【前沿】Nature | Eva：基于AI强化学习的COVID-19检测判断模型

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1.Nature新闻摘录

Greece used AI to curb COVID: what other nations can learn
希腊采用基于强化学习的AI来加强对于COVID-19的检测，从而抑制COVID-19的发展

Governments are hungry to deploy big data in health emergencies. Scientists must help to lay the legal, ethical and logistical groundwork.

A few months into the COVID-19 pandemic, operations researcher Kimon Drakopoulos e-mailed both the Greek prime minister and the head of the country’s COVID-19 scientific task force to ask if they needed any extra advice.

Drakopoulos works in data science at the University of Southern California in Los Angeles, and is originally from Greece. To his surprise, he received a reply from Prime Minister Kyriakos Mitsotakis within hours. The European Union was asking member states, many of which had implemented widespread lockdowns in March, to allow non-essential travel to recommence from July 2020, and the Greek government needed help in deciding when and how to reopen borders.
起因：欧盟要求成员国重启非必要旅游业。

Greece, like many other countries, lacked the capacity to test all travellers, particularly those not displaying symptoms. One option was to test a sample of visitors, but Greece opted to trial an approach rooted in artificial intelligence (AI).
很多国家缺乏对于无症状患者的检测能力，希腊决定借助AI的帮助

Between August and November 2020 — with input from Drakopoulos and his colleagues — the authorities launched a system that uses a machine-learning algorithm to determine which travellers entering the country should be tested for COVID-19. The authors found machine learning to be more effective at identifying asymptomatic people than was random testing or testing based on a traveller’s country of origin. According to the researchers’ analysis, during the peak tourist season, the system detected two to four times more infected travellers than did random testing.
传统的检测方式：1、通过入境游客的来源地的风险程度判断游客是否需要进行检测；2、随机抽检。
新的检测方式：Drakopoulos团队应用机器学习算法有选择地检测入境游客，比传统方式更加高效。

The machine-learning system, which is among the first of its kind, is called Eva and is described in Nature this week (H. Bastani et al. Nature https://doi.org/10.1038/s41586-021-04014-z; 2021). It’s an example of how data analysis can contribute to effective COVID-19 policies. But it also presents challenges, from ensuring that individuals’ privacy is protected to the need to independently verify its accuracy. Moreover, Eva is a reminder of why proposals for a pandemic treaty (see Nature 594, 8; 2021) must consider rules and protocols on the proper use of AI and big data. These need to be drawn up（起草） in advance so that such analyses can be used quickly and safely in an emergency.

In many countries, travellers are chosen for COVID-19 testing at random or according to risk categories. For example, a person coming from a region with a high rate of infections might be prioritized for testing over someone travelling from a region with a lower rate.
很多国家用随机抽检或者根据来源地风险等级区分检测优先级的方法



By contrast, Eva collected not only travel history, but also demographic data such as age and sex from the passenger information forms required for entry to Greece. It then matched those characteristics with data from previously tested passengers and used the results to estimate an individual’s risk of infection. COVID-19 tests were targeted to travellers calculated to be at highest risk. The algorithm also issued tests to allow it to fill data gaps, ensuring that it remained up to date as the situation unfolded.
Eva不仅会考虑旅客的旅行史，而且会（从入境个人信息表中）提取人口学数据资料（如年龄、性别），随后将它们与先前训练的旅客信息特征匹配，由此分析出个体的感染风险等级。

data gaps：数据空缺
remain up to date：保持最新

During the pandemic, there has been no shortage of ideas on how to deploy big data and AI to improve public health or assess the pandemic’s economic impact. However, relatively few of these ideas have made it into practice. This is partly because companies and governments that hold relevant data — such as mobile-phone records or details of financial transactions — need agreed systems to be in place before they can share the data with researchers. It’s also not clear how consent can be obtained to use such personal data, or how to ensure that these data are stored safely and securely.
用AI和大数据加强公共卫生系统和评估流行病影响的想法很少付诸实施。政府、公司等手中握有相关信息的团体需要建立起认同体系后才能将数据提供给科学家。所以，关键在于如何在使用个人信息的问题上达成一种共识，如何确保这些信息能被安全、可靠地储存。

consent：同意，准许，赞同
stored：（被）存储

Eva was developed in consultation with lawyers, who ensured that the program abided by the privacy protections afforded by the EU’s General Data Protection Regulation (GDPR). Under the GDPR, organizations, such as airlines, that collect personal data need to follow security standards and obtain consent to store and use the data — and to share them with a public authority. The information collected tends to be restricted to the minimum amount required for the stated purpose.
Eva遵守欧盟《通用数据保护条例》（GDPR）。
数据收集领域倾向于将数据限制在达成某种固定目的所需的最小数据量上。

But this is not necessarily the case outside the EU. Moreover, techniques such as machine learning that use AI are limited by the quality of the available data. Researchers have revealed many instances in which algorithms that were intended to improve decision-making in areas such as medicine and criminal justice reflect and perpetuate biases that are common in society. The field needs to develop standards to indicate when data — and the algorithms that learn from them — are of sufficient quality to be used to make important decisions in an emergency. There must also be a focus on transparency about how algorithms are designed and what data are used to train them.
AI算法受限于可以允许使用的数据规模。

transparency：透明

The hunger with which Drakopoulos’s offer of help was accepted shows how eager policymakers are to improve their ability to respond in an emergency. As such algorithms become increasingly prominent and more widely accepted, it could be easy for them to slide（悄悄移动）, unnoticed（未经察觉）, into day-to-day life, or be put to nefarious use. One example is that of facial-recognition technologies which can be used to reduce criminal behaviour, but can also be abused to invade people’s privacy (see Nature 587, 354–358; 2020). Although Eva’s creators succeeded in doing what they set out to do, it’s important to remember the limitations of big data and machine learning, and to develop ways to govern such techniques so that they can be quickly — and safely — deployed.
但算法具有两面性：成为便利的工具，也可能成为罪恶的帮凶。在运用大数据进行挖掘分析时一定要谨记规则。

facial-recognition technologies:面部识别系统

Despite a wealth of methods for collecting data, many policymakers have been unable to access and harness data during the pandemic. Researchers and funders should start laying the groundwork now for emergencies of the future, developing data-sharing agreements and privacy-protection protocols in advance to improve reaction times. And discussions should also begin about setting sensible limits on how much decision-making power an algorithm should be given in a crisis.
研究者和资助者需要建立起数据分享条约和隐私保护协议，这样就可以大大缩短调用数据算法的反应时间。

lay the groundwork(for sth.)：to create or prepare the basics or essential fundation for sth.

Nature 597, 447-448 (2021)

link：https://www.nature.com/articles/d41586-021-02554-y

2.论文摘要

https://www.nature.com/articles/s41586-021-04014-z

Abstract:
Throughout the COVID-19 pandemic, countries 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 based on population-level epidemiological metrics such as cases, deaths or testing positivity rates1,2.

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 SARS-CoV-2, and to inform border policies through real-time estimates of COVID-19 prevalence.

（方法）In contrast to country-wide protocols, Eva allocated Greece’s limited testing resources based upon incoming travellers’ demographic information and testing results from previous travellers.

（方法）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 only utilize epidemiological metrics.

（结果）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.

ad-hoc：为了某种目的，特别的。源自拉丁语
counterfactual scenarios：反事实因果情景（反事实因果(Counterfactual)简介）
quarantine：隔离检疫
epidemiological metrics：流行病学指标

doi: https://doi.org/10.1038/d41586-021-02554-y