Speaker
Description
Heterogeneity in mosquito biting rates is a key driver of malaria transmission dynamics and strongly influences the distribution of cases within a population. We develop a stochastic compartmental model describing malaria transmission between humans and mosquitoes, incorporating variation in biting exposure. We quantify the level of biting heterogeneity required to generate different Pareto fractions in malaria case distributions, from relatively homogeneous transmission to scenarios where a small proportion of individuals account for a large proportion of cases. We then investigate the impact of targeted control strategies that prioritise individuals based on biting exposure. Our results show that targeting highly bitten individuals can substantially reduce overall transmission. However, protecting specific individuals may initially lead to temporary increases in cases among non-targeted individuals due to shifts in transmission dynamics. Over longer time horizons, targeted interventions generally reduce transmission across the entire community, including those not directly receiving control measures. These findings highlight the importance of accounting for exposure heterogeneity when designing malaria control strategies and demonstrate the potential long-term community-wide benefits of targeted interventions.
