Accepted for/Published in: JMIR Public Health and Surveillance
Date Submitted: Mar 21, 2020
Open Peer Review Period: Mar 25, 2020 - Mar 25, 2020
Date Accepted: Apr 1, 2020
Date Submitted to PubMed: Apr 2, 2020
(closed for review but you can still tweet)
Predicting COVID-19 incidence using Google Trends and data mining techniques: A pilot study in Iran
ABSTRACT
Background:
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.
Objective:
This study is aimed to predict the incidence of COVID-19 in Iran.
Methods:
The data is obtained from the Google Trend website. Linear regression and long short-term memory (LSTM) models have been used to estimate the number of positive COVID-19 cases. All models are evaluated using 10-fold-cross validation, and Root Mean Square Error (RMSE) is used as the performance metric.
Results:
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.
Conclusions:
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.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.