Work

Optimizing microbe-infected mosquito release: a stochastic model for malaria prevention

Year: 2024

Type: article

Abstract: Malaria remains a critical public health challenge in Africa, demanding innovative control strategies. This study introduces a novel approach using Microsporidia MB-infected mosquitoes and stochastic optimal control within a Lévy process framework to regulate mosquito release strategies. The primary goal is to optimize Microsporidia MB prevalence within mosquito populations to disrupt Plasmodium transmission to humans. By incorporating Lévy noise into the modeling process, we capture the inherent randomness of mosquito dynamics, improving intervention accuracy. The model, guided by the Hamilton–Jacobi–Bellman (HJB) equation, optimizes release protocols while accounting for key environmental factors like seasonality and temperature fluctuations. Results show that intervention success depends on local climatic conditions, underscoring the need for flexible, region-specific strategies in malaria-endemic areas. Focus regions include Kenya, Ghana, Niger, and Benin, where Microsporidia MB has been confirmed. Findings suggest that targeted mosquito releases could significantly reduce malaria transmission, offering valuable insights for public health efforts.

Source: Frontiers in Applied Mathematics and Statistics

DOI: 10.3389/fams.2024.1465153

Authors: Steeven Belvinos Affognon, Henri E. Z. Tonnang, Philip Ngare, Benard Kipchumba Kiplangat, Shirley Abelman, Jeremy K. Herren

Institutions: University of Nairobi, International Centre of Insect Physiology and Ecology, University of the Witwatersrand

Language: English

Topic: Mathematical and Theoretical Epidemiology and Ecology Models

Subfield: Public Health, Environmental and Occupational Health

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,285