title: Measuring Spatial Dependence for Infectious Disease Epidemiology.
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.
authors.affiliation: Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
authors.forename: Justin
authors.initials: J
authors.lastname: Lessler
authors.affiliation: Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
authors.forename: Henrik
authors.identifier: 0000-0003-3626-4254
authors.initials: H
authors.lastname: Salje
authors.affiliation: Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
authors.forename: M Kate
authors.initials: MK
authors.lastname: Grabowski
authors.affiliation: Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
authors.forename: Derek A T
authors.initials: DA
authors.lastname: Cummings
mesh_terms: D003141:Communicable Diseases / Q000453:epidemiology*; D003198:Computer Simulation; D003715:Dengue / Q000453:epidemiology / Q000635:transmission; D005843:Geography; D005858:Germany; D015658:HIV Infections / Q000453:epidemiology / Q000635:transmission; D049672:History, 19th Century; D049673:History, 20th Century; D006801:Humans; D055552:Infectious Disease Medicine / Q000266:history / Q000379:methods*; D008457:Measles / Q000453:epidemiology / Q000635:transmission; D015233:Models, Statistical; D011381:Programming Languages; D012306:Risk; D062206:Spatial Analysis; D013997:Time Factors