Venkatesh U, Gandhi Periyasamy Aravind
Department of Community Medicine, Vardhman Mahavir Medical College (VMMC) and Safdarjung Hospital, New Delhi, India.
Department of Community Medicine and School of Public Health, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
Healthc Inform Res. 2020 Jul;26(3):175-184. doi: 10.4258/hir.2020.26.3.175. Epub 2020 Jul 31.
Considering the rising menace of coronavirus disease 2019 (COVID-19), it is essential to explore the methods and resources that might predict the case numbers expected and identify the locations of outbreaks. Hence, we have done the following study to explore the potential use of Google Trends (GT) in predicting the COVID-19 outbreak in India.
The Google search terms used for the analysis were "coronavirus", "COVID", "COVID 19", "corona", and "virus". GTs for these terms in Google Web, News, and YouTube, and the data on COVID-19 case numbers were obtained. Spearman correlation and lag correlation were used to determine the correlation between COVID-19 cases and the Google search terms.
"Coronavirus" and "corona" were the terms most commonly used by Internet surfers in India. Correlation for the GTs of the search terms "coronavirus" and "corona" was high (r > 0.7) with the daily cumulative and new COVID-19 cases for a lag period ranging from 9 to 21 days. The maximum lag period for predicting COVID-19 cases was found to be with the News search for the term "coronavirus", with 21 days, i.e., the search volume for "coronavirus" peaked 21 days before the peak number of cases reported by the disease surveillance system.
Our study revealed that GTs may predict outbreaks of COVID-19, 2 to 3 weeks earlier than the routine disease surveillance, in India. Google search data may be considered as a supplementary tool in COVID-19 monitoring and planning in India.
鉴于2019年冠状病毒病（COVID-19）日益严重的威胁，探索可能预测预期病例数并确定疫情爆发地点的方法和资源至关重要。因此，我们开展了以下研究，以探讨谷歌趋势（GT）在预测印度COVID-19疫情方面的潜在用途。
用于分析的谷歌搜索词为“冠状病毒”“COVID”“COVID-19”“新冠”和“病毒”。获取了这些词在谷歌网页、新闻和YouTube上的趋势数据以及COVID-19病例数数据。使用斯皮尔曼相关性和滞后相关性来确定COVID-19病例与谷歌搜索词之间的相关性。
“冠状病毒”和“新冠”是印度互联网用户最常用的搜索词。搜索词“冠状病毒”和“新冠”的趋势数据与每日累计和新增COVID-19病例在9至21天的滞后期间相关性较高（r>0.7）。发现预测COVID-19病例的最大滞后时间是新闻搜索词“冠状病毒”的，为21天，即“冠状病毒”的搜索量在疾病监测系统报告的病例数峰值前21天达到峰值。
我们的研究表明，在印度，GT可能比常规疾病监测提前2至3周预测COVID-19疫情爆发。谷歌搜索数据可被视为印度COVID-19监测和规划中的一种辅助工具。
