Louisiana State University
This project will assess how the transmission success of arboviruses is mediated by temperature variation. lt aims to experimentally investigate the effects of different temperatures on various mosquito life history traits and on the dynamic process of vector competence of two strains of chikungunya virus (CHIKV) and the newly emergent Zika virus (ZIKV) in their primary vectors (Aedes aegypti and Ae. albopictus), The results of these experiments will be used to develop and parameterize mathematical models to quantify the subsequent effects of temperature variation on mosquito population structure and ultimately arbovirus transmission. Current mathematical models of the transmission dynamics of mosquito-borne diseases are limited by certain assumptions that could systematically bias or compound predictions. The key assumptions our experimental work will address are: 1) mosquito development is uniform across a temperature spectrum in sub-/tropical regions; 2) development is not affected by infection status of the mosquito; 3) that mortality is uniform across this temperature spectrum; 4) mortality is not affected by infection status; and 5) temperature does not simultaneously affect vector competence outcomes (% infectious) and the extrinsic incubation period. Individually these assumptions could have important impacts on model predictions and together it is unclear how these impacts might interact. Experimentally, the effects of temperature have not been comprehensively investigated for CHIKV virus; there is no experimental study evaluating temperature effects on ZIKV. With these data, existing model frameworks can be better informed. But to fully capitalize on these data we will develop novel frameworks at the population level, which (i) incorporate both individual variability in specific mosquito life history traits and the explicit dynamism of vector competence; and (ii) are flexible enough to allow for the investigation of temporally-varying temperature inputs. In addition, we propose to derive an analytical estimate of the time-dependent basic reproduction number and a corresponding metric that captures the variance in secondary cases.