홈 › Papers

Once bitten, twice shy: A modeling framework for incorporating heterogeneous mosquito biting into transmission models

The potential for outbreaks of mosquito-borne disease is linked to the frequency at which mosquitoes feed on blood, also known as the biting rate. But standard models of mosquito-borne disease transmission inherently assume that mosquitoes bite only once per reproductive cycle, an assumption commonly violated in nature. Drivers of multiple biting, like host defensive behaviors or climate factors, also affect the mosquito gonotrophic cycle duration, a quantity used to estimate the biting rate. Here, we present a novel framework for incorporating complex mosquito biting behaviors into transmission models. This framework accounts for heterogeneity in and linkages between biting rates and multiple biting. We provide formulas for the basic offspring number, $\mathcal{N}_0$, and basic reproduction number, $\mathcal{R}_0$, threshold measures for mosquito population and pathogen transmission persistence, respectively. To exhibit its flexibility, we expand on models derived from the framework that arise from empirical, phenomenological, or mechanistic perspectives. Using the GCD as a standard quantity to make comparisons among the models, we show that assumptions about the biting process affect the relationship between GCD and $\mathcal{R}_0$. While under standard assumptions, $\mathcal{R}_0$ is an increasing linear function of the inverse of the GCD, other models of the biting process exhibit saturating or concave relationships. Through sensitivity analysis of the mechanistic model, we determine that parameters related to probing and ingesting success are the most important targets for disease control. This work highlights the importance of incorporating the behavioral dynamics of mosquitoes into transmission models and provides methods for evaluating how individual-level interventions against mosquito biting scale up to determine population-level mosquito-borne disease risk.

Code (1)

Similar Papers 제목 키워드 기반