Predicting COVID-19 Incidence Through Analysis of Google Trends Data in Iran: Data Mining and Deep Learning Pilot Study

Ayyoubzadeh, Seyed Mohammad; Ayyoubzadeh, Seyed Mehdi; Zahedi, Hoda; Ahmadi, Mahnaz; R Niakan Kalhori, Sharareh

BACKGROUND: The recent global outbreak of coronavirus disease (COVID-19) is affecting many countries worldwide. Iran is one of the top 10 most affected countries. Search engines provide useful data from populations, and these data might be useful to analyze epidemics. Utilizing data mining methods on electronic resources’ data might provide a better insight into the COVID-19 outbreak to manage the health crisis in each country and worldwide. OBJECTIVE: This study aimed to predict the incidence of COVID-19 in Iran. METHODS: Data were obtained from the Google Trends website. Linear regression and long short-term memory (LSTM) models were used to estimate the number of positive COVID-19 cases. All models were evaluated using 10-fold cross-validation, and root mean square error (RMSE) was used as the performance metric. RESULTS: The linear regression model predicted the incidence with an RMSE of 7.562 (SD 6.492). The most effective factors besides previous day incidence included the search frequency of handwashing, hand sanitizer, and antiseptic topics. The RMSE of the LSTM model was 27.187 (SD 20.705). CONCLUSIONS: Data mining algorithms can be employed to predict trends of outbreaks. This prediction might support policymakers and health care managers to plan and allocate health care resources accordingly.