Assessment of relationship between Google Trend search data on clinical symptoms and cases reported during the first wave of COVID-19 outbreak in India

Infodemiology and infoveillance approaches have been extensively used in recent years to support traditional epidemiology and disease surveillance. Hence, the present study aimed to explore the association between Google Trends (GTs) search of clinical symptoms and cases reported during the first wave of COVID-19. The GT data from January 30, 2020, to September 30, 2020, and daily COVID-19 cases in India and a few selected states were collected from the Ministry of Health and Family Welfare, Government of India. Correlation analysis was performed between the GT index values and the number of confirmed cases. Followed by, the COVID-19 cases were predicted using Bayesian regression and classical linear regression models. A strong association was observed between the search index of clinical symptoms and reported COVID-19 cases (cold: R=0.41, headache: R=0.46, fever: R=0.66, loss of taste: R=0.78, loss of smell R=0.86) across India. Similarly, lagged correlations were also observed (loss of smell, loss of taste, loss of taste and loss of smell, fever and headache show 3, 9, 1, 9, and 13 days lag periods respectively). Besides this, the Bayesian regression model was outperformed (MAE: 0.331164, RMSE: 0.411087) for predicting the COVID-19 cases in India and regionally than the frequentist linear regression (MAE: 0.33134, RMSE: 0.411316). The study helps health authorities better prepare and planning of health care facility timely to avoid adverse impacts. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement The authors are grateful to the Director of the Council of Scientific and Industrial Research-Indian Institute of Chemical Technology, Hyderabad, for his encouragement and support. The present work is supported by the DST (Department of Science and Technology) under Epidemiology Data Analytics (EDA) of Interdisciplinary cyber- physical systems (ICPS) programme (Grant number: DST/ICPS/EDA/2018), Govt. of India and the Ministry of Environment, Forest & Climate Change (MoEF & CC), Government of India for funding the project Environmental Information Awareness Capacity Building and Livelihood Programme (EIACP: Resource Partner on Climate Change and Public Health). The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript. CSIR-IICT communication number of the article is IICT/Pubs./2021/074. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors