IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i12p2609-d1165968.html
   My bibliography  Save this article

Adapting Strategies for Effective Schistosomiasis Prevention: A Mathematical Modeling Approach

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/12/2609/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/12/2609/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. 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.
    3. 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.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fatima-Zahra Jaouimaa & Daniel Dempsey & Suzanne Van Osch & Stephen Kinsella & Kevin Burke & Jason Wyse & James Sweeney, 2021. "An age-structured SEIR model for COVID-19 incidence in Dublin, Ireland with framework for evaluating health intervention cost," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-25, December.
    2. Belém Barbosa & José Ramón Saura & Dag Bennett, 2024. "How do entrepreneurs perform digital marketing across the customer journey? A review and discussion of the main uses," The Journal of Technology Transfer, Springer, vol. 49(1), pages 69-103, February.
    3. Overstall, Antony M. & Woods, David C. & Martin, Kieran J., 2019. "Bayesian prediction for physical models with application to the optimization of the synthesis of pharmaceutical products using chemical kinetics," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 126-142.
    4. Serrouya, R. & Dickie, M. & DeMars, C. & Wittmann, M.J. & Boutin, S., 2020. "Predicting the effects of restoring linear features on woodland caribou populations," Ecological Modelling, Elsevier, vol. 416(C).
    5. Moore, Christopher M. & Catella, Samantha A. & Abbott, Karen C., 2018. "Population dynamics of mutualism and intraspecific density dependence: How θ-logistic density dependence affects mutualistic positive feedback," Ecological Modelling, Elsevier, vol. 368(C), pages 191-197.
    6. Yan, Chuan & Zhang, Zhibin, 2018. "Dome-shaped transition between positive and negative interactions maintains higher persistence and biomass in more complex ecological networks," Ecological Modelling, Elsevier, vol. 370(C), pages 14-21.
    7. Cécile Cathalot & Erwan G. Roussel & Antoine Perhirin & Vanessa Creff & Jean-Pierre Donval & Vivien Guyader & Guillaume Roullet & Jonathan Gula & Christian Tamburini & Marc Garel & Anne Godfroy & Pier, 2021. "Hydrothermal plumes as hotspots for deep-ocean heterotrophic microbial biomass production," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    8. Lamonica, Dominique & Herbach, Ulysse & Orias, Frédéric & Clément, Bernard & Charles, Sandrine & Lopes, Christelle, 2016. "Mechanistic modelling of daphnid-algae dynamics within a laboratory microcosm," Ecological Modelling, Elsevier, vol. 320(C), pages 213-230.
    9. Beekam Kebede Olkeba & Pieter Boets & Seid Tiku Mereta & Belayhun Mandefro & Gemechu Debesa & Mahmud Ahmednur & Argaw Ambelu & Wolyu Korma & Peter L. M. Goethals, 2021. "Malacological and Parasitological Surveys on Ethiopian Rift Valley Lakes: Implications for Control and Elimination of Snail-Borne Diseases," IJERPH, MDPI, vol. 19(1), pages 1-15, December.
    10. Stahl, Gerhard & Wang, Shaohui & Wendt, Markus, 2011. "Validate Correlation of an ESG: Treasury Yields across," Hannover Economic Papers (HEP) dp-476, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    11. Hassan Ahmed Hassan Ahmed Ismail & Abed el Aziz Abed el Rahim Mohamed Ahmed & Seungman Cha & Yan Jin, 2022. "The Life Histories of Intermediate Hosts and Parasites of Schistosoma haematobium and Schistosoma mansoni in the White Nile River, Sudan," IJERPH, MDPI, vol. 19(3), pages 1-12, January.
    12. Alex Root, 2019. "Mathematical Modeling of The Challenge to Detect Pancreatic Adenocarcinoma Early with Biomarkers," Challenges, MDPI, vol. 10(1), pages 1-15, April.
    13. Chevallier, Damien & Mourrain, Baptiste & Girondot, Marc, 2020. "Modelling leatherback biphasic indeterminate growth using a modified Gompertz equation," Ecological Modelling, Elsevier, vol. 426(C).
    14. Diane Lefaudeux & Supriya Sen & Kevin Jiang & Alexander Hoffmann, 2022. "Kinetics of mRNA nuclear export regulate innate immune response gene expression," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    15. Jessica A Lee & Siavash Riazi & Shahla Nemati & Jannell V Bazurto & Andreas E Vasdekis & Benjamin J Ridenhour & Christopher H Remien & Christopher J Marx, 2019. "Microbial phenotypic heterogeneity in response to a metabolic toxin: Continuous, dynamically shifting distribution of formaldehyde tolerance in Methylobacterium extorquens populations," PLOS Genetics, Public Library of Science, vol. 15(11), pages 1-38, November.
    16. Turner, Rolf & Banerjee, Pradeep & Shahlori, Rayomand, 2014. "Optimal Asset Pricing," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 58(i11).
    17. Carturan, Bruno S. & Siewe, Nourridine & Cobbold, Christina A. & Tyson, Rebecca C., 2023. "Bumble bee pollination and the wildflower/crop trade-off: When do wildflower enhancements improve crop yield?," Ecological Modelling, Elsevier, vol. 484(C).
    18. Yerin Jung & Yoonsub Kim & Hwi-Soo Seol & Jong-Hyeon Lee & Jung-Hwan Kwon, 2021. "Spatial Uncertainty in Modeling Inhalation Exposure to Volatile Organic Compounds in Response to the Application of Consumer Spray Products," IJERPH, MDPI, vol. 18(10), pages 1-11, May.
    19. Gregory L Watson & Di Xiong & Lu Zhang & Joseph A Zoller & John Shamshoian & Phillip Sundin & Teresa Bufford & Anne W Rimoin & Marc A Suchard & Christina M Ramirez, 2021. "Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-20, March.
    20. Venolia, Celeste T. & Lavaud, Romain & Green-Gavrielidis, Lindsay A. & Thornber, Carol & Humphries, Austin T., 2020. "Modeling the Growth of Sugar Kelp (Saccharina latissima) in Aquaculture Systems using Dynamic Energy Budget Theory," Ecological Modelling, Elsevier, vol. 430(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:12:p:2609-:d:1165968. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.