Waterborne Disease Dynamics with a River
DOI:
https://doi.org/10.33043/c9yu5f2yznfKeywords:
Compartmental Model, Waterborne Disease, Cholera, Basic Reproductive Number, Parameter SensitivityAbstract
A wide range of waterborne diseases spread through a population through both human to-human interaction and water-to-human interaction. In this paper, we propose a deterministic compartment model to simulate the transmission of a waterborne pathogen through a population whose common water source is a river with both upstream and downstream access points. This allows for a distinction between drinking and shedding behavior with respect to the upstream and downstream water sources. We consider the effectiveness of several intervention methods with respect to two metrics: the basic reproductive number, R0, in the epidemic phase and the steady-state infected population fraction, i∞, in the endemic phase. Using both local and global sensitivity analysis techniques, the relative effectiveness of interventions are demonstrated, leading to a clearer understanding of how to prioritize efforts to either prevent an epidemic or to reduce the endemic level of disease in a population.
References
I.B. Augsburger, G.K. Galanthay, J.H. Tarosky, J. Rychta, and D. Taylor, Voluntary vaccination may not stop monkeypox outbreak: A game-theoretic model, PLoS Negl Trop Dis 16 (2002), no. 12, 1–29.
O.C. Collins and K.J. Duffy, Analysis and optimal control intervention strategies of a waterborne disease model: A realistic case study, Journal of Applied Mathematics 2018 (2018), 1–14.
PL Delamater, EJ Street, TF Leslie, Y. Yang, and KH Jacobsen, Complexity of the basic reproduction number (r0)., Emerg Infect Dis. 25 (2019), no. 1, 1–4.
O. Diekmann, J.A. Heesterbeek, and M.G. Roberts, The construction of next-generation matrices for compartmental epidemic models, J R Soc Interface 7 (2010), no. 47, 873–885.
M Doherty, P. Buchy, B. Standaert, C. Giaquinto, and D. Prado-Cohrs, Vaccine impact: Benefits for human health., Vaccine 34 (2016), no. 52, 6707–6714.
Center for Disease Control, Waterborne disease in the United States, (2023), https://www.cdc.gov/healthywater/surveillance/burden/index.html.
I. C.-H. Fung, Cholera transmission dynamic models for public health practitioners, Emerg Themes Epidemiol 11 (2014), no. 1, 1–11.
J.I. Irunde, J.Z. Ndendya, J.A. Mwasunda, and P.K. Robert, Modeling the impact of screening and treatment on typhoid fever dynamics in unprotected population, Results in Physics 54 (2023), 107120.
G. Kozyreff, Asymptotic solutions of the SIR and SEIR models well above the epidemic threshold, medRxiv 9 (2022), no. 12, 1–15.
M. Li, J. Ma, and P. van den Driessche, Model for disease dynamics of a water-borne pathogen on a random network, J Math Biol 71 (2015), no. 4, 961–977.
S. Marino, I.B. Hogue, C.J. Ray, and D.E. Kirschner, A methodology for performing global uncertainty and sensitivity analysis in systems biology, J Theor Biol 254 (2008), no. 1, 178–196.
R.L.M. Neilan, E. Schaefer, and H. Gaff, Modeling optimal intervention strategies for Cholera, Bull. Math. Biol. 72 (2010), no. 1, 2004–2018.
World Health Organization, Drinking-water, Sept. 2023, https://www.who.int/news-room/fact-sheets/detail/drinking-water.
S.L. Robertson, M.C. Eisenberg, and J.H. Tien, Heterogeneity in multiple transmission pathways: modelling the spread of cholera and other waterborne disease in networks with a common water source, Journal of Biological Dynamics 7 (2013), no. 1, 254–275, PMID: 24303905.
N. Shah, Deterministic mathematical model for dynamics of water borne diseases, Advances in Research 2 (2014), 515–522.
J.H. Tien and D.J.D. Earn, Multiple transmission pathways and disease dynamics in a waterborne pathogen model, Bull. Math. Biol. 72 (2010), no. 1, 1506–1533.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Chad Westphal, Jackson Leeper

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.