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Adapting Strategies for Effective Schistosomiasis Prevention: A Mathematical Modeling Approach

Author

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  • Zadoki Tabo

    (Department of Animal Ecology and Systematics, Justus Liebig University Giessen, Heinrich-Buff-Ring 26 (iFZ), 35392 Giessen, Germany
    Department of Landscape Ecology and Resource Management, Justus Liebig University Giessen, Heinrich-Buff-Ring 26 (iFZ), 35392 Giessen, Germany)

  • Chester Kalinda

    (Department of Animal Ecology and Systematics, Justus Liebig University Giessen, Heinrich-Buff-Ring 26 (iFZ), 35392 Giessen, Germany
    Bill and Joyce Cummings Institute of Global Health, University of Global Health Equity, Kigali Heights, Plot 772 KG 7 Ave., Kigali P.O. Box 6955, Rwanda)

  • Lutz Breuer

    (Department of Landscape Ecology and Resource Management, Justus Liebig University Giessen, Heinrich-Buff-Ring 26 (iFZ), 35392 Giessen, Germany
    Centre for International Development and Environmental Research (ZEU), Justus Liebig University Giessen, Senckenbergstrasse 3, 35390 Giessen, Germany)

  • Christian Albrecht

    (Department of Animal Ecology and Systematics, Justus Liebig University Giessen, Heinrich-Buff-Ring 26 (iFZ), 35392 Giessen, Germany)

Abstract

One of the most deadly neglected tropical diseases known to man is schistosomiasis. Understanding how the disease spreads and evaluating the relevant control strategies are key steps in predicting its spread. We propose a mathematical model to evaluate the potential impact of four strategies: chemotherapy, awareness programs, the mechanical removal of snails and molluscicides, and the impact of a change in temperature on different molluscicide performances based on their half-lives and the length of time they persist in contact with target species. The results show that the recruitment rate of humans and the presence of cercaria and miracidia parasites are crucial factors in disease transmission. However, schistosomiasis can be entirely eradicated by combining all of the four strategies. In the face of climate change and molluscicide degradation, the results show that increasing the temperatures and the number of days a molluscicide persists in the environment before it completely degrades decreases the chemically induced mortality rate of snails while increasing the half-life of different molluscicides increases the death rate of snails. Therefore, eradicating schistosomiasis effectively necessitates a comprehensive integration of all preventative measures. Moreover, regions with different weather patterns and seasonal climates need strategies that have been adapted in terms of the appropriate molluscicide and time intervals for reapplication and effective schistosomiasis control.

Suggested Citation

  • Zadoki Tabo & Chester Kalinda & Lutz Breuer & Christian Albrecht, 2023. "Adapting Strategies for Effective Schistosomiasis Prevention: A Mathematical Modeling Approach," Mathematics, MDPI, vol. 11(12), pages 1-18, June.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:12:p:2609-:d:1165968
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    References listed on IDEAS

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    1. Gisele Andrade & David J Bertsch & Andrea Gazzinelli & Charles H King, 2017. "Decline in infection-related morbidities following drug-mediated reductions in the intensity of Schistosoma infection: A systematic review and meta-analysis," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 11(2), pages 1-23, February.
    2. Charles H King & Laura J Sutherland & David Bertsch, 2015. "Systematic Review and Meta-analysis of the Impact of Chemical-Based Mollusciciding for Control of Schistosoma mansoni and S. haematobium Transmission," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 9(12), pages 1-23, December.
    3. Ebrima Kanyi & Ayodeji Sunday Afolabi & Nelson Owuor Onyango, 2021. "Mathematical Modeling and Analysis of Transmission Dynamics and Control of Schistosomiasis," Journal of Applied Mathematics, Hindawi, vol. 2021, pages 1-20, May.
    4. Soetaert, Karline & Petzoldt, Thomas & Setzer, R. Woodrow, 2010. "Solving Differential Equations in R: Package deSolve," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i09).
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