To demonstrate the scan statistics in this package, we will use a dataset of the annual number of brain cancer cases in the counties of New Mexico, for the years 1973-1991. This data was studied by Kulldorff et al. (1998), who detected a cluster of cancer cases in the counties Los Alamos and Santa Fe during the years 1986-1989, though the excess of brain cancer in this cluster was not deemed statistically significant. The data originally comes from the package rsatscan (Kleinman 2015), which provides an interface to the program SaTScan, but it has been aggregated and extended for the scanstatistics package.
To get familiar with the counties of New Mexico, we begin by plotting them on a map using the data frames NM_map
and NM_geo
supplied by the scanstatistics package:
library(scanstatistics)
library(ggplot2)
# Load map data
data(NM_map)
data(NM_geo)
# Plot map with labels at centroids
ggplot() +
geom_polygon(data = NM_map,
mapping = aes(x = long, y = lat, group = group),
color = "grey", fill = "white") +
geom_text(data = NM_geo,
mapping = aes(x = center_long, y = center_lat, label = county)) +
ggtitle("Counties of New Mexico")
We can further obtain the yearly number of cases and the population for each country for the years 1973-1991 from the data table NM_popcas
provided by the package:
It should be noted that Cibola county was split from Valencia county in 1981, and cases in Cibola have been counted to Valencia in the data.
A scan statistic for Poisson data
The Poisson distribution is a natural first option when dealing with count data. The scanstatistics package provides the two functions scan_eb_poisson
and scan_pb_poisson
with this distributional assumption. The first is an expectation-based scan statistic for univariate Poisson-distributed data proposed by Neill et al. (2005), and we focus on this one in the example below. The second scan statistic is the population-based scan statistic proposed by Kulldorff (2001).
Theoretical motivation
For the expectation-based Poisson scan statistic, the null hypothesis of no anomaly states that at each location \(i\) and duration \(t\), the observed count is Poisson-distributed with expected value \(\mu_{it}\): \[
H_0 \! : Y_{it} \sim \textrm{Poisson}(\mu_{it}),
\] for locations \(i=1,\ldots,m\) and durations \(t=1,\ldots,T\), with \(T\) being the maximum duration considered. Under the alternative hypothesis, there is a space-time cluster \(W\) consisting of a spatial zone \(Z \subset \{1,\ldots,m\}\) and a time window \(D = \{1, 2, \ldots, d\} \subseteq \{1,2,\ldots,T\}\) such that the counts in \(W\) have their expected values inflated by a factor \(q_W > 1\) compared to the null hypothesis: \[
H_1 \! : Y_{it} \sim \textrm{Poisson}(q_W \mu_{it}), ~~(i,t) \in W.
\] For locations and durations outside of this window, counts are assumed to be distributed as under the null hypothesis. Calculating the scan statistic then involves three steps:
- For each space-time window \(W\), find the maximum likelihood estimate of \(q_W\), treating all \(\mu_{it}\)’s as constants.
- Plug the estimated \(q_W\) into (the logarithm of) a likelihood ratio with the likelihood of the alternative hypothesis in the numerator and the likelihood under the null hypothesis (in which \(q_W=1\)) in the denominator, again for each \(W\).
- Take the scan statistic as the maximum of these likelihood ratios, and the corresponding window \(W^*\) as the most likely cluster (MLC).
Using the Poisson scan statistic
The first argument to any of the scan statistics in this package should be a matrix (or array) of observed counts, whether they be integer counts or real-valued “counts”. In such a matrix, the columns should represent locations and the rows the time intervals, ordered chronologically from the earliest interval in the first row to the most recent in the last. In this example we would like to detect a potential cluster of brain cancer in the counties of New Mexico during the years 1986-1989, so we begin by retrieving the count and population data from that period and reshaping them to a matrix using the helper function df_to_matrix
:
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
counts <- NM_popcas %>%
filter(year >= 1986 & year < 1990) %>%
df_to_matrix(time_col = "year", location_col = "county", value_col = "count")
Spatial zones
The second argument to scan_eb_poisson
should be a list of integer vectors, each such vector being a zone, which is the name for the spatial component of a potential outbreak cluster. Such a zone consists of one or more locations grouped together according to their similarity across features, and each location is numbered as the corresponding column index of the counts
matrix above (indexing starts at 1).
In this example, the locations are the counties of New Mexico and the features are the coordinates of the county seats. These are made available in the data table NM_geo
. Similarity will be measured using the geographical distance between the seats of the counties, taking into account the curvature of the earth. A distance matrix is calculated using the spDists
function from the sp package, which is then passed to dist_to_knn
(with \(k=15\) neighbors) and on to knn_zones
:
library(sp)
library(magrittr)
# Remove Cibola since cases have been counted towards Valencia. Ideally, this
# should be accounted for when creating the zones.
zones <- NM_geo %>%
filter(county != "cibola") %>%
select(seat_long, seat_lat) %>%
as.matrix %>%
spDists(x = ., y = ., longlat = TRUE) %>%
dist_to_knn(k = 15) %>%
knn_zones
Baselines
The advantage of expectation-based scan statistics is that parameters such as the expected value can be modelled and estimated from past data e.g. by some form of regression. For the expectation-based Poisson scan statistic, we can use a (very simple) Poisson GLM to estimate the expected value of the count in each county and year, accounting for the different populations in each region. Similar to the counts
argument, the expected values should be passed as a matrix to the scan_eb_poisson
function:
mod <- glm(count ~ offset(log(population)) + 1 + I(year - 1985),
family = poisson(link = "log"),
data = NM_popcas %>% filter(year < 1986))
ebp_baselines <- NM_popcas %>%
filter(year >= 1986 & year < 1990) %>%
mutate(mu = predict(mod, newdata = ., type = "response")) %>%
df_to_matrix(value_col = "mu")
Note that the population numbers are (perhaps poorly) interpolated from the censuses conducted in 1973, 1982, and 1991.
Calculation
We can now calculate the Poisson scan statistic. To give us more confidence in our detection results, we will perform 999 Monte Carlo replications, by which data is generated using the parameters from the null hypothesis and a new scan statistic calculated. This is then summarized in a \(P\)-value, calculated as the proportion of times the replicated scan statistics exceeded the observed one. The output of scan_poisson
is an object of class “scanstatistic”, which comes with the print method seen below.
set.seed(1)
poisson_result <- scan_eb_poisson(counts = counts,
zones = zones,
baselines = ebp_baselines,
n_mcsim = 999)
print(poisson_result)
## Data distribution: Poisson
## Type of scan statistic: expectation-based
## Setting: univariate
## Number of locations considered: 32
## Maximum duration considered: 4
## Number of spatial zones: 415
## Number of Monte Carlo replicates: 999
## Monte Carlo P-value: 0.005
## Gumbel P-value: 0.004
## Most likely event duration: 4
## ID of locations in MLC: 15, 26
As we can see, the most likely cluster for an anomaly stretches from 1986-1989 and involves the locations numbered 15 and 26, which correspond to the counties
counties <- as.character(NM_geo$county)
counties[c(15, 26)]
[1] "losalamos" "santafe"
These are the same counties detected by Kulldorff et al. (1998), though their analysis was retrospective rather than prospective as ours was. Ours was also data dredging as we used the same study period with hopes of detecting the same cluster.
A heuristic score for locations
We can score each county according to how likely it is to be part of a cluster in a heuristic fashion using the function score_locations
, and visualize the results on a heatmap as follows:
# Calculate scores and add column with county names
county_scores <- score_locations(poisson_result, zones)
county_scores %<>% mutate(county = factor(counties[-length(counties)],
levels = levels(NM_geo$county)))
# Create a table for plotting
score_map_df <- merge(NM_map, county_scores, by = "county", all.x = TRUE) %>%
arrange(group, order)
# As noted before, Cibola county counts have been attributed to Valencia county
score_map_df[score_map_df$subregion == "cibola", ] %<>%
mutate(relative_score = score_map_df %>%
filter(subregion == "valencia") %>%
select(relative_score) %>%
.[[1]] %>% .[1])
ggplot() +
geom_polygon(data = score_map_df,
mapping = aes(x = long, y = lat, group = group,
fill = relative_score),
color = "grey") +
scale_fill_gradient(low = "#e5f5f9", high = "darkgreen",
guide = guide_colorbar(title = "Relative\nScore")) +
geom_text(data = NM_geo,
mapping = aes(x = center_long, y = center_lat, label = county),
alpha = 0.5) +
ggtitle("County scores")
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)
A warning though: the score_locations
function can be quite slow for large data sets. This might change in future versions of the package.
Finding the top-scoring clusters
Finally, if we want to know not just the most likely cluster, but say the five top-scoring space-time clusters, we can use the function top_clusters
. The clusters returned can either be overlapping or non-overlapping in the spatial dimension, according to our liking.
top5 <- top_clusters(poisson_result, zones, k = 5, overlapping = FALSE)
# Find the counties corresponding to the spatial zones of the 5 clusters.
top5_counties <- top5$zone %>%
purrr::map(get_zone, zones = zones) %>%
purrr::map(function(x) counties[x])
# Add the counties corresponding to the zones as a column
top5 %<>% mutate(counties = top5_counties)
The top_clusters
function includes Monte Carlo and Gumbel \(P\)-values for each cluster. These \(P\)-values are conservative, since secondary clusters from the original data are compared to the most likely clusters from the replicate data sets.