Epidemiological models of COVID-19 transmission assume that recovered individuals have a fully protected immunity. To date, there is no definite answer about whether people who recover from COVID-19 can be reinfected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In the absence of a clear answer about the risk of reinfection, it is instructive to consider the possible scenarios. To study the epidemiological dynamics with the possibility of reinfection, I use a Susceptible-Exposed-Infectious-Resistant-Susceptible model with the time-varying transmission rate. I consider three different ways of modeling reinfection. The crucial feature of this study is that I explore both the difference between the reinfection and no-reinfection scenarios and how the mitigation measures affect this difference. The principal results are the following. First, the dynamics of reinfection and no-reinfection scenarios are indistinguishable before the infection peak. Second, the mitigation measures delay not only the infection peak, but also the moment when the difference between the reinfection and no-reinfection scenarios becomes prominent. These results are robust to various modeling assumptions.

COVID-19 传播的流行病学模型假设康复者具有完全受保护的免疫力。迄今为止，关于从 COVID-19 中康复的人是否会再次感染严重急性呼吸综合征冠状病毒 2 (SARS-CoV-2)，尚无明确答案。在没有关于再次感染风险的明确答案的情况下，考虑可能的情况是有启发性的。为了研究再次感染可能性的流行病学动态，我使用了具有时变传播率的易感-暴露-传染-耐药-易感模型。我考虑了三种不同的再感染建模方法。这项研究的关键特征是，我探讨了再感染和无再感染情景之间的差异，以及缓解措施如何影响这种差异。主要结果如下。首先，在感染高峰之前，再感染和无再感染情况的动态是无法区分的。其次，缓解措施不仅推迟了感染高峰，还推迟了再感染和非再感染情况之间的差异变得明显的时刻。这些结果对于各种建模假设都是稳健的。