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ORIGINAL RESEARCH article

Front. Appl. Math. Stat.
Sec. Mathematical Biology
Volume 10 - 2024 | doi: 10.3389/fams.2024.1465153

Optimizing Microbe-Infected Mosquito Release: A Stochastic Model for Malaria Prevention

Provisionally accepted
Steeven Belvinos Affognon Steeven Belvinos Affognon 1*Henri Edouard ZEFACK TONNANG Henri Edouard ZEFACK TONNANG 2Philip Ngare Philip Ngare 1Benard Kipchumba Kiplangat Benard Kipchumba Kiplangat 1Shirley Abelman Shirley Abelman 3Jeremy Keith Herren Jeremy Keith Herren 2
  • 1 University of Nairobi, Nairobi, Kenya
  • 2 International Centre of Insect Physiology and Ecology (ICIPE), Nairobi, Kenya
  • 3 University of the Witwatersrand, Johannesburg, South Africa

The final, formatted version of the article will be published soon.

    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 L évy 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 L évy 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.

    Keywords: Malaria, vector control, Stochastic control, HJB equation, Microsporidia MB, L évy Process

    Received: 15 Jul 2024; Accepted: 30 Oct 2024.

    Copyright: © 2024 Affognon, TONNANG, Ngare, Kiplangat, Abelman and Herren. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Steeven Belvinos Affognon, University of Nairobi, Nairobi, Kenya

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.