The outbreak of Coronavirus Disease 2019 (COVID-19) has infected and killed millions of people globally, resulting in a pandemic with enormous global impact. This disease affects the respiratory system, and the viral agent that causes it, SARS-CoV-2, spreads through droplets of saliva, as well as through coughing and sneezing. As an extremely transmissible viral infection, COVID-19 is causing significant damage to the economies of both developed and lower- and middle-income countries because of its direct impact on the health of citizens and the containment measures taken to curtail the virus. Methods to reduce or control the spread of the virus and protect the global population are needed to avoid further deaths, long-term health issues, and prolonged economic impact. The most effective approach to reduce viral spread and avoid a substantial collapse of the health system, in the absence of vaccines, is nonpharmaceutical interventions (NPI) such as enforcing social containment restrictions, monitoring overall population mobility, implementing widespread viral testing, and increasing hygiene measures. Our approach consists of combining network analytics with machine learning models by using a combination of anonymized health and telecommunications data to better understand the correlation between population movements and virus spread. This approach, called location network analysis (LNA), allows for accurate prediction of possible new outbreaks. It gives governments and health authorities a crucial tool that can help define more accurate public health metrics and can be used either to intensify social containment policies to avoid further spread or to ease them to reopen the economy. LNA can also help to retrospectively evaluate the effectiveness of policy responses to COVID-19.

2019 年冠状病毒病 (COVID-19) 的爆发已在全球范围内感染并杀死了数百万人，导致了一场具有巨大全球影响的大流行病。这种疾病会影响呼吸系统，导致它的病毒病原体 SARS-CoV-2 通过唾液飞沫以及咳嗽和打喷嚏传播。作为一种极易传播的病毒感染，COVID-19 对发达国家和中低收入国家的经济造成重大损害，因为它直接影响公民的健康以及为遏制该病毒而采取的遏制措施。需要减少或控制病毒传播并保护全球人口的方法，以避免进一步的死亡、长期的健康问题和长期的经济影响。在没有疫苗的情况下，减少病毒传播和避免卫生系统大幅崩溃的最有效方法是非药物干预 (NPI)，例如实施社会遏制限制、监测总体人口流动、实施广泛的病毒检测和加强卫生措施。我们的方法包括通过使用匿名健康和电信数据的组合将网络分析与机器学习模型相结合，以更好地了解人口流动与病毒传播之间的相关性。这种称为定位网络分析 (LNA) 的方法可以准确预测可能的新爆发。它为政府和卫生当局提供了一个关键工具，可以帮助定义更准确的公共卫生指标，并可用于加强社会遏制政策以避免进一步传播或放松政策以重新开放经济。LNA 还可以帮助回顾性评估对 COVID-19 的政策响应的有效性。