Article ; Online: Predicting COVID-19 new cases in California with Google Trends data and a machine learning approach.
Informatics for health & social care
2024 Volume 49, Issue 1, Page(s) 56–72
Abstract: Background: Google Trends data can be a valuable source of information for health-related issues such as predicting infectious disease trends.: Objectives: To evaluate the accuracy of predicting COVID-19 new cases in California using Google Trends ... ...
| Abstract | Background: Google Trends data can be a valuable source of information for health-related issues such as predicting infectious disease trends. Objectives: To evaluate the accuracy of predicting COVID-19 new cases in California using Google Trends data, we develop and use a GMDH-type neural network model and compare its performance with a LTSM model. Methods: We predicted COVID-19 new cases using Google query data over three periods. Our first period covered March 1, 2020, to July 31, 2020, including the first peak of infection. We also estimated a model from October 1, 2020, to January 7, 2021, including the second wave of COVID-19 and avoiding possible biases from public interest in searching about the new pandemic. In addition, we extended our forecasting period from May 20, 2020, to January 31, 2021, to cover an extended period of time. Results: Our findings show that Google relative search volume (RSV) can be used to accurately predict new COVID-19 cases. We find that among our Google relative search volume terms, "Fever," "COVID Testing," "Signs of COVID," "COVID Treatment," and "Shortness of Breath" increase model predictive accuracy. Conclusions: Our findings highlight the value of using data sources providing |
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| MeSH term(s) | Humans ; California/epidemiology ; COVID-19/epidemiology ; COVID-19 Testing ; Machine Learning ; Search Engine |
| Language | English |
| Publishing date | 2024-02-14 |
| Publishing country | England |
| Document type | Journal Article |
| ZDB-ID | 2431238-1 |
| ISSN | 1753-8165 ; 1753-8157 |
| ISSN (online) | 1753-8165 |
| ISSN | 1753-8157 |
| DOI | 10.1080/17538157.2024.2315246 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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