Throughout the coronavirus disease 2019 (COVID-19) pandemic, countries have relied on a variety of ad hoc border control protocols to allow for non-essential travel while safeguarding public health, from quarantining all travellers to restricting entry from select nations on the basis of population-level epidemiological metrics such as cases, deaths or testing positivity rates^1,2. Here we report the design and performance of a reinforcement learning system, nicknamed Eva. In the summer of 2020, Eva was deployed across all Greek borders to limit the influx of asymptomatic travellers infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and to inform border policies through real-time estimates of COVID-19 prevalence. In contrast to country-wide protocols, Eva allocated Greece’s limited testing resources on the basis of incoming travellers’ demographic information and testing results from previous travellers. By comparing Eva’s performance against modelled counterfactual scenarios, we show that Eva identified 1.85 times as many asymptomatic, infected travellers as random surveillance testing, with up to 2–4 times as many during peak travel, and 1.25–1.45 times as many asymptomatic, infected travellers as testing policies that utilize only epidemiological metrics. We demonstrate that this latter benefit arises, at least partially, because population-level epidemiological metrics had limited predictive value for the actual prevalence of SARS-CoV-2 among asymptomatic travellers and exhibited strong country-specific idiosyncrasies in the summer of 2020. Our results raise serious concerns on the effectiveness of country-agnostic internationally proposed border control policies³ that are based on population-level epidemiological metrics. Instead, our work represents a successful example of the potential of reinforcement learning and real-time data for safeguarding public health.

在 2019 年冠状病毒病 (COVID-19) 大流行期间，各国依靠各种临时边境控制协议来允许非必要的旅行，同时保障公众健康，从隔离所有旅行者到根据特定国家限制入境人口水平的流行病学指标，例如病例、死亡或检测阳性率^1,2. 在这里，我们报告了一个名为 Eva 的强化学习系统的设计和性能。2020 年夏天，Eva 被部署到希腊所有边境，以限制感染严重急性呼吸系统综合症冠状病毒 2 (SARS-CoV-2) 的无症状旅行者的涌入，并通过对 COVID-19 的实时估计来告知边境政策流行。与全国范围的协议相比，Eva 根据入境旅客的人口统计信息和以前旅客的测试结果分配了希腊有限的测试资源。通过将 Eva 的表现与模拟的反事实场景进行比较，我们表明 Eva 识别出的无症状感染旅行者是随机监测测试的 1.85 倍，在旅行高峰期高达 2-4 倍，无症状旅行者高达 1.25-1.45 倍，受感染的旅行者作为仅使用流行病学指标的测试策略。我们证明，后一种好处的出现至少部分是因为人口水平的流行病学指标对 SARS-CoV-2 在无症状旅行者中的实际流行率的预测价值有限，并且在 2020 年夏季表现出强烈的国家特定性。我们的结果对与国家无关的国际提议的边境管制政策的有效性提出严重关切³基于人口水平的流行病学指标。相反，我们的工作代表了强化学习和实时数据在保护公共健康方面的潜力的成功范例。