The COVID-19 pandemic has forced societies across the world to resort to social distancing to slow the spread of the SARS-CoV-2 virus. Due to the economic impacts of social distancing, there is growing desire to relax these measures. To characterize a range of possible strategies for control and to understand their consequences, we performed an optimal control analysis of a mathematical model of SARS-CoV-2 transmission. Given that the pandemic is already underway and controls have already been initiated, we calibrated our model to data from the USA and focused our analysis on optimal controls from May 2020 through December 2021. We found that a major factor that differentiates strategies that prioritize lives saved versus reduced time under control is how quickly control is relaxed once social distancing restrictions expire in May 2020. Strategies that maintain control at a high level until at least summer 2020 allow for tapering of control thereafter and minimal deaths, whereas strategies that relax control in the short term lead to fewer options for control later and a higher likelihood of exceeding hospital capacity. Our results also highlight that the potential scope for controlling COVID-19 until a vaccine is available depends on epidemiological parameters about which there is still considerable uncertainty, including the basic reproduction number and the effectiveness of social distancing. In light of those uncertainties, our results do not constitute a quantitative forecast and instead provide a qualitative portrayal of possible outcomes from alternative approaches to control.

中文翻译：

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通过非药物干预措施最佳控制 COVID-19 大流行


COVID-19 大流行迫使世界各地的社会采取保持社交距离的措施来减缓 SARS-CoV-2 病毒的传播。由于社交距离的经济影响，人们越来越希望放松这些措施。为了描述一系列可能的控制策略并了解其后果，我们对 SARS-CoV-2 传播的数学模型进行了最优控制分析。鉴于大流行已经发生并且控制措施已经启动，我们根据美国的数据校准了我们的模型，并将我们的分析重点放在 2020 年 5 月至 2021 年 12 月的最佳控制上。我们发现，区分优先考虑挽救生命的策略的一个主要因素与减少控制时间的关系是，一旦社交距离限制在 2020 年 5 月到期，放松控制的速度有多快。至少在 2020 年夏季之前保持高水平控制的策略允许在此后逐渐减少控制并最大限度地减少死亡，而在 2020 年 5 月放松控制的策略短期导致以后控制的选择较少，并且超出医院容量的可能性更高。我们的结果还强调，在疫苗问世之前控制 COVID-19 的潜在范围取决于流行病学参数，这些参数仍存在相当大的不确定性，包括基本繁殖数和社交距离的有效性。鉴于这些不确定性，我们的结果并不构成定量预测，而是提供了替代控制方法可能产生的结果的定性描述。