WASHINGTON/SAN FRANCISCO On Dec 30, researchers using artificial intelligence systems to comb through media and social platforms detected the spread of an unusual flu-like illness in Wuhan, China.华盛顿 / 旧金山12月30日，研究人员利用人工智能系统对媒体和社交平台进行梳理，发现了一种不寻常的类似流感的疾病在中国武汉的传播。

      It would be days before the World Health Organization released a risk assessment and a full month before the UN agency declared a global public health emergency for the novel coronavirus.几天后，世界卫生组织公布了一份风险评估报告，整整一个月后，联合国宣布新型冠状病毒进入全球公共卫生紧急状态。

      Could the AI systems have accelerated the process and limited, or even arrested, the extent of the COVID-19 pandemic?人工智能系统是否可以加速这一进程，限制甚至阻止新型冠状病毒流行病的蔓延？

      Clark Freifeld, a Northeastern University computer scientist working with the global disease surveillance platform HealthMap, one of the systems detecting the outbreak, said it remains an open question.东北大学的计算机科学家 Clark Freifeld 在全球疾病监测平台 HealthMap 工作，该平台是检测疫情爆发的系统之一，他说这仍然是一个悬而未决的问题。

      “We identified the early signals, but the reality is it's hard to tell when you have an unidentified respiratory illness if it's a really serious situation，”said Freifeld.弗雷菲尔德说: “我们发现了早期信号，但现实是，当你患有不明呼吸道疾病时，很难判断这是否是一个真正严重的情况。”。

      Dataminr, a real-time risk detection technology firm, said it delivered the earliest warning about COVID-19 on December 30 based on eyewitness accounts from inside Wuhan hospitals, pictures of the disinfection of the Wuhan seafood market where the virus originated and a warning by a Chinese doctor who later died from the virus himself.实时风险检测技术公司 Dataminr 表示，根据武汉医院内部目击者的描述、病毒发源地武汉海鲜市场消毒的图片以及一名后来死于病毒的中国医生的警告，他们于12月30日发出了关于新型冠状病毒的最早警告。

      "One of our biggest challenges is we tend to be reactive in these situations, it's human nature," said Kamran Khan, founder and chief executive of the Toronto-based disease tracking firm BlueDot, one of the early systems that flashed warning flags in December over the epidemic.总部位于多伦多的疾病追踪公司 BlueDot 的创始人兼首席执行官卡姆兰 · 汗说: “我们面临的最大挑战之一就是在这种情况下我们倾向于被动应对，这是人类的天性。” BlueDot 是去年12月就艾滋病流行发出警告的早期系统之一。

      "Whenever you're dealing with a new, emerging disease, you don't have all the answers. Time is your most valuable resource; you cannot get it back."“无论何时，当你面对一种新的、正在出现的疾病时，你并不拥有所有的答案。 时间是你最宝贵的资源，你不可能把它夺回来。”

      Khan, who is also a professor of medicine and public health at the University of Toronto, told AFP by telephone the data showed "echoes of the SARS outbreak 17 years earlier, but we didn't know was how contagious this was." Khan 也是多伦多大学的医学和公共卫生教授，他通过电话告诉法新社，这些数据显示“与17年前 SARS 爆发的情况相似，但我们不知道这种情况的传染性有多强。”

      Nevertheless, AI systems have proven to be valuable in tracking epidemics by scouring a diverse array of sources ranging from airline bookings, Twitter and Weibo messages to news reports and sensors on connected devices.尽管如此，人工智能系统已经被证明在跟踪流行病方面是有价值的，它搜索了各种各样的来源，从机票预订、 Twitter 和微博消息到新闻报道和连接设备上的传感器。

      Still, Freifeld said AI systems have limits, and the big decisions must still be made by humans.尽管如此，弗雷菲尔德说人工智能系统还是有局限性的，重大决策仍然必须由人类来做出。

      "We use the AI system as a force multiplier, but we are committed to the concept of having humans in the loop," he said. 他说: “我们使用人工智能系统作为力量倍增器，但我们致力于让人类参与进来的理念。”。

      AI and machine learning systems are likely to help the battle in several ways, from tracking the outbreak itself to speeding up drug testing.人工智能和机器学习系统可能在几个方面帮助战斗，从跟踪疫情爆发本身到加快药物测试。

      "We can run simulations unlike we've ever done before, we understand biological pathways unlike we've ever understood before, and that's all because of the power of AI," said Michael Greeley of the equity firm Flare Capital Partners, which has invested in several AI medical startups. “我们可以运行前所未有的模拟，我们可以理解前所未有的生物路径，这都归功于人工智能的力量，”资本公司 Flare Capital Partners 的迈克尔 · 格里利(Michael Greeley)说。该公司已经投资了几家人工智能医疗创业公司。

      But Greeley said it remains challenging to apply these technologies to sectors like drug delivery where the normal testing time can be years. 但格里利表示，将这些技术应用于像药物输送这样的部门仍然具有挑战性，因为正常的测试时间可能为数年。

      "There is extraordinary pressure on the industry to start using these tools even though they may not be ready for prime time," he said. 他表示: “尽管这些工具可能还没有为黄金时段做好准备，但业界面临着开始使用这些工具的巨大压力。”。

      According to Khan, AI is helping in the containment phase with systems that used "anonymized" smartphone location data to track the progression of the disease and find hotspots, and to determine if people are following "social distancing" guidelines. 根据 Khan 的说法，人工智能系统使用“匿名”的智能手机位置数据来追踪疾病的进程和找到热点，并确定人们是否遵循了“社会距离”的指导方针，从而帮助控制疾病。

      Andrew Kress, CEO of the health technology firm HealthVerity, said it remains challenging to collect medical data for disease outbreaks while complying with patient privacy. 健康技术公司 HealthVerity 的首席执行官安德鲁 · 克雷斯说，在遵守病人隐私的同时收集疾病爆发的医疗数据仍然是一个挑战。

      It's possible to detect trends with signals such as pharmacy visits and sales of certain medications or even online searches, Kress said, but aggregating that has privacy implications. 克雷斯说，通过药店访问量、某些药物的销售量、甚至在线搜索等信号可以发现趋势，但聚合信息会对隐私产生影响。

      "We need to have a real discussion about balance and utility around specific use cases and potentially the right kind of research to continue to figure out new ways to leverage some of these nontraditional data sources," Kress said. 克雷斯说: “我们需要就特定用例的平衡性和实用性进行真正的讨论，并且可能需要进行正确的研究，以便继续找到利用这些非传统数据源的新方法。”。

      AI systems are also being put to work to scour the thousands of research studies for clues on what treatments might be effective. 人工智能系统也正在努力搜索数以千计的研究成果，以寻找哪些治疗方法可能有效的线索。

      Last week, researchers joined the White House in an effort to make available some 29,000 coronavirus research articles that can be scanned for data mining. 上周，研究人员与白宫一道，努力提供约29,000篇冠状病毒研究文章，以便扫描数据挖掘。

      The effort brought together the Allen Institute for AI, Chan Zuckerberg Initiative, Microsoft, Georgetown University and others. 这项工作汇集了艾伦人工智能研究所，Chan Zuckerberg Initiative，微软，乔治城大学和其他公司。

      Through Kaggle, a machine learning and data science community owned by Google, these tools will be openly available for researchers around the world. 通过 Google 旗下的机器学习和数据科学社区 Kaggle，这些工具将为世界各地的研究人员开放使用。

      "It's difficult for people to manually go through more than 20,000 articles and synthesize their findings," said Kaggle CEO and co-founder Anthony Goldbloom. Kaggle 首席执行官兼联合创始人安东尼•戈德布鲁姆说: “人们很难手工浏览超过2万篇文章并综合他们的发现。”。

      "Recent advances in technology can be helpful here. We're putting machine-readable versions of these articles in front of our community of more than four million data scientists. Our hope is that AI can be used to help find answers to a key set of questions about COVID-19." “最近的技术进步在这方面可能有所帮助。 我们正在把这些文章的机器可读版本放在我们超过四百万数据科学家的社区面前。 我们希望人工智能能够帮助我们找到一系列关于新型冠状病毒的关键问题的答案。