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Which mechanisms drive seasonal rabies outbreaks in raccoons? A test using dynamic social network models
Author(s) -
Hirsch Ben T.,
Reynolds Jennifer J.H.,
Gehrt Stanley D.,
Craft Meggan E.
Publication year - 2016
Publication title -
journal of applied ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.503
H-Index - 181
eISSN - 1365-2664
pISSN - 0021-8901
DOI - 10.1111/1365-2664.12628
Subject(s) - wildlife , rabies , outbreak , seasonality , wildlife disease , geography , population , transmission (telecommunications) , biology , ecology , environmental health , medicine , virology , electrical engineering , engineering
Summary The timing of raccoon rabies outbreaks in the eastern USA is non‐random and often exhibits a seasonal peak. While fluctuations in disease transmission can be driven by seasonal changes in animal population dynamics, behaviour and physiology, it is still unclear which causal factors lead to seasonal outbreaks of raccoon rabies. We used dynamic network modelling to test which of three seasonally changing factors are most likely responsible for raccoon rabies outbreaks: (i) birth pulses, (ii) changes in social network structure and (iii) changes in social contact duration. In contrast to previous predictions, we found that a change in social contact duration was the single most important driver of rabies seasonality. More specifically, co‐denning for thermoregulation during the winter increases the amount of time individuals spend in close contact, which in turn should lead to peaks in rabies transmission during the winter. Increased time spent in close proximity during cold winter months has implications for seasonal disease patterns in raccoon populations across a latitudinal gradient, as well as potentially being important for pathogens transmitted by close contact in other wildlife hosts. Synthesis and applications . By incorporating detailed empirical data describing variation in raccoon contacts into a network modelling framework, it is possible to determine the likely causal mechanisms driving seasonal disease patterns. This can be crucial information for wildlife and public health officials implementing wildlife disease control programmes.

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