Climate-driven variation in mosquito density predicts the spatiotemporal dynamics of dengue

Abstract

Dengue is a climate-sensitive mosquito-borne disease with increasing geographic extent and human incidence. Although the climate-epidemic association and outbreak risks have been assessed using both statistical and mathematical models, local mosquito population dynamics have not been incorporated in a unified predictive framework. Here, we use mosquito surveillance data from 2005 to 2015 in China to integrate a generalized additive model of mosquito dynamics with a susceptible-infected-recovered (SIR) compartmental model of viral transmission to establish a predictive model linking climate and seasonal dengue risk. The findings illustrate that spatiotemporal dynamics of dengue are predictable from the local vector dynamics, which in turn, can be predicted by climate conditions. On the basis of the similar epidemiology and transmission cycles, we believe that this integrated approach and the finer mosquito surveillance data provide a framework that can be extended to predict outbreak risk of other mosquito-borne diseases as well as project dengue risk maps for future climate scenarios.

Keywords: climate variation; dengue fever; integrated modeling approach; mosquito density.

Fig. 1.

Spatial and temporal distribution of dengue human incidence in 2005–2015. (A) Times series of the dengue human incidence in China (on the logarithmic scale) is projected to (B) case numbers and distinguished by color according to the magnitude in each city.

Fig. 2.

Partial effect from temperature and precipitation on mosquito density. The potential nonlinear effects of the number of precipitating days in (A) north, (B) middle, and (C) south China and (D) mean temperature in the previous month on mosquito density are quantified using GAM. Results of the significance test are also shown for each partial effect of climate predictor on mosquito density.

Fig. 3.

Observed and predicted dengue human cases across various cities between 2005–2015. The observed number of human cases in outbreak years (the gray shaded area) during 2005–2014 is used for model simulation and parameter estimation. The model was reinitialized using a plausible range of infectious periods at the beginning of each outbreak year. The median estimates of human cases (red lines) and corresponding confidence intervals (red shaded area) for both simulation and forecasts were compared with observed data (black lines) on the logarithmic scale.

Fig. 4.

The dynamics of R₀ during 2005–2014. The median estimates and the corresponding 5th and 95th quantile intervals represent the seasonality of the human-to-human basic reproductive rate in each city in 2005–2014.