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Stochastic SIR model predicts the evolution of COVID-19 epidemics from public health and wastewater data in small and medium-sized municipalities: A one year study

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  • Pájaro, Manuel
  • Fajar, Noelia M.
  • Alonso, Antonio A.
  • Otero-Muras, Irene

Abstract

The level of unpredictability of the COVID-19 pandemics poses a challenge to effectively model its dynamic evolution. In this study we incorporate the inherent stochasticity of the SARS-CoV-2 virus spread by reinterpreting the classical compartmental models of infectious diseases (SIR type) as chemical reaction systems modeled via the Chemical Master Equation and solved by Monte Carlo Methods. Our model predicts the evolution of the pandemics at the level of municipalities, incorporating for the first time (i) a variable infection rate to capture the effect of mitigation policies on the dynamic evolution of the pandemics (ii) SIR-with-jumps taking into account the possibility of multiple infections from a single infected person and (iii) data of viral load quantified by RT-qPCR from samples taken from Wastewater Treatment Plants. The model has been successfully employed for the prediction of the COVID-19 pandemics evolution in small and medium size municipalities of Galicia (Northwest of Spain).

Suggested Citation

  • Pájaro, Manuel & Fajar, Noelia M. & Alonso, Antonio A. & Otero-Muras, Irene, 2022. "Stochastic SIR model predicts the evolution of COVID-19 epidemics from public health and wastewater data in small and medium-sized municipalities: A one year study," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    • Handle: RePEc:eee:chsofr:v:164:y:2022:i:c:s0960077922008505
    DOI: 10.1016/j.chaos.2022.112671
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    References listed on IDEAS

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    1. Cai, Yongli & Kang, Yun & Wang, Weiming, 2017. "A stochastic SIRS epidemic model with nonlinear incidence rate," Applied Mathematics and Computation, Elsevier, vol. 305(C), pages 221-240.
    2. Jones, Andrew & Strigul, Nikolay, 2021. "Is spread of COVID-19 a chaotic epidemic?," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    3. Cumsille, Patricio & Rojas-Díaz, Óscar & de Espanés, Pablo Moisset & Verdugo-Hernández, Paula, 2022. "Forecasting COVID-19 Chile’ second outbreak by a generalized SIR model with constant time delays and a fitted positivity rate," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 193(C), pages 1-18.
    4. M. Pájaro & I. Otero-Muras & C. Vázquez & A. A. Alonso, 2019. "Transient hysteresis and inherent stochasticity in gene regulatory networks," Nature Communications, Nature, vol. 10(1), pages 1-7, December.
    5. Mahapatra, D.P. & Triambak, S., 2022. "Towards predicting COVID-19 infection waves: A random-walk Monte Carlo simulation approach," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
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    1. Dongpo Hu & Xuexue Liu & Kun Li & Ming Liu & Xiao Yu, 2023. "Codimension-Two Bifurcations of a Simplified Discrete-Time SIR Model with Nonlinear Incidence and Recovery Rates," Mathematics, MDPI, vol. 11(19), pages 1-24, September.

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