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. (November 2022)

• Record Type: Journal Article
• Authors: Pájaro, Manuel; Fajar, Noelia M.; Alonso, Antonio A.; Otero-Muras, Irene
• Is Part Of: Chaos, solitons and fractals. Volume 164(2022)
• Publication Date: 2022-11
• Subjects: SIR model -- COVID-19 -- SARS-coV-2 -- Stochastic mechanistic model -- Chemical master equation -- Stochastic simulation algorithm; Chaotic behavior in systems -- Periodicals; Solitons -- Periodicals; Fractals -- Periodicals; Chaotic behavior in systems; Fractals; Solitons; Periodicals; 003.7
• DOI: 10.1016/j.chaos.2022.112671
• Languages: English
• ISSNs: 0960-0779

Abstract

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). Highlights: A stochastic SIR model predicts the evolution of COVID-19 using data from sewage. Model and software are easily calibrated to be applied in different municipalities. The model is robust and flexible, adaptable for surveillance of other pathogens. The model successfully predicts COVID-19 spread in Galician municipalities.