Inter-annual variation in seasonal dengue epidemics driven by multiple interacting factors in Guangzhou, China

Rachel J. Oidtman, Shengjie Lai, Zhoujie Huang, Juan Yang, Amir S. Siraj, Robert C. Reiner, Andrew J. Tatem, T. Alex Perkins () and Hongjie Yu ()
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Rachel J. Oidtman: University of Notre Dame
Shengjie Lai: Key Laboratory of Public Health Safety, Ministry of Education
Zhoujie Huang: Key Laboratory of Public Health Safety, Ministry of Education
Juan Yang: Key Laboratory of Public Health Safety, Ministry of Education
Amir S. Siraj: University of Notre Dame
Robert C. Reiner: University of Washington
Andrew J. Tatem: University of Southampton
T. Alex Perkins: University of Notre Dame
Hongjie Yu: Key Laboratory of Public Health Safety, Ministry of Education

Nature Communications, 2019, vol. 10, issue 1, 1-12

Abstract: Abstract Vector-borne diseases display wide inter-annual variation in seasonal epidemic size due to their complex dependence on temporally variable environmental conditions and other factors. In 2014, Guangzhou, China experienced its worst dengue epidemic on record, with incidence exceeding the historical average by two orders of magnitude. To disentangle contributions from multiple factors to inter-annual variation in epidemic size, we fitted a semi-mechanistic model to time series data from 2005–2015 and performed a series of factorial simulation experiments in which seasonal epidemics were simulated under all combinations of year-specific patterns of four time-varying factors: imported cases, mosquito density, temperature, and residual variation in local conditions not explicitly represented in the model. Our results indicate that while epidemics in most years were limited by unfavorable conditions with respect to one or more factors, the epidemic in 2014 was made possible by the combination of favorable conditions for all factors considered in our analysis.

Date: 2019
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DOI: 10.1038/s41467-019-09035-x

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