文章基本信息

标题：Global monitoring of the impact of the COVID-19 pandemic through online surveys sampled from the Facebook user base
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作者：Christina M. Astley ; Gaurav Tuli ; Kimberly A. Mc Cord 等
期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2021
卷号：118
期号：51
DOI：10.1073/pnas.2111455118
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：Significance The University of Maryland Global COVID Trends and Impact Survey (UMD-CTIS), launched April 2020, is the largest remote global health monitoring system. This study includes ∼30 million responses through December 2020 from all 114 countries/territories with survey weights to adjust for nonresponse and demographics. Using self-reported cross-sectional survey data sampled daily from Facebook users, we confirm consistent demographics and COVID-19 symptoms. Our global model predicts local COVID-19 case trends. Importantly, one survey item strongly correlates with reported cases, demonstrating potential utility in locales with scant UMD-CTIS sampling or government data. Despite limitations resulting from sampling, nonresponse, coverage, and measurement error, UMD-CTIS has the potential to support existing monitoring systems for COVID-19 as well as other new as-yet-undefined global health threats. Simultaneously tracking the global impact of COVID-19 is challenging because of regional variation in resources and reporting. Leveraging self-reported survey outcomes via an existing international social media network has the potential to provide standardized data streams to support monitoring and decision-making worldwide, in real time, and with limited local resources. The University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS), in partnership with Facebook, has invited daily cross-sectional samples from the social media platform's active users to participate in the survey since its launch on April 23, 2020. We analyzed UMD-CTIS survey data through December 20, 2020, from 31,142,582 responses representing 114 countries/territories weighted for nonresponse and adjusted to basic demographics. We show consistent respondent demographics over time for many countries/territories. Machine Learning models trained on national and pooled global data verified known symptom indicators. COVID-like illness (CLI) signals were correlated with government benchmark data. Importantly, the best benchmarked UMD-CTIS signal uses a single survey item whereby respondents report on CLI in their local community. In regions with strained health infrastructure but active social media users, we show it is possible to define COVID-19 impact trajectories using a remote platform independent of local government resources. This syndromic surveillance public health tool is the largest global health survey to date and, with brief participant engagement, can provide meaningful, timely insights into the global COVID-19 pandemic at a local scale.
关键词：COVID-19 surveillance; global health; human social sensing; SARS-CoV-2 testing