Section of Clinical Biochemistry, University of Verona, Verona, Italy.
Service of Clinical Governance, Provincial Agency for Social and Sanitary Services, Trento, Italy.
Lab Med. 2021 Jul 1;52(4):311-314. doi: 10.1093/labmed/lmab013.
Evidence has shown that Google searches for clinical symptom keywords correlates with the number of new weekly patients with COVID-19. This multinational study assessed whether demand for SARS-CoV-2 tests could also be predicted by Google searches for key COVID-19 symptoms.
The weekly number of SARS-CoV-2 tests performed in Italy and the United States was retrieved from official sources. A concomitant electronic search was performed in Google Trends, using terms for key COVID-19 symptoms.
The model that provided the highest coefficient of determination for the United States (R2 = 82.8%) included a combination of searching for cough (with a time lag of 2 weeks), fever (with a time lag of 2 weeks), and headache (with a time lag of 3 weeks; the time lag refers to the amount of time between when a search was conducted and when a test was administered). In Italy, headache provided the model with the highest adjusted R2 (86.8%), with time lags of both 1 and 2 weeks.
Weekly monitoring of Google Trends scores for nonspecific COVID-19 symptoms is a reliable approach for anticipating SARS-CoV-2 testing demands ~2 weeks in the future.
有证据表明，针对临床症状关键词的谷歌搜索量与每周新增 COVID-19 患者人数相关。本项多国研究评估了针对关键 COVID-19 症状的谷歌搜索是否也可预测对 SARS-CoV-2 检测的需求。
从官方来源获取意大利和美国每周进行的 SARS-CoV-2 检测数量。同时在谷歌趋势中进行了针对关键 COVID-19 症状的电子搜索。
为美国提供最高决定系数的模型（R2 = 82.8%）包括咳嗽（时间滞后 2 周）、发热（时间滞后 2 周）和头痛（时间滞后 3 周）的搜索组合（时间滞后是指进行搜索与进行检测之间的时间量）。在意大利，头痛为模型提供了最高的调整 R2（86.8%），时间滞后为 1 周和 2 周。
对非特异性 COVID-19 症状的谷歌趋势评分进行每周监测是一种可靠的方法，可在未来约 2 周预测 SARS-CoV-2 检测需求。
