Modeling the impact of public response on the COVID-19 pandemic in Ontario.

The outbreak of SARS-CoV-2 is thought to have originated in Wuhan, China in late 2019 and has since spread quickly around the world. To date, the virus has infected tens of millions of people worldwide, compelling governments to implement strict policies to counteract community spread. Federal, prov...

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Main Authors: Brydon Eastman, Cameron Meaney, Michelle Przedborski, Mohammad Kohandel
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2021-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0249456
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spelling doaj-8c641b79989e4de4b97e3d9307e83d8b2021-05-08T04:32:14ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01164e024945610.1371/journal.pone.0249456Modeling the impact of public response on the COVID-19 pandemic in Ontario.Brydon EastmanCameron MeaneyMichelle PrzedborskiMohammad KohandelThe outbreak of SARS-CoV-2 is thought to have originated in Wuhan, China in late 2019 and has since spread quickly around the world. To date, the virus has infected tens of millions of people worldwide, compelling governments to implement strict policies to counteract community spread. Federal, provincial, and municipal governments have employed various public health policies, including social distancing, mandatory mask wearing, and the closure of schools and businesses. However, the implementation of these policies can be difficult and costly, making it imperative that both policy makers and the citizenry understand their potential benefits and the risks of non-compliance. In this work, a mathematical model is developed to study the impact of social behaviour on the course of the pandemic in the province of Ontario. The approach is based upon a standard SEIRD model with a variable transmission rate that depends on the behaviour of the population. The model parameters, which characterize the disease dynamics, are estimated from Ontario COVID-19 epidemiological data using machine learning techniques. A key result of the model, following from the variable transmission rate, is the prediction of the occurrence of a second wave using the most current infection data and disease-specific traits. The qualitative behaviour of different future transmission-reduction strategies is examined, and the time-varying reproduction number is analyzed using existing epidemiological data and future projections. Importantly, the effective reproduction number, and thus the course of the pandemic, is found to be sensitive to the adherence to public health policies, illustrating the need for vigilance as the economy continues to reopen.https://doi.org/10.1371/journal.pone.0249456
collection DOAJ
language English
format Article
sources DOAJ
author Brydon Eastman
Cameron Meaney
Michelle Przedborski
Mohammad Kohandel
spellingShingle Brydon Eastman
Cameron Meaney
Michelle Przedborski
Mohammad Kohandel
Modeling the impact of public response on the COVID-19 pandemic in Ontario.
PLoS ONE
author_facet Brydon Eastman
Cameron Meaney
Michelle Przedborski
Mohammad Kohandel
author_sort Brydon Eastman
title Modeling the impact of public response on the COVID-19 pandemic in Ontario.
title_short Modeling the impact of public response on the COVID-19 pandemic in Ontario.
title_full Modeling the impact of public response on the COVID-19 pandemic in Ontario.
title_fullStr Modeling the impact of public response on the COVID-19 pandemic in Ontario.
title_full_unstemmed Modeling the impact of public response on the COVID-19 pandemic in Ontario.
title_sort modeling the impact of public response on the covid-19 pandemic in ontario.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2021-01-01
description The outbreak of SARS-CoV-2 is thought to have originated in Wuhan, China in late 2019 and has since spread quickly around the world. To date, the virus has infected tens of millions of people worldwide, compelling governments to implement strict policies to counteract community spread. Federal, provincial, and municipal governments have employed various public health policies, including social distancing, mandatory mask wearing, and the closure of schools and businesses. However, the implementation of these policies can be difficult and costly, making it imperative that both policy makers and the citizenry understand their potential benefits and the risks of non-compliance. In this work, a mathematical model is developed to study the impact of social behaviour on the course of the pandemic in the province of Ontario. The approach is based upon a standard SEIRD model with a variable transmission rate that depends on the behaviour of the population. The model parameters, which characterize the disease dynamics, are estimated from Ontario COVID-19 epidemiological data using machine learning techniques. A key result of the model, following from the variable transmission rate, is the prediction of the occurrence of a second wave using the most current infection data and disease-specific traits. The qualitative behaviour of different future transmission-reduction strategies is examined, and the time-varying reproduction number is analyzed using existing epidemiological data and future projections. Importantly, the effective reproduction number, and thus the course of the pandemic, is found to be sensitive to the adherence to public health policies, illustrating the need for vigilance as the economy continues to reopen.
url https://doi.org/10.1371/journal.pone.0249456
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