Abstract
Global spatial clustering is the tendency of points, here cases of infectious disease, to occur closer together than expected by chance. The extent of global clustering can provide a window into the spatial scale of disease transmission, thereby providing insights into the mechanism of spread, and informing optimal surveillance and control. Here the authors present an interpretable measure of spatial clustering, τ, which can be understood as a measure of relative risk. When biological or temporal information can be used to identify sets of potentially linked and likely unlinked cases, this measure can be estimated without knowledge of the underlying population distribution. The greater our ability to distinguish closely related (i.e., separated by few generations of transmission) from more distantly related cases, the more closely τ will track the true scale of transmission. The authors illustrate this approach using examples from the analyses of HIV, dengue and measles, and provide an R package implementing the methods described. The statistic presented, and measures of global clustering in general, can be powerful tools for analysis of spatially resolved data on infectious diseases.
Highlights
The spatial and temporal scales over which cases are likely to be found is one of the most fundamental determinants of the population dynamics of infectious disease, but is one of the hardest to measure
We discuss the potential uses of global clustering statistics in infectious disease epidemiology, and introduce an approach that can be used to measure clustering resulting from the disease process even when information on the underlying spatial distribution of the population is unknown
While local clustering statistics have many uses in epidemiology, here we focus on the overall tendency of infectious diseases to cluster in space
Summary
The spatial and temporal scales over which cases are likely to be found is one of the most fundamental determinants of the population dynamics of infectious disease, but is one of the hardest to measure. We discuss the potential uses of global clustering statistics in infectious disease epidemiology, and introduce an approach that can be used to measure clustering resulting from the disease process even when information on the underlying spatial distribution of the population is unknown. If a global clustering statistic is to be useful in the study of infectious disease it must be interpretable in terms of disease risk, comparable across settings and distinguish spatial variation due to the transmission process from variation due to clustering in the underlying population. In an attempt to meet this challenge, we have sought to develop a measure of spatial dependence that is interpretable in terms of disease risk, and extend this measure to make efficient use of pathogen strain (as measured by serotype, genotype, or other measure) to make valid inferences in cases when the spatial distribution of the underlying population is unknown. This approach should be useful even when the catchment area for cases or the underlying distribution of the population is unknown
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