Ups and downs of COVID-19: can we predict the future? Local analysis with Google Trends for forecasting the burden of COVID-19 in Pakistan

Background. We aim to study the utility of Google Trends search history data for demonstrating if a correlation may exist between web-based information and actual coronavirus disease 2019 (COVID-19) cases, as well as if such data can be used to forecast patterns of disease spikes. Patients & methods. Weekly data of COVID-19 cases in Pakistan was re¬trieved from online COVID-19 data banks for a peri¬od of 60 weeks. Search history related to COVID-19, coronavirus and the most common symptoms of dis¬ease was retrieved from Google Trends during the to analyze the correlation between the two data sets. Search terms were adjusted for time-lag over weeks, to find the highest cross-correlation for each of the search terms. Results. Search terms of ‘fever’ and ‘cough’ were the most commonly searched online, followed by corona¬virus and COVID. The highest peak correlations with the weekly case series, with a 1-week back¬log, was noted for loss of smell and loss of taste. The combined model yielded a modest perfor¬mance for forecasting positive cases. The linear regression model revealed loss of smell (adjust¬ed R2 of 0.7) with significant 1-week, 2-week and 3-week lagged time series, as the best predictor of weekly positive case counts. Conclusions. Our local analysis of Pakistan-based data seem¬ingly confirms that Google trends can be used as an important tool for anticipating and predict¬ing pandemic patterns and pre-hand prepared¬ness in such unprecedented pandemic crisis.