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Quantifying the role of social distancing, personal protection and case detection in mitigating COVID-19 outbreak in Ontario, Canada.
Journal of Mathematics in Industry Pub Date : 2020-05-26 , DOI: 10.1186/s13362-020-00083-3
Jianhong Wu 1, 2 , Biao Tang 1, 2 , Nicola Luigi Bragazzi 1, 2 , Kyeongah Nah 1, 2 , Zachary McCarthy 1, 2
Affiliation  

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.

中文翻译:

量化社交疏远,个人保护和案件发现在缓解加拿大安大略省COVID-19爆发中的作用。

已实施公共卫生干预措施以减轻加拿大安大略省2019年冠状病毒病(COVID-19)的传播; 然而,对其有效性的量化尚待完成,对于确定是否可以放松一些社会疏远措施而又不会引起第二波冲击至关重要。我们旨在为当地公共卫生决策者和决策者提供基于数学模型的已实施公共卫生措施量化方法,并估算安大略省COVID-19的趋势,从而为今后在疾病暴发控制和降级方面的行动提供信息疏远。我们的估计证实(1)社会疏远措施通过降低每日感染的接触率有助于缓解传播,但每次接触的疾病传播几率仍高达0。145,病例发现率很低,以致有效繁殖数量一直高于疾病控制阈值,直到该省非必需业务关闭为止;(2)案件发现率的提高和非必要业务的关闭导致有效控制数量进一步降低到阈值以下。我们根据不同的控制效果,包括进一步降低接触率和降低每次接触的传播概率的组合,来预测确诊病例的数量。我们表明,提高病例发现率对于减少有效再生产数量起着决定性作用,并且在改善人身保护措施以补偿严格的社会疏远措施方面仍有很大空间。
更新日期:2020-05-26
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