Vector-borne diseases (VBDs) are infectious diseases caused by vectors, such as mosquitoes, ticks, and flies. A vector is an organism that spreads infection by transmitting pathogens between human or animal hosts. Each vector is able to carry one or multiple pathogens causing different types of VBDs and mosquitoes are such vectors. Although mosquitoes are highly sensitive to climate factors, particularly temperature, modeling studies tend to ignore those effects. Here, we propose a combination of mathematical and statistical models that investigate how temperature mediate the spread of VBDs, with an application to Dengue. With our models, we are interested in addressing three questions: (1) How do mosquitoes respond to temperature changes throughout their life stages? (2) Which mosquito traits impact disease spread the most? (3) Can we successfully predict the time-lag between the start and peak of an outbreak, and therefore improve our prevention and control strategies? To answer these questions, we collect lab data on mosquito traits’ response to temperature and use a Bayesian model for fitting. The outcome curves from this model are incorporated into our mathematical model as parameter functions. Then we derive the basic reproduction ratio and simulate the disease dynamics to formulate our predictions.
Results/Conclusions
Our results include a Bayesian model fitting lab data of dengue mosquito traits’ response to temperature. The results show that mosquitoes are sensitive to temperature during each of their four life stages, namely, eggs, larvae, pupae and adult. In each stage, both the development rate and mortality depend on temperature. We observe that medium temperatures are suitable for a faster development and low mortality due to temperature. Given the derived functions from the Bayesian model, we use the mathematical model to observe Dengue dynamics. Effectively, we find that the disease outbreaks occur when temperatures are medium. For future directions, we are interested in predicting the time lag between an outbreak start and peak, and eventually use this prediction to inform/propose suitable prevention and control strategies for VBDs.