Abstract
funding section is incomplete. complete funding information is: The authors acknowledge financial support from NIH U01 {type:entrez-nucleotide,attrs:{text:GM087719,term_id:221567418,term_text:GM087719}}GM087719. JCM acknowledges support from the RAPIDD program of Department of Homeland Security and the NIH Fogarty International Center; and the United States National Institutes of Health Models of Infectious Disease Agent Study program through cooperative agreement 1U54GM088558. funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Highlights
Contacts sufficient for transmission of infectious disease occur repeatedly within stable relationships such as between sex partners or within households and workplaces
The transmission dynamics of infectious diseases are sensitive to the patterns of interactions among susceptible and infectious individuals
This paper introduces a versatile mathematical model that takes both heterogeneous connectivity and clustering into account and uses it to quantify the relative impact of clustered contacts on epidemics and the prediction biases that can arise when clustering and variability in infectious periods are ignored
Summary
Contacts sufficient for transmission of infectious disease occur repeatedly within stable relationships such as between sex partners or within households and workplaces. Epidemiologists increasingly use random network models that explicitly capture such interactions to study disease dynamics [1]. This work has shown that infectious disease dynamics can be profoundly influenced by two key network properties– the distribution in the number of contacts per individual (the degree distribution) [2] and the transitivity or clustering of contacts, such as within households [3,4]. We lack a general framework for studying the combined epidemiological impacts of clustering and degree distribution. For public health, such understanding may be critical to predicting epidemiological events across diverse populations and tailoring control strategies appropriately. There remains a clear need for general, systematic model selection rules
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