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

Autor:	Oidtman RJ; Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, 46556, IN, USA., Lai S; School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, 200032, China.; WorldPop, Department of Geography and Environment, University of Southampton, Southampton, SO17 1BJ, UK.; Flowminder Foundation, Stockholm, SE-11355, Sweden., Huang Z; School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, 200032, China., Yang J; School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, 200032, China., Siraj AS; Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, 46556, IN, USA., Reiner RC Jr; Institute for Health and Metrics and Evaluation, University of Washington, Seattle, 98195, WA, USA., Tatem AJ; WorldPop, Department of Geography and Environment, University of Southampton, Southampton, SO17 1BJ, UK.; Flowminder Foundation, Stockholm, SE-11355, Sweden., Perkins TA; Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, 46556, IN, USA. taperkins@nd.edu., Yu H; School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, 200032, China. yhj@fudan.edu.cn.
Jazyk:	angličtina
Zdroj:	Nature communications [Nat Commun] 2019 Mar 08; Vol. 10 (1), pp. 1148. Date of Electronic Publication: 2019 Mar 08.
DOI:	10.1038/s41467-019-09035-x
Abstrakt:	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.
Databáze:	MEDLINE
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