Temperature, precipitation, and other weather conditions are well-accepted as drivers of temporal variation in vector-borne disease transmission. In settings where dengue—a major mosquito-borne disease of humans—is endemic, weather conditions are known to interact with demography and population immunity to jointly drive inter-annual variation in incidence. In settings where dengue is not endemic but poses a recurring seasonal risk, drivers of inter-annual variation in incidence are not well understood. In 2014, Guangzhou, China experienced its worst dengue epidemic on record, with incidence exceeding the historical average by two orders of magnitude. Understanding what drove this unprecedented event is critical for informing future mitigation policy in Guangzhou and similar epidemiological settings. To disentangle contributions from multiple factors to inter-annual variation in incidence, we fitted a semi-mechanistic model to time series data on daily incidence of locally acquired and imported cases, daily mean temperature, and monthly Aedes albopictus mosquito density from 2005-2015. After fitting the model, we performed a series of factorial simulation experiments in which seasonal epidemics were simulated under all combinations of year-specific patterns of four time-varying drivers of transmission: imported cases, mosquito density, temperature, and residual variation in local conditions not explicitly represented in the model.
Results/Conclusions
Analysis of these simulation experiments showed that local factors (i.e., mosquito density, temperature, and residual variation in local conditions) explained 88.9% of inter-annual variation in incidence, with residual variation in local conditions being the foremost driver. Factors that could have contributed to the residual variation in local conditions include the possibility that transmission was not actually higher in 2014 but instead a larger proportion of DENV infections resulted in symptomatic disease than in other years. A second, related possibility is that there were serotype differences in the severity of symptoms in 2014. When 2014 was excluded from the analysis of simulation experiments, local conditions and importation each explained equal portions of inter-annual variation in incidence. Our results indicate that while epidemics in most years were limited by unfavorable conditions with respect to one or more factors, the large epidemic in 2014 was made possible by a combination of favorable conditions for all factors considered in our analysis.