A weather-driven mathematical model of Culex population abundance and the impact of vector control interventions

TL;DR

This paper introduces a weather-driven mechanistic ODE model for mosquito population dynamics, capturing seasonal fluctuations and evaluating vector control strategies to better predict and mitigate mosquito-borne disease risks.

Contribution

It presents a novel process-based model incorporating weather data and lifecycle stages, improving prediction of mosquito abundance and intervention impacts.

Findings

Model accurately predicts seasonal mosquito abundance fluctuations.

Sensitivity analysis identifies key factors influencing population dynamics.

Model assesses effectiveness of adulticide strategies in reducing mosquito populations.

Abstract

Even as the incidence of mosquito-borne diseases like West Nile Virus (WNV) in North America has risen over the past decade, effectively modelling mosquito population density or, the abundance has proven to be a persistent challenge. It is critical to capture the fluctuations in mosquito abundance across seasons in order to forecast the varying risk of disease transmission from one year to the next. We develop a process-based mechanistic weather-driven Ordinary Differential Equation (ODE) model to study the population biology of both aqueous and terrestrial stages of mosquito population. The progression of mosquito lifecycle through these stages is influenced by different factors, including temperature, daylight hours, intra-species competition and the availability of aquatic habitats. Weather-driven parameters are utilised in our work, are a combination of laboratory research and literature data. In our model, we include precipitation data as a substitute for evaluating additional mortality in the mosquito population. We compute the \textit{Basic offspring number} of the associated model and perform sensitivity analysis. Finally, we employ our model to assess the effectiveness of various adulticides strategies to predict the reduction in mosquito population. This enhancement in modelling of mosquito abundance can be instrumental in guiding interventions aimed at reducing mosquito populations and mitigating mosquito-borne diseases such as the WNV.