The COVID-19 pandemic has become a crucial public health problem in the world that disrupted the lives of millions in many countries including the United States. In this study, we present a decision analytic approach which is an efficient tool to assess the effectiveness of early social distancing measures in communities with different population characteristics. First, we empirically estimate the reproduction numbers for two different states. Then, we develop an age-structured compartmental simulation model for the disease spread to demonstrate the variation in the observed outbreak. Finally, we analyze the computational results and show that early trigger social distancing strategies result in smaller death tolls; however, there are relatively larger second waves. Conversely, late trigger social distancing strategies result in higher initial death tolls but relatively smaller second waves. This study shows that decision analytic tools can help policy makers simulate different social distancing scenarios at the early stages of a global outbreak. Policy makers should expect multiple waves of cases as a result of the social distancing policies implemented when there are no vaccines available for mass immunization and appropriate antiviral treatments.

COVID-19 大流行已成为世界上一个重要的公共卫生问题，它扰乱了包括美国在内的许多国家数百万人的生活。在这项研究中，我们提出了一种决策分析方法，这是一种有效的工具，可以评估具有不同人口特征的社区中早期社会疏远措施的有效性。首先，我们根据经验估计两个不同状态的再生数。然后，我们为疾病传播开发了一个年龄结构的分区模拟模型，以证明观察到的爆发的变化。最后，我们分析了计算结果，表明早期触发社会疏远策略会导致较小的死亡人数；但是，还有相对较大的第二波。反过来，延迟触发社会疏远策略导致较高的初始死亡人数，但第二波相对较小。这项研究表明，决策分析工具可以帮助决策者在全球疫情爆发的早期模拟不同的社交距离场景。由于在没有疫苗可用于大规模免疫和适当的抗病毒治疗的情况下实施的社会疏远政策，政策制定者应该预料到会出现多波病例。