RT Journal Article
SR Electronic
T1 Inter-annual variation in seasonal dengue epidemics driven by multiple interacting factors in Guangzhou, China
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 310896
DO 10.1101/310896
A1 Rachel J. Oidtman
A1 Shengjie Lai
A1 Zhoujie Huang
A1 Juan Yang
A1 Amir S. Siraj
A1 Robert C. Reiner
A1 Andrew J. Tatem
A1 T. Alex Perkins
A1 Hongjie Yu
YR 2019
UL http://biorxiv.org/content/early/2019/01/14/310896.abstract
AB 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.