Exploring heterogeneity in mosquito exposure and attraction and its implications for malaria transmission
5 affiliations
- 1Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwill, Switzerland
- 2University of Basel, Basel, Switzerland
- 3The Kids Research Institute Australia, Nedlands, Australia
- 4Centre for Child Health Research, University of Western Australia, Crawley, Australia
- 5The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, Mathematics Institute, University of Warwick, Coventry, United Kingdom
Abstract
Background: Malaria transmission exhibits significant heterogeneity within communities, with small proportions of individuals experiencing disproportionate mosquito exposure. Methods: 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. Findings: 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. Interpretation: 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. Author summary: Malaria does not affect everyone equally. Within a community some individuals are bitten by infectious mosquitoes more frequently than others, due to socioeconomic factors. This unequal distribution of bites, known as heterogeneity, means that a small proportion of the population often bears a disproportionate share of the transmission burden. This heterogeneity becomes more pronounced as transmission intensity declines, yet it is rarely accounted for in the mathematical models used to guide malaria control policy. In this study, we used statistical methods to analyse field data from Burkina Faso and determine the best mathematical description of how mosquito bites are distributed across individuals. We found that a gamma distribution describes this heterogeneity more accurately than the lognormal distribution. We then incorporated this into OpenMalaria, a widely used malaria transmission model, and showed that accounting for heterogeneity substantially changes predicted patterns of disease burden, particularly across age groups. For several field sites where standard models performed poorly, incorporating heterogeneity improved model fit by up to 95%. As transmission declines and elimination becomes a realistic goal for many countries, ensuring that models accurately capture who is most at risk will be critical for designing interventions that reach those who need them most.
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BibTeX
@article{kamber2026exploring,
title = {{Exploring heterogeneity in mosquito exposure and attraction and its implications for malaria transmission}},
author = {Lars Kamber and Aurélien Cavelan and Melissa A. Penny and Nakul Chitnis and Emma Louise Fairbanks},
journal = {PLOS Computational Biology},
year = {2026},
month = sep,
doi = {10.1371/journal.pcbi.1014631},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014631},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Exploring heterogeneity in mosquito exposure and attraction and its implications for malaria transmission
AU - Lars Kamber
AU - Aurélien Cavelan
AU - Melissa A. Penny
AU - Nakul Chitnis
AU - Emma Louise Fairbanks
PY - 2026
DA - 2026/09/08
JO - PLOS Computational Biology
DO - 10.1371/journal.pcbi.1014631
UR - https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014631
AB - Background: Malaria transmission exhibits significant heterogeneity within communities, with small proportions of individuals experiencing disproportionate mosquito exposure. Methods: 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. Findings: 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. Interpretation: 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. Author summary: Malaria does not affect everyone equally. Within a community some individuals are bitten by infectious mosquitoes more frequently than others, due to socioeconomic factors. This unequal distribution of bites, known as heterogeneity, means that a small proportion of the population often bears a disproportionate share of the transmission burden. This heterogeneity becomes more pronounced as transmission intensity declines, yet it is rarely accounted for in the mathematical models used to guide malaria control policy. In this study, we used statistical methods to analyse field data from Burkina Faso and determine the best mathematical description of how mosquito bites are distributed across individuals. We found that a gamma distribution describes this heterogeneity more accurately than the lognormal distribution. We then incorporated this into OpenMalaria, a widely used malaria transmission model, and showed that accounting for heterogeneity substantially changes predicted patterns of disease burden, particularly across age groups. For several field sites where standard models performed poorly, incorporating heterogeneity improved model fit by up to 95%. As transmission declines and elimination becomes a realistic goal for many countries, ensuring that models accurately capture who is most at risk will be critical for designing interventions that reach those who need them most.
SN - 1553-7358
ER -