Predicting COVID-19 incidence using Google Trends and data mining techniques: A pilot study in Iran

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Predicting COVID-19 incidence using Google Trends and data mining techniques: A pilot study in Iran

Predicting COVID-19 incidence using Google Trends and data mining techniques: A pilot study in Iran

This study is aimed to predict the incidence of COVID-19 in Iran using data mining methods obtained from Google.

COVID-19 is a recent global outbreak affecting 186 countries around the world. Iran is one of the ten most affected countries. Search engines provide useful data from populations and this data might be useful to analyze epidemics. Using data mining methods for available data might give better insight to manage the health crisis of coronavirus outbreak for each country and the world.

This study is aimed to predict the incidence of COVID-19 in Iran using data mining methods obtained from Google.

Data were obtained from the Google Trend website. Linear regression and long short-term memory (LSTM) models were used to estimate the number of positive COVID-19 cases.

The Linear Regression model predicts the incidence with RMSE of 7.562 ± 6.492. The most effective factors are the frequency of searches of handwashing, hand sanitizer, antiseptic topics, and previous day incidence. The RMSE of LSTM model was equal to 28.487.

The data mining algorithms can be employed to predict outbreak spreading trends. This prediction might support policymakers and healthcare managers to plan and allocate healthcare resources accordingly.

|2020-04-03T13:32:31-04:00April 3rd, 2020|COVID-19 Literature|Comments Off on Predicting COVID-19 incidence using Google Trends and data mining techniques: A pilot study in Iran

About the Author: Erika Cheng

Erika Cheng

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