org/10.1016/j.jtbi.2008.09.021},
}

@Article{handel10,
  author =      {Handel, Andreas and Longini, Jr, Ira M and Antia, Rustom},
  title =       {Towards a quantitative understanding of the within-host dynamics of influenza A infections.},
  journal =     {J R Soc Interface},
  year =        {2010},
  volume =      {7},
  number =      {42},
  pages =       {35--47},
  month =       {Jan},
  abstract =    {Although the influenza A virus has been extensively studied, a quantitative understanding of the infection dynamics is still lacking. To make progress in this direction, we designed several mathematical models and compared them with data from influenza A infections of mice. We find that the immune response (IR) plays an important part in the infection dynamics. Both an innate and an adaptive IR are required to provide adequate explanation of the data. In contrast, regrowth of epithelial cells did not seem to be an important mechanism on the time scale of the infection. We also find that different model variants for both innate and adaptive responses fit the data well, indicating the need for additional data to allow further model discrimination.},
  doi =         {10.1098/rsif.2009.0067},
  institution = {Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA 30602, USA. ahandel@uga.edu},
  keywords =    {Animals; Computer Simulation; Disease Models, Animal; Epithelial Cells, immunology/virology; Host-Pathogen Interactions, immunology; Humans; Immunity, Innate, immunology; Influenza A virus, immunology/pathogenicity/physiology; Influenza, Human, immunology/virology; Mice; Models, Immunological},
  language =    {eng},
  medline-pst = {ppublish},
  pii =         {rsif.2009.0067},
  pmc =         {PMC2839376},
  pmid =        {19474085},
  url =         {http://dx.doi.org/10.1098/rsif.2009.0067}
}

@Article{handel13,
  Title                    = {A multi-scale analysis of influenza A virus fitness trade-offs due to temperature-dependent virus persistence.},
  Author                   = {Handel, Andreas and Brown, Justin and Stallknecht, David and Rohani, Pejman},
  Journal                  = {PLoS Comput Biol},
  Year                     = {2013},
  Number                   = {3},
  Pages                    = {e1002989},
  Volume                   = {9},

  Abstract                 = {Successful replication within an infected host and successful transmission between hosts are key to the continued spread of most pathogens. Competing selection pressures exerted at these different scales can lead to evolutionary trade-offs between the determinants of fitness within and between hosts. Here, we examine such a trade-off in the context of influenza A viruses and the differential pressures exerted by temperature-dependent virus persistence. For a panel of avian influenza A virus strains, we find evidence for a trade-off between the persistence at high versus low temperatures. Combining a within-host model of influenza infection dynamics with a between-host transmission model, we study how such a trade-off affects virus fitness on the host population level. We show that conclusions regarding overall fitness are affected by the type of link assumed between the within- and between-host levels and the main route of transmission (direct or environmental). The relative importance of virulence and immune response mediated virus clearance are also found to influence the fitness impacts of virus persistence at low versus high temperatures. Based on our results, we predict that if transmission occurs mainly directly and scales linearly with virus load, and virulence or immune responses are negligible, the evolutionary pressure for influenza viruses to evolve toward good persistence at high within-host temperatures dominates. For all other scenarios, influenza viruses with good environmental persistence at low temperatures seem to be favored.},
  Doi                      = {10.1371/journal.pcbi.1002989},
  Institution              = {Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, Georgia, United States of America. ahandel@uga.edu},
  Keywords                 = {Animals; Birds; Computational Biology; Feces, virology; Genetic Fitness, physiology; Host-Pathogen Interactions; Humans; Influenza A virus, pathogenicity/physiology; Influenza, Human, transmission/virology; Models, Biological; Orthomyxoviridae Infections, transmission/virology; Temperature},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Pii                      = {PCOMPBIOL-D-12-01335},
  Pmc                      = {PMC3605121},
  Pmid                     = {23555223},
  Url                      = {http://dx.doi.org/10.1371/journal.pcbi.1002989}
}

@Article{handel14,
  Title                    = {Trade-offs between and within scales: environmental persistence and within-host fitness of avian influenza viruses.},
  Author                   = {Handel, Andreas and Lebarbenchon, Camille and Stallknecht, David and Rohani, Pejman},
  Journal                  = {Proc Biol Sci},
  Year                     = {2014},

  Month                    = {Jul},
  Number                   = {1787},
  Volume                   = {281},

  Abstract                 = {Trade-offs between different components of a pathogen's replication and transmission cycle are thought to be common. A number of studies have identified trade-offs that emerge across scales, reflecting the tension between strategies that optimize within-host proliferation and large-scale population spread. Most of these studies are theoretical in nature, with direct experimental tests of such cross-scale trade-offs still rare. Here, we report an analysis of avian influenza A viruses across scales, focusing on the phenotype of temperature-dependent viral persistence. Taking advantage of a unique dataset that reports both environmental virus decay rates and strain-specific viral kinetics from duck challenge experiments, we show that the temperature-dependent environmental decay rate of a strain does not impact within-host virus load. Hence, for this phenotype, the scales of within-host infection dynamics and between-host environmental persistence do not seem to interact: viral fitness may be optimized on each scale without cross-scale trade-offs. Instead, we confirm the existence of a temperature-dependent persistence trade-off on a single scale, with some strains favouring environmental persistence in water at low temperatures while others reduce sensitivity to increasing temperatures. We show that this temperature-dependent trade-off is a robust phenomenon and does not depend on the details of data analysis. Our findings suggest that viruses might employ different environmental persistence strategies, which facilitates the coexistence of diverse strains in ecological niches. We conclude that a better understanding of the transmission and evolutionary dynamics of influenza A viruses probably requires empirical information regarding both within-host dynamics and environmental traits, integrated within a combined ecological and within-host framework.},
  Doi                      = {10.1098/rspb.2013.3051},
  Institution              = {Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI 48109, USA Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA.},
  Keywords                 = {Animals; Biological Evolution; Bird Diseases, transmission/virology; Ducks; Fresh Water, virology; Genetic Fitness; Humans; Influenza A virus, genetics/physiology; Influenza in Birds, transmission/virology; Influenza, Human, transmission/virology; Minnesota; Models, Biological; Phenotype; Temperature; Viral Load, veterinary},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Pii                      = {rspb.2013.3051},
  Pmc                      = {PMC4071531},
  Pmid                     = {24898369},
  Url                      = {http://dx.doi.org/10.1098/rspb.2013.3051}
}

@Article{handel15,
  Title                    = {Crossing the scale from within-host infection dynamics to between-host transmission fitness: a discussion of current assumptions and knowledge.},
  Author                   = {Handel, Andreas and Rohani, Pejman},
  Journal                  = {Philos Trans R Soc Lond B Biol Sci},
  Year                     = {2015},

  Month                    = {Aug},
  Number                   = {1675},
  Volume                   = {370},

  Abstract                 = {The progression of an infection within a host determines the ability of a pathogen to transmit to new hosts and to maintain itself in the population. While the general connection between the infection dynamics within a host and the population-level transmission dynamics of pathogens is widely acknowledged, a comprehensive and quantitative understanding that would allow full integration of the two scales is still lacking. Here, we provide a brief discussion of both models and data that have attempted to provide quantitative mappings from within-host infection dynamics to transmission fitness. We present a conceptual framework and provide examples of studies that have taken first steps towards development of a quantitative framework that scales from within-host infections to population-level fitness of different pathogens. We hope to illustrate some general themes, summarize some of the recent advances and-maybe most importantly-discuss gaps in our ability to bridge these scales, and to stimulate future research on this important topic.},
  Doi                      = {10.1098/rstb.2014.0302},
  Institution              = {Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI 48109, USA Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA.},
  Keywords                 = {Animals; HIV Infections, transmission; Host-Pathogen Interactions; Humans; Infection, transmission; Influenza, Human, transmission; Models, Biological},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Pii                      = {rstb.2014.0302},
  Pmc                      = {PMC4528500},
  Pmid                     = {26150668},
  Url                      = {http://dx.doi.org/10.1098/rstb.2014.0302}
}

@Article{keeler12,
  Title                    = {Persistence of low pathogenic avian influenza viruses in filtered surface water from waterfowl habitats in Georgia, USA.},
  Author                   = {Keeler, Shamus P. and Berghaus, Roy D. and Stallknecht, David E.},
  Journal                  = {J Wildl Dis},
  Year                     = {2012},

  Month                    = {Oct},
  Number                   = {4},
  Pages                    = {999--1009},
  Volume                   = {48},
  Abstract                 = {The natural reservoirs for avian influenza virus (AIV) are wild bird species of the orders Anseriformes and Charadriiformes. The primary route of transmission for wild birds is through fecally contaminated surface water on shared aquatic habitats. A distilled water model has shown that AIV remains infectious in water for weeks to months with pH, salinity, and temperature affecting stability. To evaluate the effect of pH, salinity, and temperature on AIV persistence in natural surface water, we measured the duration of infectivity for two common low pathogenic AIV subtypes in 15 filtered surface water samples collected from major waterfowl habitats in Georgia, USA. Trials were performed at three incubation temperatures 10, 17, and 28 C. Consistent with previous studies, pH and temperature had a significant effect on the stability of AIV in filtered surface water. Both viruses were less stable at warmer temperatures and in acidic water (pH <5.0). Due to the limited range of salinity of the field water samples, the role of salinity in AIV stability in surface water could not adequately be evaluated. Variations in persistence times between water samples with comparable pH and salinities indicated that other factors affect AIV stability in natural surface water. These results contribute to the current understanding of AIV persistence in aquatic habitats and may help in identifying areas with an increased likelihood of AIV persistence and potential transmission.},
  Doi                      = {10.7589/2011-11-314},
  Institution              = {Southeastern Cooperative Wildlife Disease Study, Department of Population Health, College of Veterinary Medicine, University of Georgia, Athens, Georgia 30602, USA. skeeler@uga.edu},
  Keywords                 = {Animals; Animals, Wild; Birds; Disease Reservoirs, veterinary/virology; Female; Georgia; Hydrogen-Ion Concentration; Influenza A virus, isolation /&/ purification/pathogenicity/physiology; Influenza in Birds, transmission/virology; Male; Microbial Viability; Temperature; Virus Shedding; Water Microbiology},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Pii                      = {48/4/999},
  Pmid                     = {23060501},
  Url                      = {http://dx.doi.org/10.7589/2011-11-314}
}

@Article{keeler13,
  Title                    = {Strain-related variation in the persistence of influenza A virus in three types of water: distilled water, filtered surface water, and intact surface water.},
  Author                   = {Keeler, Shamus P. and Lebarbenchon, Camille and Stallknecht, David E.},
  Journal                  = {Virol J},
  Year                     = {2013},
  Pages                    = {13},
  Volume                   = {10},
  Abstract                 = {The persistence of influenza A (IA) virus in aquatic habitats has been demonstrated to be a determinant for virus transmission dynamics in wild duck populations. In this study, we investigated virus strain-related variation in persistence in water for nine wild duck isolated IA viruses of three subtypes (H3N8, H4N6, and H8N4).We experimentally estimated the loss of infectivity over time in three different types of water: distilled, filtered surface water, and intact surface water. All viruses persisted longest in distilled water followed by filtered surface water with markedly reduced durations of persistence observed in the intact surface water. Strain-related variations were observed in distilled and filtered surface water but limited variation was observed in the intact surface water.Our findings suggest that the role of surface water for long-term (between years) maintenance of AI viruses in the environment may be limited, and suggest that the physico-chemical characteristics of water, as well as microorganisms, may be of strong importance. Results also indicate that the extent of strain-related variation observed in distilled water may overestimate persistence abilities for IA viruses in the wild and supports the need to develop experiments that account for these effects to assess subtype, genotype, as well as spatial and temporal variation in the persistence of IA viruses in aquatic habitats.},
  Doi                      = {10.1186/1743-422X-10-13},
  Institution              = {Southeastern Cooperative Wildlife Disease Study, Department of Population Health, College of Veterinary Medicine, The University of Georgia, Athens, GA 30602, USA. skeeler@uga.edu},
  Keywords                 = {Animals; Chick Embryo; Ducks, virology; Ecosystem; Hydrogen-Ion Concentration; Influenza A Virus, H3N8 Subtype, classification/isolation /&/ purification/pathogenicity; Madin Darby Canine Kidney Cells; Orthomyxoviridae Infections, virology; Species Specificity; Time Factors; Virus Cultivation; Water Microbiology; Water, analysis/chemistry},
  Language                 = {eng},
  Medline-pst              = {epublish},
  Pii                      = {1743-422X-10-13},
  Pmc                      = {PMC3584729},
  Pmid                     = {23289857},
  Url                      = {http://dx.doi.org/10.1186/1743-422X-10-13}
}

@Article{keeler14,
  Title                    = {Abiotic factors affecting the persistence of avian influenza virus in surface waters of waterfowl habitats.},
  Author                   = {Keeler, Shamus P. and Dalton, Melinda S. and Cressler, Alan M. and Berghaus, Roy D. and Stallknecht, David E.},
  Journal                  = {Appl Environ Microbiol},
  Year                     = {2014},

  Month                    = {May},
  Number                   = {9},
  Pages                    = {2910--2917},
  Volume                   = {80},
  Abstract                 = {Avian influenza (AI) virus can remain infectious in water for months, and virus-contaminated surface water is considered to be a source of infection within wild waterfowl populations. Previous work has characterized the effects of pH, salinity, and temperature on viral persistence in water, but most of that work was done with modified distilled water. The objective of this study was to identify the abiotic factors that influence the duration of AI virus persistence in natural surface water. Surface water samples were collected from 38 waterfowl habitats distributed across the United States. Samples were submitted to the U.S. Geological Survey National Water Quality Laboratory for chemical analysis and the University of Georgia for viral reduction time analysis. Samples were filtered with 0.22-?m filters, and the durations of persistence of three wild-bird-derived influenza A viruses within each water sample at 10, 17, and 28°C were determined. The effects of the surface water physicochemical factors on the duration of AI viral persistence in laboratory experiments were evaluated by multivariable linear regression with robust standard errors. The duration of AI virus persistence was determined to be longest in filtered surface water with a low temperature (<17°C), a neutral-to-basic pH (7.0 to 8.5), low salinity (<0.5 ppt), and a low ammonia concentration (<0.5 mg/liter). Our results also highlighted potential strain-related variation in the stability of AI virus in surface water. These results bring us closer to being able to predict the duration of AI virus persistence in surface water of waterfowl habitats.},
  Doi                      = {10.1128/AEM.03790-13},
  Institution              = {Southeast Cooperative Wildlife Disease Study, Department of Population Health, College of Veterinary Medicine, University of Georgia, Athens, Georgia, USA.},
  Keywords                 = {Animals; Animals, Wild, growth /&/ development/virology; Anseriformes, growth /&/ development/virology; Ecosystem; Fresh Water, chemistry/virology; Hydrogen-Ion Concentration; Influenza A virus, classification/genetics/isolation /&/ purification; Influenza in Birds, virology; Temperature; United States},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Pii                      = {AEM.03790-13},
  Pmc                      = {PMC3993280},
  Pmid                     = {24584247},
  Url                      = {http://dx.doi.org/10.1128/AEM.03790-13}
}

@Article{keeling02,
  author =               {Keeling, Matt J and Grenfell, Bryan T},
  title =                {Understanding the persistence of measles: reconciling theory, simulation and observation.},
  journal =              {Proceedings. Biological sciences / The Royal Society},
  year =                 {2002},
  volume =               {269},
  pages =                {335--343},
  month =                {Feb},
  abstract =             {Ever since the pattern of localized extinction associated with measles was discovered by Bartlett in 1957, many models have been developed in an attempt to reproduce this phenomenon. Recently, the use of constant infectious and incubation periods, rather than the more convenient exponential forms, has been presented as a simple means of obtaining realistic persistence levels. However, this result appears at odds with rigorous mathematical theory; here we reconcile these differences. Using a deterministic approach, we parameterize a variety of models to fit the observed biennial attractor, thus determining the level of seasonality by the choice of model. We can then compare fairly the persistence of the stochastic versions of these models, using the 'best-fit' parameters. Finally, we consider the differences between the observed fade-out pattern and the more theoretically appealing 'first passage time'.},
  citation-subset =      {IM},
  completed =            {2002-06-07},
  country =              {England},
  created =              {2002-03-11},
  issn =                 {0962-8452},
  issn-linking =         {0962-8452},
  issue =                {1489},
  journal-abbreviation = {Proc Biol Sci},
  keywords =             {Cities, epidemiology; England, epidemiology; Humans; Measles, epidemiology; Models, Biological; Models, Statistical; Population Density; Seasons; Stochastic Processes; Urban Population; Wales, epidemiology},
  nlm =                  {PMC1690899},
  nlm-id =               {101245157},
  owner =                {NLM},
  pmid =                 {11886620},
  pubmodel =             {Print},
  pubstatus =            {ppublish},
  revised =              {2014-06-13},
  status =               {MEDLINE},
  timestamp =            {2016.10.12}
}

@Article{keeling05,
  author =      {Keeling, Matt J. and Eames, Ken T D.},
  title =       {Networks and epidemic models.},
  journal =     {J R Soc Interface},
  year =        {2005},
  volume =      {2},
  number =      {4},
  pages =       {295--307},
  month =       {Sep},
  abstract =    {Networks and the epidemiology of directly transmitted infectious diseases are fundamentally linked. The foundations of epidemiology and early epidemiological models were based on population wide random-mixing, but in practice each individual has a finite set of contacts to whom they can pass infection; the ensemble of all such contacts forms a 'mixing network'. Knowledge of the structure of the network allows models to compute the epidemic dynamics at the population scale from the individual-level behaviour of infections. Therefore, characteristics of mixing networks-and how these deviate from the random-mixing norm-have become important applied concerns that may enhance the understanding and prediction of epidemic patterns and intervention measures. Here, we review the basis of epidemiological theory (based on random-mixing models) and network theory (based on work from the social sciences and graph theory). We then describe a variety of methods that allow the mixing network, or an approximation to the network, to be ascertained. It is often the case that time and resources limit our ability to accurately find all connections within a network, and hence a generic understanding of the relationship between network structure and disease dynamics is needed. Therefore, we review some of the variety of idealized network types and approximation techniques that have been utilized to elucidate this link. Finally, we look to the future to suggest how the two fields of network theory and epidemiological modelling can deliver an improved understanding of disease dynamics and better public health through effective disease control.},
  doi =         {10.1098/rsif.2005.0051},
  institution = { Mathematics, University of Warwick, Institute Gibbet Hill Road, Coventry CV4 7AL, UK. m.j.keeling@warwick.ac.uk},
  keywords =    {Animals; Communicable Diseases, Emerging, epidemiology; Computer Simulation; Disease Outbreaks, statistics /&/ numerical data; Disease Susceptibility, epidemiology; Disease Transmission, Infectious, statistics /&/ numerical data; Epidemiologic Methods; Humans; Incidence; Models, Biological; Population Dynamics; Prevalence; Risk Assessment, methods; Risk Factors; Social Support},
  language =    {eng},
  medline-pst = {ppublish},
  pii =         {L2L04UAFY5F3HYFF},
  pmc =         {PMC1578276},
  pmid =        {16849187},
  url =         {http://dx.doi.org/10.1098/rsif.2005.0051}
}

@Book{keeling08,
  Title                    = {Modeling infectious diseases in humans and animals},
  Author                   = {Keeling, Matt J and Rohani, Pejman},
  Publisher                = {Princeton University Press},
  Year                     = {2008},

  Owner                    = {andreas},
  Timestamp                = {2015.09.11}
}

@Article{lebarbenchon11,
  Title                    = {Viral replication, persistence in water and genetic characterization of two influenza A viruses isolated from surface lake water.},
  Author                   = {Lebarbenchon, Camille and Yang, My and Keeler, Shamus P. and Ramakrishnan, Muthannan A. and Brown, Justin D. and Stallknecht, David E. and Sreevatsan, Srinand},
  Journal                  = {PLoS One},
  Year                     = {2011},
  Number                   = {10},
  Pages                    = {e26566},
  Volume                   = {6},
  Abstract                 = {Water-borne transmission has been suggested as an important transmission mechanism for Influenza A (IA) viruses in wild duck populations; however, relatively few studies have attempted to detect IA viruses from aquatic habitats. Water-isolated viruses have rarely been genetically characterized and evaluation for persistence in water and infectivity in natural hosts has never been documented. In this study, we focused on two IA viruses (H3N8 and H4N6 subtypes) isolated from surface lake water in Minnesota, USA. We investigated the relative prevalence of the two virus subtypes in wild duck populations at the sampling site and their genetic relatedness to IA viruses isolated in wild waterbirds in North America. Viral persistence under different laboratory conditions (temperature and pH) and replication in experimentally infected Mallards (Anas platyrhynchos) were also characterized. Both viruses were the most prevalent subtype one year following their isolation in lake water. The viruses persisted in water for an extended time period at constant temperature (several weeks) but infectivity rapidly reduced under multiple freeze-thaw cycles. Furthermore, the two isolates efficiently replicated in Mallards. The complete genome characterization supported that these isolates originated from genetic reassortments with other IA viruses circulating in wild duck populations during the year of sampling. Based on phylogenetic analyses, we couldn't identify genetically similar viruses in duck populations in the years following their isolation from lake water. Our study supports the role for water-borne transmission for IA viruses but also highlights that additional field and experimental studies are required to support inter-annual persistence in aquatic habitats.},
  Doi                      = {10.1371/journal.pone.0026566},
  Institution              = {Southeastern Cooperative Wildlife Disease Study, Department of Population Health, College of Veterinary Medicine, University of Georgia, Athens, Georgia, United States of America. cleb@uga.edu},
  Keywords                 = {Animals; Ducks, virology; Genomics; Hydrogen-Ion Concentration; Influenza A Virus, H3N8 Subtype, classification/genetics/isolation /&/ purification/physiology; Influenza A virus, classification/genetics/isolation /&/ purification/physiology; Laboratories; Lakes, virology; Phylogeny; Seasons; Temperature; Virus Replication; Water Microbiology},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Pii                      = {PONE-D-11-08831},
  Pmc                      = {PMC3197669},
  Pmid                     = {22028909},
  Url                      = {http://dx.doi.org/10.1371/journal.pone.0026566}
}

@Article{lipsitch09,
  Title                    = {Influenza seasonality: lifting the fog.},
  Author                   = {Lipsitch, Marc and Viboud, C{\'{e}}cile},
  Journal                  = {Proc Natl Acad Sci U S A},
  Year                     = {2009},

  Month                    = {Mar},
  Number                   = {10},
  Pages                    = {3645--3646},
  Volume                   = {106},

  Doi                      = {10.1073/pnas.0900933106},
  Institution              = {Department of Epidemiology, Harvard School of Public Health, Boston, MA 02130, USA. mlipsitc@hsph.harvard.edu},
  Keywords                 = {Humans; Influenza, Human, transmission; Seasons; Vapor Pressure},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {andreas},
  Pii                      = {106/10/3645},
  Pmid                     = {19276125},
  Timestamp                = {2016.08.04},
  Url                      = {http://dx.doi.org/10.1073/pnas.0900933106}
}

@Article{lloyd-smith05,
  author =      {Lloyd-Smith, J. O. and Schreiber, S. J. and Kopp, P. E. and Getz, W. M.},
  title =       {Superspreading and the effect of individual variation on disease emergence.},
  journal =     {Nature},
  year =        {2005},
  volume =      {438},
  number =      {7066},
  pages =       {355--359},
  month =       {Nov},
  abstract =    {Population-level analyses often use average quantities to describe heterogeneous systems, particularly when variation does not arise from identifiable groups. A prominent example, central to our current understanding of epidemic spread, is the basic reproductive number, R(0), which is defined as the mean number of infections caused by an infected individual in a susceptible population. Population estimates of R(0) can obscure considerable individual variation in infectiousness, as highlighted during the global emergence of severe acute respiratory syndrome (SARS) by numerous 'superspreading events' in which certain individuals infected unusually large numbers of secondary cases. For diseases transmitted by non-sexual direct contacts, such as SARS or smallpox, individual variation is difficult to measure empirically, and thus its importance for outbreak dynamics has been unclear. Here we present an integrated theoretical and statistical analysis of the influence of individual variation in infectiousness on disease emergence. Using contact tracing data from eight directly transmitted diseases, we show that the distribution of individual infectiousness around R(0) is often highly skewed. Model predictions accounting for this variation differ sharply from average-based approaches, with disease extinction more likely and outbreaks rarer but more explosive. Using these models, we explore implications for outbreak control, showing that individual-specific control measures outperform population-wide measures. Moreover, the dramatic improvements achieved through targeted control policies emphasize the need to identify predictive correlates of higher infectiousness. Our findings indicate that superspreading is a normal feature of disease spread, and to frame ongoing discussion we propose a rigorous definition for superspreading events and a method to predict their frequency.},
  doi =         {10.1038/nature04153},
  institution = {Department of Environmental Science, Policy and Management, 140 Mulford Hall, University of California, Berkeley, California 94720-3114, USA. jls@nature.berkeley.edu},
  keywords =    {Contact Tracing; Disease Outbreaks, statistics /&/ numerical data; Disease Susceptibility; Disease Transmission, Infectious, statistics /&/ numerical data; Humans; Models, Biological; Severe Acute Respiratory Syndrome, epidemiology/transmission/virology; Singapore, epidemiology},
  language =    {eng},
  medline-pst = {ppublish},
  owner =       {andreas},
  pii =         {nature04153},
  pmid =        {16292310},
  timestamp =   {2016.10.10},
  url =         {http://dx.doi.org/10.1038/nature04153}
}

@Article{mccallum01,
  Title                    = {How should pathogen transmission be modelled?},
  Author                   = {McCallum, H. and Barlow, N. and Hone, J.},
  Journal                  = {Trends Ecol Evol},
  Year                     = {2001},

  Month                    = {Jun},
  Number                   = {6},
  Pages                    = {295--300},
  Volume                   = {16},

  Abstract                 = {Host-pathogen models are essential for designing strategies for managing disease threats to humans, wild animals and domestic animals. The behaviour of these models is greatly affected by the way in which transmission between infected and susceptible hosts is modelled. Since host-pathogen models were first developed at the beginning of the 20th century, the 'mass action' assumption has almost always been used for transmission. Recently, however, it has been suggested that mass action has often been modelled wrongly. Alternative models of transmission are beginning to appear, as are empirical tests of transmission dynamics.},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {andreas},
  Pii                      = {S0169534701021449},
  Pmid                     = {11369107},
  Timestamp                = {2016.09.01}
}

@Article{mills04,
  Title                    = {Transmissibility of 1918 pandemic influenza.},
  Author                   = {Mills, Christina E. and Robins, James M. and Lipsitch, Marc},
  Journal                  = {Nature},
  Year                     = {2004},

  Month                    = {Dec},
  Number                   = {7019},
  Pages                    = {904--906},
  Volume                   = {432},

  Abstract                 = {The 1918 influenza pandemic killed 20-40 million people worldwide, and is seen as a worst-case scenario for pandemic planning. Like other pandemic influenza strains, the 1918 A/H1N1 strain spread extremely rapidly. A measure of transmissibility and of the stringency of control measures required to stop an epidemic is the reproductive number, which is the number of secondary cases produced by each primary case. Here we obtained an estimate of the reproductive number for 1918 influenza by fitting a deterministic SEIR (susceptible-exposed-infectious-recovered) model to pneumonia and influenza death epidemic curves from 45 US cities: the median value is less than three. The estimated proportion of the population with A/H1N1 immunity before September 1918 implies a median basic reproductive number of less than four. These results strongly suggest that the reproductive number for 1918 pandemic influenza is not large relative to many other infectious diseases. In theory, a similar novel influenza subtype could be controlled. But because influenza is frequently transmitted before a specific diagnosis is possible and there is a dearth of global antiviral and vaccine stores, aggressive transmission reducing measures will probably be required.},
  Doi                      = {10.1038/nature03063},
  Institution              = {Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, Massachusetts 02115, USA.},
  Keywords                 = {Cities; Communicable Disease Control; History, 20th Century; Humans; Influenza, Human, history/mortality/prevention /&/ control/transmission; Models, Biological; Probability; Survival Analysis; Time Factors; United States, epidemiology},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {andreas},
  Pii                      = {nature03063},
  Pmid                     = {15602562},
  Timestamp                = {2015.09.11},
  Url                      = {http://dx.doi.org/10.1038/nature03063}
}

@Book{newman13,
  title =     {Networks},
  publisher = {Oxford Univ. Press},
  author =    {M. E. J. Newman},
  address =   {Oxford [u.a.]},
  edition =   {Reprint. with corr.},
  note =      {Hier auch später erschienene, unveränderte Nachdrucke},
  date =      {2013},
  isbn =      {9780199206650},
  pagetotal = {XI, 772},
  ppn_gvk =   {775902462},
  subtitle =  {An introduction}
}

@Article{pepin14,
  Title                    = {Using quantitative disease dynamics as a tool for guiding response to avian influenza in poultry in the United States of America.},
  Author                   = {Pepin, K. M. and Spackman, E. and Brown, J. D. and Pabilonia, K. L. and Garber, L. P. and Weaver, J. T. and Kennedy, D. A. and Patyk, K. A. and Huyvaert, K. P. and Miller, R. S. and Franklin, A. B. and Pedersen, K. and Bogich, T. L. and Rohani, P. and Shriner, S. A. and Webb, C. T. and Riley, S.},
  Journal                  = {Prev Vet Med},
  Year                     = {2014},

  Month                    = {Mar},
  Number                   = {4},
  Pages                    = {376--397},
  Volume                   = {113},

  Abstract                 = {Wild birds are the primary source of genetic diversity for influenza A viruses that eventually emerge in poultry and humans. Much progress has been made in the descriptive ecology of avian influenza viruses (AIVs), but contributions are less evident from quantitative studies (e.g., those including disease dynamic models). Transmission between host species, individuals and flocks has not been measured with sufficient accuracy to allow robust quantitative evaluation of alternate control protocols. We focused on the United States of America (USA) as a case study for determining the state of our quantitative knowledge of potential AIV emergence processes from wild hosts to poultry. We identified priorities for quantitative research that would build on existing tools for responding to AIV in poultry and concluded that the following knowledge gaps can be addressed with current empirical data: (1) quantification of the spatio-temporal relationships between AIV prevalence in wild hosts and poultry populations, (2) understanding how the structure of different poultry sectors impacts within-flock transmission, (3) determining mechanisms and rates of between-farm spread, and (4) validating current policy-decision tools with data. The modeling studies we recommend will improve our mechanistic understanding of potential AIV transmission patterns in USA poultry, leading to improved measures of accuracy and reduced uncertainty when evaluating alternative control strategies.},
  Doi                      = {10.1016/j.prevetmed.2013.11.011},
  Institution              = {Fogarty International Center, National Institute of Health, Bethesda, MD, USA; MRC Centre for Outbreak Analysis and Disease Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, Norfolk Place, London, UK. Electronic address: s.riley@imperial.ac.uk.},
  Keywords                 = {Animal Husbandry, legislation /&/ jurisprudence/organization /&/ administration; Animals; Birds; Disease Reservoirs, veterinary; Influenza A virus, physiology; Influenza in Birds, epidemiology/prevention /&/ control/transmission; Poultry; Poultry Diseases, epidemiology/prevention /&/ control/transmission; United States},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Pii                      = {S0167-5877(13)00361-9},
  Pmc                      = {PMC3945821},
  Pmid                     = {24462191},
  Url                      = {http://dx.doi.org/10.1016/j.prevetmed.2013.11.011}
}

@Article{roche11,
  Title                    = {The curse of the Pharaoh revisited: evolutionary bi-stability in environmentally transmitted pathogens.},
  Author                   = {Roche, Benjamin and Drake, John M. and Rohani, Pejman},
  Journal                  = {Ecol Lett},
  Year                     = {2011},

  Month                    = {Jun},
  Number                   = {6},
  Pages                    = {569--575},
  Volume                   = {14},
  Abstract                 = {It is increasingly evident that for a number of high-profile pathogens, transmission involves both direct and environmental pathways. Much of the distinguished evolutionary theory has, however, focused on each of transmission component separately. Herein, we use the framework of adaptive dynamics to study the evolutionary consequences of mixed transmission. We find that environmental transmission can select for increased virulence when direct transmission is low. Increasing the efficiency of direct transmission gives rise to an evolutionary bi-stability, with coexistence of different levels of virulence. We conclude that the overlooked contribution of environmental transmission may explain the curious appearance of high virulence in pathogens that are typically only moderately pathogenic, as observed for avian influenza viruses and cholera.},
  Doi                      = {10.1111/j.1461-0248.2011.01619.x},
  Institution              = {Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA. benjamin.roche@ird.fr},
  Keywords                 = {Biological Evolution; Disease Transmission, Infectious; Environment; Influenza A virus, genetics/pathogenicity; Models, Biological; Population Dynamics; Selection, Genetic; Vibrio cholerae, genetics/pathogenicity; Virulence, genetics},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Pmid                     = {21496194},
  Url                      = {http://dx.doi.org/10.1111/j.1461-0248.2011.01619.x}
}

@Article{roche11a,
  Title                    = {An agent-based model to study the epidemiological and evolutionary dynamics of Influenza viruses.},
  Author                   = {Roche, Benjamin and Drake, John M. and Rohani, Pejman},
  Journal                  = {BMC Bioinformatics},
  Year                     = {2011},
  Pages                    = {87},
  Volume                   = {12},
  Abstract                 = {Influenza A viruses exhibit complex epidemiological patterns in a number of mammalian and avian hosts. Understanding transmission of these viruses necessitates taking into account their evolution, which represents a challenge for developing mathematical models. This is because the phrasing of multi-strain systems in terms of traditional compartmental ODE models either requires simplifying assumptions to be made that overlook important evolutionary processes, or leads to complex dynamical systems that are too cumbersome to analyse.Here, we develop an Individual-Based Model (IBM) in order to address simultaneously the ecology, epidemiology and evolution of strain-polymorphic pathogens, using Influenza A viruses as an illustrative example.We carry out careful validation of our IBM against comparable mathematical models to demonstrate the robustness of our algorithm and the sound basis for this novel framework. We discuss how this new approach can give critical insights in the study of influenza evolution.},
  Doi                      = {10.1186/1471-2105-12-87},
  Institution              = {Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA. benjamin.roche@ird.fr},
  Keywords                 = {Biological Evolution; Humans; Influenza A virus, genetics; Influenza, Human, epidemiology/transmission/virology; Models, Biological; Reproducibility of Results},
  Language                 = {eng},
  Medline-pst              = {epublish},
  Pii                      = {1471-2105-12-87},
  Pmc                      = {PMC3078862},
  Pmid                     = {21450071},
  Url                      = {http://dx.doi.org/10.1186/1471-2105-12-87}
}

@Article{roche14,
  Title                    = {Adaptive evolution and environmental durability jointly structure phylodynamic patterns in avian influenza viruses.},
  Author                   = {Roche, Benjamin and Drake, John M. and Brown, Justin and Stallknecht, David E. and Bedford, Trevor and Rohani, Pejman},
  Journal                  = {PLoS Biol},
  Year                     = {2014},

  Month                    = {Aug},
  Number                   = {8},
  Pages                    = {e1001931},
  Volume                   = {12},

  Abstract                 = {Avian influenza viruses (AIVs) have been pivotal to the origination of human pandemic strains. Despite their scientific and public health significance, however, there remains much to be understood about the ecology and evolution of AIVs in wild birds, where major pools of genetic diversity are generated and maintained. Here, we present comparative phylodynamic analyses of human and AIVs in North America, demonstrating (i) significantly higher standing genetic diversity and (ii) phylogenetic trees with a weaker signature of immune escape in AIVs than in human viruses. To explain these differences, we performed statistical analyses to quantify the relative contribution of several potential explanations. We found that HA genetic diversity in avian viruses is determined by a combination of factors, predominantly subtype-specific differences in host immune selective pressure and the ecology of transmission (in particular, the durability of subtypes in aquatic environments). Extending this analysis using a computational model demonstrated that virus durability may lead to long-term, indirect chains of transmission that, when coupled with a short host lifespan, can generate and maintain the observed high levels of genetic diversity. Further evidence in support of this novel finding was found by demonstrating an association between subtype-specific environmental durability and predicted phylogenetic signatures: genetic diversity, variation in phylogenetic tree branch lengths, and tree height. The conclusion that environmental transmission plays an important role in the evolutionary biology of avian influenza viruses-a manifestation of the "storage effect"-highlights the potentially unpredictable impact of wildlife reservoirs for future human pandemics and the need for improved understanding of the natural ecology of these viruses.},
  Doi                      = {10.1371/journal.pbio.1001931},
  Institution              = {Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, United States of America; Center for the Study of Complex Systems, University of Michigan, Ann Arbor, Michigan, United States of America; Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America.},
  Keywords                 = {Adaptation, Physiological, genetics; Animals; Base Sequence; Birds, virology; Environment; Genetic Variation; Hemagglutinin Glycoproteins, Influenza Virus, genetics; Humans; Influenza A virus, genetics; Models, Molecular; North America; Phylogeny; Regression Analysis},
  Language                 = {eng},
  Medline-pst              = {epublish},
  Pii                      = {PBIOLOGY-D-14-00514},
  Pmc                      = {PMC4130664},
  Pmid                     = {25116957},
  Url                      = {http://dx.doi.org/10.1371/journal.pbio.1001931}
}

@Article{rohani09,
  Title                    = {Environmental transmission of low pathogenicity avian influenza viruses and its implications for pathogen invasion.},
  Author                   = {Rohani, Pejman and Breban, Romulus and Stallknecht, David E. and Drake, John M.},
  Journal                  = {Proc Natl Acad Sci U S A},
  Year                     = {2009},

  Month                    = {Jun},
  Number                   = {25},
  Pages                    = {10365--10369},
  Volume                   = {106},
  Abstract                 = {Understanding the transmission dynamics and persistence of avian influenza viruses (AIVs) in the wild is an important scientific and public health challenge because this system represents both a reservoir for recombination and a source of novel, potentially human-pathogenic strains. The current paradigm locates all important transmission events on the nearly direct fecal/oral bird-to-bird pathway. In this article, on the basis of overlooked evidence, we propose that an environmental virus reservoir gives rise to indirect transmission. This transmission mode could play an important epidemiological role. Using a stochastic model, we demonstrate how neglecting environmentally generated transmission chains could underestimate the explosiveness and duration of AIV epidemics. We show the important pathogen invasion implications of this phenomenon: the nonnegligible probability of outbreak even when direct transmission is absent, the long-term infectivity of locations of prior outbreaks, and the role of environmental heterogeneity in risk.},
  Doi                      = {10.1073/pnas.0809026106},
  Institution              = {Odum School of Ecology, University of Georgia, Athens, GA 30602, USA. rohani@uga.edu},
  Keywords                 = {Animals; Birds, virology; Environment; Humans; Influenza in Birds, epidemiology/transmission/virology; Models, Biological; Orthomyxoviridae, pathogenicity},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Pii                      = {0809026106},
  Pmc                      = {PMC2690603},
  Pmid                     = {19497868},
  Url                      = {http://dx.doi.org/10.1073/pnas.0809026106}
}

@Article{shaman10,
  Title                    = {Absolute humidity and the seasonal onset of influenza in the continental United States.},
  Author                   = {Shaman, Jeffrey and Pitzer, Virginia E. and Viboud, C{\'{e}}cile and Grenfell, Bryan T. and Lipsitch, Marc},
  Journal                  = {PLoS Biol},
  Year                     = {2010},

  Month                    = {Feb},
  Number                   = {2},
  Pages                    = {e1000316},
  Volume                   = {8},

  Abstract                 = {Much of the observed wintertime increase of mortality in temperate regions is attributed to seasonal influenza. A recent reanalysis of laboratory experiments indicates that absolute humidity strongly modulates the airborne survival and transmission of the influenza virus. Here, we extend these findings to the human population level, showing that the onset of increased wintertime influenza-related mortality in the United States is associated with anomalously low absolute humidity levels during the prior weeks. We then use an epidemiological model, in which observed absolute humidity conditions temper influenza transmission rates, to successfully simulate the seasonal cycle of observed influenza-related mortality. The model results indicate that direct modulation of influenza transmissibility by absolute humidity alone is sufficient to produce this observed seasonality. These findings provide epidemiological support for the hypothesis that absolute humidity drives seasonal variations of influenza transmission in temperate regions.},
  Doi                      = {10.1371/journal.pbio.1000316},
  Institution              = {College of Oceanic and Atmospheric Sciences, Oregon State University, Corvallis, Oregon, United States of America.},
  Keywords                 = {Humans; Humidity; Influenza, Human, epidemiology/transmission/virology; Models, Statistical; Seasons; United States},
  Language                 = {eng},
  Medline-pst              = {epublish},
  Owner                    = {andreas},
  Pmid                     = {20186267},
  Timestamp                = {2016.08.04},
  Url                      = {http://dx.doi.org/10.1371/journal.pbio.1000316}
}

@Article{viboud06,
  Title                    = {Transmissibility and mortality impact of epidemic and pandemic influenza, with emphasis on the unusually deadly 1951 epidemic.},
  Author                   = {Viboud, C{\'{e}}cile and Tam, Theresa and Fleming, Douglas and Handel, Andreas and Miller, Mark A. and Simonsen, Lone},
  Journal                  = {Vaccine},
  Year                     = {2006},

  Month                    = {Nov},
  Number                   = {44-46},
  Pages                    = {6701--6707},
  Volume                   = {24},

  Abstract                 = {There are important gaps in our current understanding of the influenza virus behavior. In particular, it remains unclear why some inter-pandemic seasons are associated with unusually high mortality impact, sometimes comparable to that of pandemics. Here we compare the epidemiological patterns of the unusually deadly 1951 influenza epidemic (A/H1N1) in England and Wales and Canada with those of surrounding epidemic and pandemic seasons, in terms of overall mortality impact and transmissibility. Based on the statistical and mathematical analysis of vital statistics and morbidity epidemic curves in these two countries, we show that the 1951 epidemic was associated with both higher mortality impact and higher transmissibility than the 1957 and 1968 pandemics. Surprisingly in Liverpool, considered the 'epicenter' of the severe 1951 epidemic, the mortality impact and transmissibility even surpassed the 1918 pandemic.},
  Doi                      = {10.1016/j.vaccine.2006.05.067},
  Institution              = {Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA. viboudc@mail.nih.gov},
  Keywords                 = {Communicable Disease Control; Disease Outbreaks, history/statistics /&/ numerical data; History, 20th Century; Humans; Influenza A Virus, H1N1 Subtype; Influenza, Human, epidemiology/history/mortality/transmission; Models, Biological},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {andreas},
  Pii                      = {S0264-410X(06)00648-7},
  Pmid                     = {16806596},
  Timestamp                = {2015.09.11},
  Url                      = {http://dx.doi.org/10.1016/j.vaccine.2006.05.067}
}

@Article{wallinga04,
  Title                    = {{Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures}},
  Author                   = {Jacco Wallinga and Peter Teunis},
  Journal                  = {Am J Epidemiol},
  Year                     = {2004},

  Month                    = sep,
  Number                   = {6},
  Pages                    = {509--516},
  Volume                   = {160},

  Bdsk-url-1               = {http://dx.doi.org/10.1093/aje/kwh255},
  Date-modified            = {2008-03-06 11:49:46 -0500},
  Doi                      = {10.1093/aje/kwh255},
  Owner                    = {andreas},
  Pii                      = {160/6/509},
  Pmid                     = {15353410},
  Timestamp                = {2006.05.12}
}

@Article{wallinga07,
  Title                    = {{How generation intervals shape the relationship between growth rates and reproductive numbers}},
  Author                   = {Wallinga, J. and Lipsitch, M.},
  Journal                  = {Proceedings of the Royal Society B},
  Year                     = {2007},

  Month                    = feb,
  Number                   = {1609},
  Pages                    = {599--604},
  Volume                   = {274},

  Date-modified            = {2008-03-06 11:50:41 -0500},
  Owner                    = {andreas},
  Timestamp                = {2007.03.07}
}

@Article{yorke78,
  Title                    = {Dynamics and control of the transmission of gonorrhea.},
  Author                   = {Yorke, J. A. and Hethcote, H. W. and Nold, A.},
  Journal                  = {Sex Transm Dis},
  Year                     = {1978},
  Number                   = {2},
  Pages                    = {51--56},
  Volume                   = {5},
  Abstract                 = {Calculations revealed that approximately a third of the reported cases of gonorrhea in women during 1973-1975 were discoveries of the screening program. Theoretical models and epidemiologic data showed that the prevalence of gonorrhea adjusts rapidly to changes in social behavior, medical treatment, and control programs, that prevalence oscillates seasonally around an equilibrium state determined by the current social and medical conditions, and that this equilibrium moves as epidemiologic conditions change. The incidence of gonorrhea is theoretically limited by saturation in a sexually active core population, and this core causes gonorrhea to remain endemic.},
  Institution              = {Institute for Physical Science and Technology, University of Maryland, College Park, USA.},
  Keywords                 = {Female; Gonorrhea, epidemiology/prevention /&/ control/transmission; Humans; Incidence; Male; Mass Screening; Prevalence; Seasons; Sex Distribution; United States, epidemiology},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {andreas},
  Pmid                     = {10328031},
  Timestamp                = {2016.10.07}
}

@Book{vynnycky10,
  author    = {Vynnycky, Emilia and White, Richard},
  publisher = {Oxford University Press},
  title     = {An introduction to infectious disease modelling},
  year      = {2010},
  owner     = {andreas},
  timestamp = {2017.02.20},
}

@Article{milwid16,
  author       = {Milwid, Rachael and Steriu, Andreea and Arino, Julien and Heffernan, Jane and Hyder, Ayaz and Schanzer, Dena and Gardner, Emma and Haworth-Brockman, Margaret and Isfeld-Kiely, Harpa and Langley, Joanne M and Moghadas, Seyed M},
  title        = {Toward Standardizing a Lexicon of Infectious Disease Modeling Terms.},
  journal      = {Frontiers in public health},
  year         = {2016},
  volume       = {4},
  pages        = {213},
  abstract     = {Disease modeling is increasingly being used to evaluate the effect of health intervention strategies, particularly for infectious diseases. However, the utility and application of such models are hampered by the inconsistent use of infectious disease modeling terms between and within disciplines. We sought to standardize the lexicon of infectious disease modeling terms and develop a glossary of terms commonly used in describing models' assumptions, parameters, variables, and outcomes. We combined a comprehensive literature review of relevant terms with an online forum discussion in a virtual community of practice, mod4PH (Modeling for Public Health). Using a convergent discussion process and consensus amongst the members of mod4PH, a glossary of terms was developed as an online resource. We anticipate that the glossary will improve inter- and intradisciplinary communication and will result in a greater uptake and understanding of disease modeling outcomes in heath policy decision-making. We highlight the role of the mod4PH community of practice and the methodologies used in this endeavor to link theory, policy, and practice in the public health domain.},
  country      = {Switzerland},
  created      = {2016-10-13},
  doi          = {10.3389/fpubh.2016.00213},
  issn-linking = {2296-2565},
  keywords     = {community of practice; infectious disease modeling; lexicon of terms; public health; reproduction number},
  nlm-id       = {101616579},
  owner        = {NLM},
  pmc          = {PMC5039191},
  pmid         = {27734014},
  pubmodel     = {Electronic-eCollection},
  pubstatus    = {epublish},
  revised      = {2016-11-02},
  timestamp    = {2017.02.20},
}

@Article{kajita07,
  author          = {Kajita, Emily and Okano, Justin T and Bodine, Erin N and Layne, Scott P and Blower, Sally},
  title           = {Modelling an outbreak of an emerging pathogen.},
  journal         = {Nature reviews. Microbiology},
  year            = {2007},
  volume          = {5},
  pages           = {700--709},
  month           = sep,
  abstract        = {To illustrate the usefulness of mathematical models to the microbiology and medical communities, we explain how to construct and apply a simple transmission model of an emerging pathogen. We chose to model, as a case study, a large (>8,000 reported cases) on-going outbreak of community-acquired meticillin-resistant Staphylococcus aureus (CA-MRSA) in the Los Angeles County Jail. A major risk factor for CA-MRSA infection is incarceration. Here, we show how to design a within-jail transmission model of CA-MRSA, parameterize the model and reconstruct the outbreak. The model is then used to assess the severity of the outbreak, predict the epidemiological consequences of a catastrophic outbreak and design effective interventions for outbreak control.},
  citation-subset = {IM},
  completed       = {2007-10-15},
  country         = {England},
  created         = {2007-08-17},
  doi             = {10.1038/nrmicro1660},
  issn            = {1740-1534},
  issn-linking    = {1740-1526},
  issue           = {9},
  keywords        = {Community-Acquired Infections, microbiology, transmission; Disease Outbreaks; Humans; Los Angeles, epidemiology; Methicillin Resistance; Models, Statistical; Prisons; Staphylococcal Infections, epidemiology, microbiology; Staphylococcus aureus},
  nlm-id          = {101190261},
  owner           = {NLM},
  pii             = {nrmicro1660},
  pmid            = {17703226},
  pubmodel        = {Print},
  pubstatus       = {ppublish},
  revised         = {2007-12-03},
  timestamp       = {2017.02.20},
}

@Article{kilpatrick12,
  author          = {Kilpatrick, A Marm and Randolph, Sarah E},
  title           = {Drivers, dynamics, and control of emerging vector-borne zoonotic diseases.},
  journal         = {Lancet (London, England)},
  year            = {2012},
  volume          = {380},
  pages           = {1946--1955},
  month           = dec,
  abstract        = {Emerging vector-borne diseases are an important issue in global health. Many vector-borne pathogens have appeared in new regions in the past two decades, while many endemic diseases have increased in incidence. Although introductions and emergence of endemic pathogens are often considered to be distinct processes, many endemic pathogens are actually spreading at a local scale coincident with habitat change. We draw attention to key differences between dynamics and disease burden that result from increased pathogen transmission after habitat change and after introduction into new regions. Local emergence is commonly driven by changes in human factors as much as by enhanced enzootic cycles, whereas pathogen invasion results from anthropogenic trade and travel where and when conditions (eg, hosts, vectors, and climate) are suitable for a pathogen. Once a pathogen is established, ecological factors related to vector characteristics can shape the evolutionary selective pressure and result in increased use of people as transmission hosts. We describe challenges inherent in the control of vector-borne zoonotic diseases and some emerging non-traditional strategies that could be effective in the long term.},
  citation-subset = {AIM, IM},
  completed       = {2012-12-17},
  country         = {England},
  created         = {2012-12-03},
  doi             = {10.1016/S0140-6736(12)61151-9},
  issn            = {1474-547X},
  issn-linking    = {0140-6736},
  issue           = {9857},
  keywords        = {Animals; Blood-Borne Pathogens; Climate Change; Communicable Diseases, Emerging, epidemiology, prevention & control, transmission; Disease Vectors; Global Health; Humans; Incidence; Risk Factors; Socioeconomic Factors; Tick Infestations, epidemiology; Zoonoses, epidemiology},
  mid             = {NIHMS495681},
  nlm             = {PMC3739480},
  nlm-id          = {2985213R},
  owner           = {NLM},
  pii             = {S0140-6736(12)61151-9},
  pmc             = {PMC3739480},
  pmid            = {23200503},
  pubmodel        = {Print},
  pubstatus       = {ppublish},
  revised         = {2017-02-20},
  timestamp       = {2017.02.20},
}

@Article{codeco01,
  author          = {Codeço, C T},
  title           = {Endemic and epidemic dynamics of cholera: the role of the aquatic reservoir.},
  journal         = {BMC infectious diseases},
  year            = {2001},
  volume          = {1},
  pages           = {1},
  abstract        = {In the last decades, attention to cholera epidemiology increased, as cholera epidemics became a worldwide health problem. Detailed investigation of V. cholerae interactions with its host and with other organisms in the environment suggests that cholera dynamics is much more complex than previously thought. Here, I formulate a mathematical model of cholera epidemiology that incorporates an environmental reservoir of V. cholerae. The objective is to explore the role of the aquatic reservoir on the persistence of endemic cholera as well as to define minimum conditions for the development of epidemic and endemic cholera. The reproduction rate of cholera in a community is defined by the product of social and environmental factors. The importance of the aquatic reservoir depends on the sanitary conditions of the community. Seasonal variations of contact rates force a cyclical pattern of cholera outbreaks, as observed in some cholera-endemic communities. Further development on cholera modeling requires a better understanding of V. cholerae ecology and epidemiology. We need estimates of the prevalence of V. cholerae infection in endemic populations as well as a better description of the relationship between dose and virulence.},
  citation-subset = {IM},
  completed       = {2002-10-10},
  country         = {England},
  created         = {2002-09-23},
  issn            = {1471-2334},
  issn-linking    = {1471-2334},
  keywords        = {Brazil, epidemiology; Cholera, epidemiology; Disease Outbreaks; Endemic Diseases; Humans; Models, Biological; Models, Theoretical; Prevalence; Water Pollution; Water Supply},
  nlm             = {PMC29087},
  nlm-id          = {100968551},
  owner           = {NLM},
  pmc             = {PMC29087},
  pmid            = {11208258},
  pubmodel        = {Print-Electronic},
  pubstatus       = {ppublish},
  revised         = {2014-06-13},
  timestamp       = {2017.02.20},
}

@Article{fine11,
  author          = {Fine, Paul and Eames, Ken and Heymann, David L},
  title           = {"Herd immunity": a rough guide.},
  journal         = {Clinical infectious diseases : an official publication of the Infectious Diseases Society of America},
  year            = {2011},
  volume          = {52},
  pages           = {911--916},
  month           = apr,
  abstract        = {The term "herd immunity" is widely used but carries a variety of meanings. Some authors use it to describe the proportion immune among individuals in a population. Others use it with reference to a particular threshold proportion of immune individuals that should lead to a decline in incidence of infection. Still others use it to refer to a pattern of immunity that should protect a population from invasion of a new infection. A common implication of the term is that the risk of infection among susceptible individuals in a population is reduced by the presence and proximity of immune individuals (this is sometimes referred to as "indirect protection" or a "herd effect"). We provide brief historical, epidemiologic, theoretical, and pragmatic public health perspectives on this concept.},
  citation-subset = {IM},
  completed       = {2011-07-18},
  country         = {United States},
  created         = {2011-03-23},
  doi             = {10.1093/cid/cir007},
  issn            = {1537-6591},
  issn-linking    = {1058-4838},
  issue           = {7},
  keywords        = {Communicable Disease Control; Communicable Diseases, immunology, transmission; Disease Transmission, Infectious, prevention & control; Humans; Immunity, Herd; Terminology as Topic},
  nlm-id          = {9203213},
  owner           = {NLM},
  pii             = {cir007},
  pmid            = {21427399},
  pubmodel        = {Print},
  pubstatus       = {ppublish},
  revised         = {2011-03-23},
  timestamp       = {2017.02.20},
}

@Article{heffernan05a,
  author          = {Heffernan, J M and Smith, R J and Wahl, L M},
  title           = {Perspectives on the basic reproductive ratio.},
  journal         = {Journal of the Royal Society, Interface},
  year            = {2005},
  volume          = {2},
  pages           = {281--293},
  month           = sep,
  abstract        = {The basic reproductive ratio, R0, is defined as the expected number of secondary infections arising from a single individual during his or her entire infectious period, in a population of susceptibles. This concept is fundamental to the study of epidemiology and within-host pathogen dynamics. Most importantly, R0 often serves as a threshold parameter that predicts whether an infection will spread. Related parameters which share this threshold behaviour, however, may or may not give the true value of R0. In this paper we give a brief overview of common methods of formulating R0 and surrogate threshold parameters from deterministic, non-structured models. We also review common means of estimating R0 from epidemiological data. Finally, we survey the recent use of R0 in assessing emerging diseases, such as severe acute respiratory syndrome and avian influenza, a number of recent livestock diseases, and vector-borne diseases malaria, dengue and West Nile virus.},
  citation-subset = {IM},
  completed       = {2006-08-16},
  country         = {England},
  created         = {2006-07-19},
  doi             = {10.1098/rsif.2005.0042},
  issn            = {1742-5689},
  issn-linking    = {1742-5662},
  issue           = {4},
  keywords        = {Animals; Communicable Diseases, Emerging, epidemiology; Computer Simulation; Disease Outbreaks, statistics & numerical data; Disease Susceptibility, epidemiology; Disease Transmission, Infectious, statistics & numerical data; Epidemiologic Methods; Humans; Incidence; Models, Biological; Population Dynamics; Prevalence; Risk Assessment, methods; Risk Factors},
  nlm             = {PMC1578275},
  nlm-id          = {101217269},
  owner           = {NLM},
  pii             = {KJ2U12Q2WMNF2W1E},
  pmc             = {PMC1578275},
  pmid            = {16849186},
  pubmodel        = {Print},
  pubstatus       = {ppublish},
  references      = {77},
  revised         = {2016-10-20},
  timestamp       = {2017.02.20},
}

@Article{roberts07,
  author          = {Roberts, M G},
  title           = {The pluses and minuses of R0.},
  journal         = {Journal of the Royal Society, Interface},
  year            = {2007},
  volume          = {4},
  pages           = {949--961},
  month           = oct,
  abstract        = {The concept of the basic reproduction number (R0) occupies a central place in epidemic theory. The value of R0 determines the proportion of the population that becomes infected over the course of a (modelled) epidemic. In many models, (i) an endemic infection can persist only if R0>1, (ii) the value of R0 provides a direct measure of the control effort required to eliminate the infection, and (iii) pathogens evolve to maximize their value of R0. These three statements are not universally true. In this paper, some exceptions to them are discussed, based on the extensions of the SIR model.},
  citation-subset = {IM},
  completed       = {2007-11-13},
  country         = {England},
  created         = {2007-09-13},
  doi             = {10.1098/rsif.2007.1031},
  issn            = {1742-5689},
  issn-linking    = {1742-5662},
  issue           = {16},
  keywords        = {Animal Diseases, epidemiology; Animals; Animals, Wild; Communicable Diseases, epidemiology; Disease Transmission, Infectious, statistics & numerical data; Models, Biological; Models, Statistical},
  nlm             = {PMC2075534},
  nlm-id          = {101217269},
  owner           = {NLM},
  pii             = {P368100327733485},
  pmc             = {PMC2075534},
  pmid            = {17490942},
  pubmodel        = {Print},
  pubstatus       = {ppublish},
  revised         = {2016-10-20},
  timestamp       = {2017.02.20},
}

@Article{klepac15,
  author          = {Klepac, Petra and Funk, Sebastian and Hollingsworth, T Deirdre and Metcalf, C Jessica E and Hampson, Katie},
  title           = {Six challenges in the eradication of infectious diseases.},
  journal         = {Epidemics},
  year            = {2015},
  volume          = {10},
  pages           = {97--101},
  month           = mar,
  abstract        = {Eradication and elimination are increasingly a part of the global health agenda. Once control measures have driven infection to low levels, the ecology of disease may change posing challenges for eradication efforts. These challenges vary from identifying pockets of susceptibles, improving monitoring during and after the endgame, to quantifying the economics of disease eradication versus sustained control, all of which are shaped and influenced by processes of loss of immunity, susceptible build-up, emergence of resistance, population heterogeneities and non-compliance with control measures. Here we discuss how modelling can be used to address these challenges.},
  citation-subset = {IM},
  completed       = {2016-01-15},
  country         = {Netherlands},
  created         = {2015-04-06},
  doi             = {10.1016/j.epidem.2014.12.001},
  issn            = {1878-0067},
  issn-linking    = {1878-0067},
  keywords        = {Communicable Disease Control, economics, methods; Communicable Diseases, economics, epidemiology, immunology; Cost-Benefit Analysis; Disease Eradication, economics, methods; Disease Susceptibility, epidemiology; Humans; Models, Statistical; Population Surveillance; Dynamics; Elimination; Heterogeneity; Modelling; Surveillance},
  nlm-id          = {101484711},
  owner           = {NLM},
  pii             = {S1755-4365(14)00070-X},
  pmid            = {25843393},
  pubmodel        = {Print-Electronic},
  pubstatus       = {ppublish},
  revised         = {2016-12-17},
  timestamp       = {2017.02.20},
}

@Article{klepac13,
  author          = {Klepac, Petra and Metcalf, C Jessica E and McLean, Angela R and Hampson, Katie},
  title           = {Towards the endgame and beyond: complexities and challenges for the elimination of infectious diseases.},
  journal         = {Philosophical transactions of the Royal Society of London. Series B, Biological sciences},
  year            = {2013},
  volume          = {368},
  pages           = {20120137},
  month           = aug,
  abstract        = {Successful control measures have interrupted the local transmission of human infectious diseases such as measles, malaria and polio, and saved and improved billions of lives. Similarly, control efforts have massively reduced the incidence of many infectious diseases of animals, such as rabies and rinderpest, with positive benefits for human health and livelihoods across the globe. However, disease elimination has proven an elusive goal, with only one human and one animal pathogen globally eradicated. As elimination targets expand to regional and even global levels, hurdles may emerge within the endgame when infections are circulating at very low levels, turning the last mile of these public health marathons into the longest mile. In this theme issue, we bring together recurring challenges that emerge as we move towards elimination, highlighting the unanticipated consequences of particular ecologies and pathologies of infection, and approaches to their management.},
  citation-subset = {IM},
  completed       = {2014-02-03},
  country         = {England},
  created         = {2013-06-26},
  doi             = {10.1098/rstb.2012.0137},
  issn            = {1471-2970},
  issn-linking    = {0962-8436},
  issue           = {1623},
  keywords        = {Communicable Disease Control, methods; Disease Eradication, methods; Disease Susceptibility, epidemiology, physiopathology; Humans; Immune Evasion, physiology; Mass Vaccination, statistics & numerical data; Public Health, methods, trends; immune escape; susceptible build-up; vaccination coverage; vaccine refusal},
  nlm             = {PMC3720036},
  nlm-id          = {7503623},
  owner           = {NLM},
  pii             = {rstb.2012.0137},
  pmc             = {PMC3720036},
  pmid            = {23798686},
  pubmodel        = {Electronic-Print},
  pubstatus       = {epublish},
  revised         = {2016-11-22},
  timestamp       = {2017.02.20},
}

@Article{kirsch17,
  author       = {Kirsch, Thomas D and Moseson, Heidi and Massaquoi, Moses and Nyenswah, Tolbert G and Goodermote, Rachel and Rodriguez-Barraquer, Isabel and Lessler, Justin and Cumings, Derek A T and Peters, David H},
  title        = {Impact of interventions and the incidence of ebola virus disease in Liberia-implications for future epidemics.},
  journal      = {Health policy and planning},
  year         = {2017},
  volume       = {32},
  pages        = {205--214},
  month        = mar,
  country      = {England},
  created      = {2017-02-16},
  doi          = {10.1093/heapol/czw113},
  issn         = {1460-2237},
  issn-linking = {0268-1080},
  issue        = {2},
  nlm-id       = {8610614},
  owner        = {NLM},
  pii          = {2568849},
  pmid         = {28207062},
  pubmodel     = {Print},
  pubstatus    = {ppublish},
  revised      = {2017-02-16},
  timestamp    = {2017.02.20},
}

@Article{tognotti13,
  author          = {Tognotti, Eugenia},
  title           = {Lessons from the history of quarantine, from plague to influenza A.},
  journal         = {Emerging infectious diseases},
  year            = {2013},
  volume          = {19},
  pages           = {254--259},
  month           = feb,
  abstract        = {In the new millennium, the centuries-old strategy of quarantine is becoming a powerful component of the public health response to emerging and reemerging infectious diseases. During the 2003 pandemic of severe acute respiratory syndrome, the use of quarantine, border controls, contact tracing, and surveillance proved effective in containing the global threat in just over 3 months. For centuries, these practices have been the cornerstone of organized responses to infectious disease outbreaks. However, the use of quarantine and other measures for controlling epidemic diseases has always been controversial because such strategies raise political, ethical, and socioeconomic issues and require a careful balance between public interest and individual rights. In a globalized world that is becoming ever more vulnerable to communicable diseases, a historical perspective can help clarify the use and implications of a still-valid public health strategy.},
  citation-subset = {IM},
  completed       = {2013-07-01},
  country         = {United States},
  created         = {2013-01-24},
  doi             = {10.3201/eid1902.120312},
  issn            = {1080-6059},
  issn-linking    = {1080-6040},
  issue           = {2},
  keywords        = {Disease Outbreaks, history, prevention & control; Europe, epidemiology; History, 17th Century; History, 18th Century; History, 19th Century; History, 20th Century; History, 21st Century; History, Medieval; Humans; Influenza A virus; Influenza, Human, epidemiology, history, prevention & control; Plague, epidemiology, history, prevention & control; Quarantine, history},
  nlm             = {PMC3559034},
  nlm-id          = {9508155},
  owner           = {NLM},
  pmc             = {PMC3559034},
  pmid            = {23343512},
  pubmodel        = {Print},
  pubstatus       = {ppublish},
  revised         = {2015-02-19},
  timestamp       = {2017.02.20},
}

@Article{lloyd-smith05a,
  author       = {Lloyd-Smith, James O and Cross, Paul C and Briggs, Cheryl J and Daugherty, Matt and Getz, Wayne M and Latto, John and Sanchez, Maria S and Smith, Adam B and Swei, Andrea},
  title        = {Should we expect population thresholds for wildlife disease?},
  journal      = {Trends in ecology \& evolution},
  year         = {2005},
  volume       = {20},
  pages        = {511--519},
  month        = sep,
  abstract     = {Host population thresholds for the invasion or persistence of infectious disease are core concepts of disease ecology and underlie disease control policies based on culling and vaccination. However, empirical evidence for these thresholds in wildlife populations has been sparse, although recent studies have begun to address this gap. Here, we review the theoretical bases and empirical evidence for disease thresholds in wildlife. We see that, by their nature, these thresholds are rarely abrupt and always difficult to measure, and important facets of wildlife ecology are neglected by current theories. Empirical studies seeking to identify disease thresholds in wildlife encounter recurring obstacles of small sample sizes and confounding factors. Disease control policies based solely on threshold targets are rarely warranted, but management to reduce abundance of susceptible hosts can be effective.},
  completed    = {2007-06-18},
  country      = {England},
  created      = {2006-05-16},
  doi          = {10.1016/j.tree.2005.07.004},
  issn         = {0169-5347},
  issn-linking = {0169-5347},
  issue        = {9},
  nlm-id       = {8805125},
  owner        = {NLM},
  pii          = {S0169-5347(05)00207-7},
  pmid         = {16701428},
  pubmodel     = {Print-Electronic},
  pubstatus    = {ppublish},
  revised      = {2011-05-03},
  timestamp    = {2017.02.20},
}

@Article{black66,
  author          = {Black, F L},
  title           = {Measles endemicity in insular populations: critical community size and its evolutionary implication.},
  journal         = {Journal of theoretical biology},
  year            = {1966},
  volume          = {11},
  pages           = {207--211},
  month           = jul,
  citation-subset = {IM},
  completed       = {1967-07-26},
  country         = {England},
  created         = {1967-07-26},
  issn            = {0022-5193},
  issn-linking    = {0022-5193},
  issue           = {2},
  keywords        = {Atlantic Islands; Bermuda; Biological Evolution; Hawaii; Humans; Iceland; Measles, epidemiology; Pacific Islands; Population Surveillance},
  nlm-id          = {0376342},
  owner           = {NLM},
  pii             = {0022-5193(66)90161-5},
  pmid            = {5965486},
  pubmodel        = {Print},
  pubstatus       = {ppublish},
  revised         = {2010-11-18},
  timestamp       = {2017.02.20},
}

@Article{keeling97,
  author          = {Keeling, M J},
  title           = {Modelling the persistence of measles.},
  journal         = {Trends in microbiology},
  year            = {1997},
  volume          = {5},
  pages           = {513--518},
  month           = dec,
  abstract        = {For vaccination programs to be effective, it is important to understand and predict the persistence of the disease. By considering the process at different scales (from the individual to the population level), several models allow the persistence of diseases, such as measles, to be captured.},
  citation-subset = {IM},
  completed       = {1998-02-25},
  country         = {England},
  created         = {1998-02-25},
  doi             = {10.1016/S0966-842X(97)01147-5},
  issn            = {0966-842X},
  issn-linking    = {0966-842X},
  issue           = {12},
  keywords        = {Genetic Heterogeneity; Humans; Measles, epidemiology; Models, Biological; Population; Time Factors},
  nlm-id          = {9310916},
  owner           = {NLM},
  pii             = {S0966-842X(97)01147-5},
  pmid            = {9447665},
  pubmodel        = {Print},
  pubstatus       = {ppublish},
  references      = {28},
  revised         = {2006-11-15},
  timestamp       = {2017.02.20},
}

@Article{beldomenico09,
  author          = {Beldomenico, Pablo M and Telfer, Sandra and Gebert, Stephanie and Lukomski, Lukasz and Bennett, Malcolm and Begon, Michael},
  title           = {The vicious circle and infection intensity: the case of Trypanosoma microti in field vole populations.},
  journal         = {Epidemics},
  year            = {2009},
  volume          = {1},
  pages           = {162--167},
  month           = sep,
  abstract        = {In natural populations, infection and condition may act synergistically to trigger a vicious circle: poor condition predisposes to host infections, which further reduce condition, and so on. If this vicious circle originates from a reduced resistance to infection, it will not only result in greater proneness to becoming infected of those that are in poorer condition, but it may also cause infections of higher intensity. Here, we investigate the temporal relationship between host condition and intensity of infection by a specific pathogen using as a system the dynamics of the protozoan Trypanosoma microti in field vole (Microtus agrestis) populations. With two years of longitudinal data from three monthly-sampled populations, we evaluated if individuals acquiring a high intensity of infection previously had lower haematological indicators of condition (red blood cells [RBCs] and lymphocyte counts) than those that acquired lower infection intensities. Also, the association of these indicators with past and present trypanosome blood levels was investigated. The individuals that developed high levels of parasitaemia were those that previously had low lymphocyte counts. Greater intensity of infection corresponded with lower RBCs only in low to moderate weight females, and no effect of intensity of infection on lymphocyte counts was observed. However, delayed effects of high trypanosome intensity were seen on both RBCs and lymphocytes. The vicious circle may also result in high infection intensity: individuals in poor condition are not only more likely to become infected by one pathogen; they may also be the most important source of infection for that and for other pathogens, and thus key protagonists for parasite dynamics.},
  citation-subset = {IM},
  completed       = {2011-11-04},
  country         = {Netherlands},
  created         = {2011-02-28},
  doi             = {10.1016/j.epidem.2009.05.002},
  issn            = {1878-0067},
  issn-linking    = {1878-0067},
  issue           = {3},
  keywords        = {Animals; Arvicolinae, parasitology; Disease Susceptibility; Erythrocyte Count, veterinary; Female; Health Status; Lymphocyte Count, veterinary; Male; Rodent Diseases, blood, parasitology, transmission; Severity of Illness Index; Trypanosoma, pathogenicity; Trypanosomiasis, blood, transmission, veterinary; United Kingdom},
  nlm-id          = {101484711},
  owner           = {NLM},
  pii             = {S1755-4365(09)00029-2},
  pmid            = {21352763},
  pubmodel        = {Print-Electronic},
  pubstatus       = {ppublish},
  revised         = {2016-11-25},
  timestamp       = {2017.02.20},
}

@Article{beldomenico10,
  author          = {Beldomenico, Pablo M and Begon, Michael},
  title           = {Disease spread, susceptibility and infection intensity: vicious circles?},
  journal         = {Trends in ecology \& evolution},
  year            = {2010},
  volume          = {25},
  pages           = {21--27},
  month           = jan,
  abstract        = {Epidemiological models and studies of disease ecology typically ignore the role of host condition and immunocompetence when trying to explain the distribution and dynamics of infections and their impact on host dynamics. Recent research, however, indicates that host susceptibility should be considered carefully if we are to understand the mechanism by which parasite dynamics influence host dynamics and vice versa. Studies in insects, fish, amphibians and rodents show that infection occurrence and intensity are more probable and more severe in individuals with an underlying poor condition. Moreover, infection itself results in further deterioration of the host and a 'vicious circle' is created. We argue that this potential synergy between host susceptibility and infection should be more widely acknowledged in disease ecology research.},
  citation-subset = {IM},
  completed       = {2010-03-18},
  country         = {England},
  created         = {2010-02-01},
  doi             = {10.1016/j.tree.2009.06.015},
  issn            = {0169-5347},
  issn-linking    = {0169-5347},
  issue           = {1},
  keywords        = {Animals; Disease Susceptibility; Disease Transmission, Infectious; Host-Pathogen Interactions; Immunocompetence},
  nlm-id          = {8805125},
  owner           = {NLM},
  pii             = {S0169-5347(09)00228-6},
  pmid            = {19782425},
  pubmodel        = {Print-Electronic},
  pubstatus       = {ppublish},
  revised         = {2011-05-03},
  timestamp       = {2017.02.20},
}

@Article{altizer06,
  author          = {Altizer, Sonia and Dobson, Andrew and Hosseini, Parviez and Hudson, Peter and Pascual, Mercedes and Rohani, Pejman},
  title           = {Seasonality and the dynamics of infectious diseases.},
  journal         = {Ecology letters},
  year            = {2006},
  volume          = {9},
  pages           = {467--484},
  month           = apr,
  issn            = {1461-0248},
  abstract        = {Seasonal variations in temperature, rainfall and resource availability are ubiquitous and can exert strong pressures on population dynamics. Infectious diseases provide some of the best-studied examples of the role of seasonality in shaping population fluctuations. In this paper, we review examples from human and wildlife disease systems to illustrate the challenges inherent in understanding the mechanisms and impacts of seasonal environmental drivers. Empirical evidence points to several biologically distinct mechanisms by which seasonality can impact host-pathogen interactions, including seasonal changes in host social behaviour and contact rates, variation in encounters with infective stages in the environment, annual pulses of host births and deaths and changes in host immune defences. Mathematical models and field observations show that the strength and mechanisms of seasonality can alter the spread and persistence of infectious diseases, and that population-level responses can range from simple annual cycles to more complex multiyear fluctuations. From an applied perspective, understanding the timing and causes of seasonality offers important insights into how parasite-host systems operate, how and when parasite control measures should be applied, and how disease risks will respond to anthropogenic climate change and altered patterns of seasonality. Finally, by focusing on well-studied examples of infectious diseases, we hope to highlight general insights that are relevant to other ecological interactions.},
  citation-subset = {IM},
  completed       = {2006-05-23},
  country         = {England},
  created         = {2006-04-20},
  doi             = {10.1111/j.1461-0248.2005.00879.x},
  issn-linking    = {1461-023X},
  issue           = {4},
  keywords        = {Animal Diseases, epidemiology, transmission; Animals; Animals, Wild; Communicable Diseases, epidemiology, transmission; Disease Susceptibility; Host-Parasite Interactions; Models, Theoretical; Population Dynamics; Reproduction; Risk Factors; Seasons; Social Behavior},
  nlm-id          = {101121949},
  owner           = {NLM},
  pii             = {ELE879},
  pmid            = {16623732},
  pubmodel        = {Print},
  pubstatus       = {ppublish},
  references      = {126},
  revised         = {2007-11-15},
  timestamp       = {2017.02.25},
}

@Article{dowell01,
  author          = {Dowell, S F},
  title           = {Seasonal variation in host susceptibility and cycles of certain infectious diseases.},
  journal         = {Emerging infectious diseases},
  year            = {2001},
  volume          = {7},
  pages           = {369--374},
  issn            = {1080-6040},
  abstract        = {Seasonal cycles of infectious diseases have been variously attributed to changes in atmospheric conditions, the prevalence or virulence of the pathogen, or the behavior of the host. Some observations about seasonality are difficult to reconcile with these explanations. These include the simultaneous appearance of outbreaks across widespread geographic regions of the same latitude; the detection of pathogens in the off-season without epidemic spread; and the consistency of seasonal changes, despite wide variations in weather and human behavior. In contrast, an increase in susceptibility of the host population, perhaps linked to the annual light/dark cycle and mediated by the pattern of melatonin secretion, might account for many heretofore unexplained features of infectious disease seasonality. Ample evidence indicates that photoperiod-driven physiologic changes are typical in mammalian species, including some in humans. If such physiologic changes underlie human resistance to infectious diseases for large portions of the year and the changes can be identified and modified, the therapeutic and preventive implications may be considerable.},
  citation-subset = {IM},
  completed       = {2001-07-05},
  country         = {United States},
  created         = {2001-05-31},
  doi             = {10.3201/eid0703.010301},
  issn-linking    = {1080-6040},
  issue           = {3},
  keywords        = {Communicable Diseases, epidemiology; Disease Outbreaks; Disease Susceptibility; Humans; Seasons; Weather},
  nlm             = {PMC2631809},
  nlm-id          = {9508155},
  owner           = {NLM},
  pmc             = {PMC2631809},
  pmid            = {11384511},
  pubmodel        = {Print},
  pubstatus       = {ppublish},
  revised         = {2014-06-13},
  timestamp       = {2017.02.25},
}

@Article{handel17,
  author    = {Handel, Andreas},
  title     = {Learning infectious disease epidemiology in a modern framework},
  journal   = {PLoS computational biology},
  year      = {2017},
  volume    = {13},
  number    = {10},
  pages     = {e1005642},
  owner     = {andreas},
  publisher = {Public Library of Science},
  timestamp = {2018.01.02},
}

@Article{luz10,
  author       = {Luz, Paula and Struchiner, Claudio and Galvani, Alison},
  title        = {Modeling transmission dynamics and control of vector-borne neglected tropical diseases.},
  journal      = {PLoS neglected tropical diseases},
  year         = {2010},
  volume       = {4},
  issn         = {1935-2735},
  country      = {United States},
  doi          = {10.1371/journal.pntd.0000761},
  issn-linking = {1935-2727},
  issue        = {10},
  nlm-id       = {101291488},
  owner        = {NLM},
  pmc          = {PMC2964290},
  pmid         = {21049062},
}

@Article{hoare08,
  author      = {Alexander Hoare and David G Regan and David P Wilson},
  title       = {Sampling and sensitivity analyses tools (SaSAT) for computational modelling.},
  journal     = {Theor Biol Med Model},
  year        = {2008},
  volume      = {5},
  pages       = {4},
  doi         = {10.1186/1742-4682-5-4},
  institution = {National Centre in HIV Epidemiology and Clinical Research, The University of New South Wales, Sydney, New South Wales, 2010, Australia. ahoare@nchecr.unsw.edu.au},
  pii         = {1742-4682-5-4},
  pmid        = {18304361},
  timestamp   = {2009.04.10},
}

@Article{marino08,
  author    = {Simeone Marino and Ian B. Hogue and Christian J. Ray and Denise E. Kirschner},
  title     = {A methodology for performing global uncertainty and sensitivity analysis in systems biology},
  journal   = {J. Theor. Biol},
  year      = {2008},
  volume    = {254},
  number    = {1},
  pages     = {178--196},
  month     = {7 September},
  doi       = {10.1016/j.jtbi.2008.04.011},
  ljournal  = {Journal of Theoretical Biology},
  timestamp = {2010.10.25},
}

@Book{saltelli04,
  title      = {Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models},
  publisher  = {Wiley},
  year       = {2004},
  author     = {Saltelli, A. and Tarantola, Stefano and Campolongo, Francesca and Ratto, Marco},
  edition    = {1},
  month      = apr,
  isbn       = {0470870931},
  shorttitle = {Sensitivity Analysis in Practice},
  timestamp  = {2012.10.03},
}

@Book{bjoernstad18,
  title      = {Epidemics: {Models} and {Data} using {R}},
  publisher  = {Springer International Publishing},
  year       = {2018},
  author     = {Bjørnstad, Ottar N.},
  series     = {Use {R}!},
  isbn       = {978-3-319-97486-6},
  abstract   = {This book is designed to be a practical study in infectious disease dynamics. The book offers an easy to follow implementation and analysis of mathematical epidemiology. The book focuses on recent case studies in order to explore various conceptual, mathematical, and statistical issues. The dynamics of infectious diseases shows a wide diversity of pattern. Some have locally persistent chains-of-transmission, others persist spatially in ‘consumer-resource metapopulations’. Some infections are prevalent among the young, some among the old and some are age-invariant. Temporally, some diseases have little variation in prevalence, some have predictable seasonal shifts and others exhibit violent epidemics that may be regular or irregular in their timing. Models and ‘models-with-data’ have proved invaluable for understanding and predicting this diversity, and thence help improve intervention and control. Using mathematical models to understand infectious disease dynamics has a very rich history in epidemiology. The field has seen broad expansions of theories as well as a surge in real-life application of mathematics to dynamics and control of infectious disease. The chapters of Epidemics: Models and Data using R have been organized in a reasonably logical way: Chapters 1-10 is a mix and match of models, data and statistics pertaining to local disease dynamics; Chapters 11-13 pertains to spatial and spatiotemporal dynamics; Chapter 14 highlights similarities between the dynamics of infectious disease and parasitoid-host dynamics; Finally, Chapters 15 and 16 overview additional statistical methodology useful in studies of infectious disease dynamics. This book can be used as a guide for working with data, models and ‘models-and-data’ to understand epidemics and infectious disease dynamics in space and time.},
  file       = {Snapshot:C\:\\Users\\andreas\\Zotero\\storage\\CGG6X56Z\\9783319974866.html:text/html},
  language   = {en},
  shorttitle = {Epidemics},
  url        = {https://www.springer.com/la/book/9783319974866},
  urldate    = {2019-03-13},
}

@Comment{jabref-meta: databaseType:bibtex;}
