An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time.
An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time.
复制标题
一种通过多个数字痕迹以接近真实的时间监测COVID-19活动的预警方法。
DOI：
10.1126/sciadv.abd6989
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发表时间：
2021-03
期刊：
影响因子：
13.6
通讯作者：
Santillana M
中科院分区：
文献类型：
--
作者：
Kogan NE;Clemente L;Liautaud P;Kaashoek J;Link NB;Nguyen AT;Lu FS;Huybers P;Resch B;Havas C;Petutschnig A;Davis J;Chinazzi M;Mustafa B;Hanage WP;Vespignani A;Santillana M
多个数字数据流比传统流行病学监测提前数周预测 COVID-19 活动。鉴于 2019 年冠状病毒病 (COVID-19) 的易感性仍然很高，而且控制传播策略不一致，美国各地继续出现疫情爆发。在有效的疫苗得到广泛部署之前，遏制 COVID-19 将需要精心安排时间的非药物干预措施 (NPI)。 COVID-19 预警系统对此至关重要。在这里，我们评估数字数据流作为2020年3月1日至9月30日州级COVID-19活动的早期指标。我们观察到，数字数据流活动的增加预计确诊病例和死亡人数将增加2至3周。根据电话衍生的匿名人员流动数据衡量，在 NPI 实施后 2 至 4 周，确诊病例和死亡人数也有所减少。我们提出了一种协调这些数据流的方法，以识别未来的 COVID-19 爆发。我们的结果表明，结合不同的健康和行为数据可能有助于在使用传统流行病学监测进行观察前几周确定疾病活动变化。
Multiple digital data streams forecast COVID-19 activity weeks before traditional epidemiological surveillance. Given still-high levels of coronavirus disease 2019 (COVID-19) susceptibility and inconsistent transmission-containing strategies, outbreaks have continued to emerge across the United States. Until effective vaccines are widely deployed, curbing COVID-19 will require carefully timed nonpharmaceutical interventions (NPIs). A COVID-19 early warning system is vital for this. Here, we evaluate digital data streams as early indicators of state-level COVID-19 activity from 1 March to 30 September 2020. We observe that increases in digital data stream activity anticipate increases in confirmed cases and deaths by 2 to 3 weeks. Confirmed cases and deaths also decrease 2 to 4 weeks after NPI implementation, as measured by anonymized, phone-derived human mobility data. We propose a means of harmonizing these data streams to identify future COVID-19 outbreaks. Our results suggest that combining disparate health and behavioral data may help identify disease activity changes weeks before observation using traditional epidemiological monitoring.
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