In the absence of drugs and vaccines, policymakers use non-pharmaceutical interventions such as social distancing to decrease rates of disease-causing contact, with the aim of reducing or delaying the epidemic peak. These measures carry social and economic costs, so societies may be unable to maintain them for more than a short period of time. Intervention policy design often relies on numerical simulations of epidemic models, but comparing policies and assessing their robustness demands clear principles that apply across strategies. Here we derive the theoretically optimal strategy for using a time-limited intervention to reduce the peak prevalence of a novel disease in the classic Susceptible-Infectious-Recovered epidemic model. We show that broad classes of easier-to-implement strategies can perform nearly as well as the theoretically optimal strategy. But neither the optimal strategy nor any of these near-optimal strategies is robust to implementation error: small errors in timing the intervention produce large increases in peak prevalence. Our results reveal fundamental principles of non-pharmaceutical disease control and expose their potential fragility. For robust control, an intervention must be strong, early, and ideally sustained.

在没有药物和疫苗的情况下，政策制定者使用非药物干预措施，例如社会疏远来降低致病的接触率，目的是减少或延迟流行高峰。这些措施带来了社会和经济成本，因此社会可能无法在短时间内维持这些成本。干预策略设计通常依赖于流行病模型的数值模拟，但是比较策略并评估其健壮性需要适用于各种策略的明确原则。在这里，我们推导了一种理论上的最佳策略，该策略是使用限时干预措施来降低经典易感感染恢复流行模型中一种新型疾病的峰值流行率。我们展示了易于实施的各种策略，其性能几乎与理论上最佳的策略一样好。但是，无论是最佳策略还是任何这些近乎最佳的策略，都无法解决实施错误：干预时机的小错误会导致峰值患病率的大幅提高。我们的结果揭示了非药物疾病控制的基本原理，并揭示了其潜在的脆弱性。为了进行稳健的控制，干预措施必须强大，尽早且理想地持续进行。