Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada.
PLoS One. 2021 Apr 14;16(4):e0249456. doi: 10.1371/journal.pone.0249456. eCollection 2021.
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.
SARS-CoV-2 的爆发被认为始于 2019 年末的中国武汉，此后迅速在全球范围内传播。迄今为止，该病毒已感染了全球数千万人，迫使各国政府实施严格的政策来遏制社区传播。联邦、省和市政府采取了各种公共卫生政策，包括社交距离、强制戴口罩以及关闭学校和企业。然而，这些政策的实施可能会遇到困难和代价高昂，因此政策制定者和民众都必须了解其潜在的益处和不遵守规定的风险。在这项工作中，开发了一个数学模型来研究社会行为对安大略省大流行进程的影响。该方法基于一个带有依赖于人口行为的可变传播率的标准 SEIRD 模型。使用机器学习技术，从安大略省 COVID-19 流行病学数据中估计出描述疾病动态的模型参数。模型的一个关键结果是，由于可变传播率，可以根据最新的感染数据和特定疾病的特征来预测第二波疫情的发生。研究了不同未来传播减少策略的定性行为，并使用现有的流行病学数据和未来预测来分析时变繁殖数。重要的是，有效繁殖数，即大流行的进程，被发现对公共卫生政策的遵守程度敏感，这表明随着经济继续重新开放，需要保持警惕。
