Exploring heterogeneity in mosquito exposure and attraction and its implications for malaria transmission

Malaria transmission exhibits significant heterogeneity within communities, with small proportions of individuals experiencing disproportionate mosquito exposure.

This study addresses critical knowledge gaps in characterising and modelling this heterogeneity. Parameterising Bayesian hierarchical models to field data from Burkina Faso, we compared gamma and lognormal distributions for describing heterogeneity in mosquito biting rates. We then implemented this heterogeneity in an individual-based stochastic modelling platform, OpenMalaria, to assess its impact on transmission dynamics.

The gamma distribution better described the observed field data than the lognormal. This choice has a natural mathematical justification: when individual biting rates follow a gamma distribution and bites occur as a Poisson process, the resulting bite counts follow a negative binomial distribution, which is well-supported empirically for overdispersed count data of this kind. Furthermore, the gamma distribution's lighter tail produces more moderate saturation and immunity effects compared to lognormal-based models, yielding more realistic transmission dynamics. When heterogeneity is introduced to malaria transmission simulations, both prevalence and incidence levels generally decrease across all age groups. Additionally, heterogeneity shifts disease burden towards younger age cohorts and alters the fundamental relationships between entomological inoculation rate and prevalence/incidence. The integration of appropriate heterogeneity distributions into transmission models substantially improved their ability to reproduce field-observed age-incidence curves.

Our findings highlight the importance of accounting for heterogeneous exposure when modelling malaria transmission, particularly in low-transmission and elimination settings where heterogeneity may be more pronounced.