Wu Jianhong, Tang Biao, Bragazzi Nicola Luigi, Nah Kyeongah, McCarthy Zachary
Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, Canada.
Fields-CQAM Laboratory of Mathematics for Public Health, York University, Toronto, Canada.
J Math Ind. 2020;10(1):15. doi: 10.1186/s13362-020-00083-3. Epub 2020 May 26.
Public health interventions have been implemented to mitigate the spread of coronavirus disease 2019 (COVID-19) in Ontario, Canada; however, the quantification of their effectiveness remains to be done and is important to determine if some of the social distancing measures can be relaxed without resulting in a second wave. We aim to equip local public health decision- and policy-makers with mathematical model-based quantification of implemented public health measures and estimation of the trend of COVID-19 in Ontario to inform future actions in terms of outbreak control and de-escalation of social distancing. Our estimates confirm that (1) social distancing measures have helped mitigate transmission by reducing daily infection contact rate, but the disease transmission probability per contact remains as high as 0.145 and case detection rate was so low that the effective reproduction number remained higher than the threshold for disease control until the closure of non-essential business in the Province; (2) improvement in case detection rate and closure of non-essential business had resulted in further reduction of the effective control number to under the threshold. We predict the number of confirmed cases according to different control efficacies including a combination of reducing further contact rates and transmission probability per contact. We show that improved case detection rate plays a decisive role to reduce the effective reproduction number, and there is still much room in terms of improving personal protection measures to compensate for the strict social distancing measures.
加拿大安大略省已实施公共卫生干预措施，以减缓2019冠状病毒病（COVID-19）的传播；然而，其有效性的量化仍有待完成，并且对于确定一些社交距离措施是否可以放宽而不会导致第二波疫情至关重要。我们旨在为当地公共卫生决策者和政策制定者提供基于数学模型的已实施公共卫生措施的量化以及安大略省COVID-19趋势的估计，以便为未来在疫情控制和社交距离放宽方面的行动提供信息。我们的估计证实：（1）社交距离措施通过降低每日感染接触率有助于减缓传播，但每次接触的疾病传播概率仍高达0.145，且病例检测率很低，以至于在该省非必要业务关闭之前，有效繁殖数一直高于疾病控制阈值；（2）病例检测率的提高和非必要业务的关闭导致有效控制数进一步降至阈值以下。我们根据不同的控制效果预测确诊病例数，包括进一步降低接触率和每次接触的传播概率的组合。我们表明，提高病例检测率在降低有效繁殖数方面起着决定性作用，并且在改善个人防护措施以弥补严格的社交距离措施方面仍有很大空间。
