Modelling Heterogeneity in Malaria Transmission Dynamics: Application of Kumaraswamy-Weibull Model to a High Burden Setting
DOI:
https://doi.org/10.63255/05-99123.25/05Keywords:
Kumaraswamy-Weibull distribution, Malaria modelling, Extrinsic incubation period, Transmission heterogeneity, mathematical epidemiology, Temperature-dependent modelsAbstract
Nigeria bears the highest global malaria burden, accounting for approximately 27% of global malaria cases and 32% of malaria deaths, although progress in control remains unchanged despite substantial investments. This study introduced the Kumaraswamy-Weibull (KW) distribution to better model malaria transmission dynamics, addressing limitations of traditional exponential and gamma distributions that inadequately represent the bounded, right-skewed nature of key biological processes such as the extrinsic incubation period (EIP). A temperature-dependent Kw-W model was developed for the EIP, incorporating climatic variables as functional parameters and comparing model performance against standard distributions using the Watanabe-Akaike Information Criterion (WAIC). The Kw-W distribution demonstrated superior fit to EIP data when compared with exponential models, capturing the bounded,
heterogeneous nature of parasite development. Temperature significantly influenced both scale and shape parameters, revealing that higher temperatures not only accelerate development, but also increase its variability and reduce synchrony within mosquito populations. Integration into a modified Ross-Macdonald framework yielded R0 estimates lower than exponential-based models during peak transmission seasons, suggesting conventional approaches may overestimate transmission potential. The framework enabled optimisation of intervention timing, identifying temperature-adjusted schedules that differed by 2-3 days from standard recommendations and could increase indoor residual spraying effectiveness by 12-18%. The Kumaraswamy-Weibull model provides a flexible, biologically grounded statistical framework for malaria modelling that offers more accurate estimates of transmission dynamics and enables precision intervention timing, offering mathematical epidemiology with direct applications to malaria elimination strategies in high-burden settings.
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