临床实验室 2019 冠状病毒疾病检测压力的临床预测因子：一项分析谷歌趋势和超过 1 亿次诊断检测的跨国研究。
Clinical Predictors of SARS-CoV-2 Testing Pressure on Clinical Laboratories: A Multinational Study Analyzing Google Trends and Over 100 Million Diagnostic Tests.
机构信息
Section of Clinical Biochemistry, University of Verona, Verona, Italy.
Service of Clinical Governance, Provincial Agency for Social and Sanitary Services, Trento, Italy.
出版信息
Lab Med. 2021 Jul 1;52(4):311-314. doi: 10.1093/labmed/lmab013.
OBJECTIVE
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.
METHODS
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.
RESULTS
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.
CONCLUSION
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.
目的
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。
方法
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。
结果
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。
结论
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。
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