A climate-driven mechanistic population model of Aedes albopictus with diapause. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Jia, Pengfei; Lu, Liang; Chen, Xiang; Chen, Jin; Guo, Li; Yu, Xiao; Liu, Qiyong
• Is Part Of: Parasites & vectors. Volume 9:Issue 1(2016)
• Page Start: 1
• Page End: 15
• Publication Date: 2016-12
• Subjects: Aedes albopictus -- Population dynamics -- Mechanistic model -- Climate-driven -- Diapause; Parasitism -- Periodicals; Parasites -- Periodicals; Vector-pathogen relationships -- Periodicals; Animals as carriers of disease -- Periodicals; Insects as carriers of disease -- Periodicals; 616.96
• DOI: 10.1186/s13071-016-1448-y
• Languages: English
• ISSNs: 1756-3305

Abstract

Abstract Background The mosquitoAedes albopitus is a competent vector for the transmission of many blood-borne pathogens. An important factor that affects the mosquitoes' development and spreading is climate, such as temperature, precipitation and photoperiod. Existing climate-driven mechanistic models overlook the seasonal pattern of diapause, referred to as the survival strategy of mosquito eggs being dormant and unable to hatch under extreme weather. With respect to diapause, several issues remain unaddressed, including identifying the time when diapause eggs are laid and hatched under different climatic conditions, demarcating the thresholds of diapause and non-diapause periods, and considering the mortality rate of diapause eggs. Methods Here we propose a generic climate-driven mechanistic population model ofAe. albopitus applicable to mostAe. albopictus -colonized areas. The new model is an improvement over the previous work by incorporating the diapause behaviors with many modifications to the stage-specific mechanism of the mosquitoes' life-cycle. monthly Container Index (CI) ofAe. albopitus collected in two Chinese cities, Guangzhou and Shanghai is used for model validation. Results The simulation results by the proposed model is validated with entomological field data by the Pearson correlation coefficientr 2 in Guangzhou (r 2 = 0.84) and in Shanghai (r 2 = 0.90). In addition, by consolidating the effect of diapause-related adjustments and temperature-related parameters in the model, the improvement is significant over the basic model. Conclusions The model highlights the importance of considering diapause in simulatingAe. albopitus population. It also corroborates that temperature and photoperiod are significant in affecting the population dynamics of the mosquito. By refining the relationship betweenAe. albopitus population and climatic factors, the model serves to establish a mechanistic relation to the growth and decline of the species. Understanding this relationship in a better way will benefit studying the transmission and the spatiotemporal distribution of mosquito-borne epidemics and eventually facilitating the early warning and control of the diseases.