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Modeling COVID-19 incidence with Google Trends

Infodemiologic methods could be used to enhance modeling infectious diseases. It is of interest to verify the utility of these methods using a Nigerian case study. We used Google Trends data to track COVID-19 incidences and assessed whether they could complement traditional data based solely on repo...

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Autores principales: Amusa, Lateef Babatunde, Twinomurinzi, Hossana, Okonkwo, Chinedu Wilfred
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9520600/
https://www.ncbi.nlm.nih.gov/pubmed/36186843
http://dx.doi.org/10.3389/frma.2022.1003972
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author Amusa, Lateef Babatunde
Twinomurinzi, Hossana
Okonkwo, Chinedu Wilfred
author_facet Amusa, Lateef Babatunde
Twinomurinzi, Hossana
Okonkwo, Chinedu Wilfred
author_sort Amusa, Lateef Babatunde
collection PubMed
description Infodemiologic methods could be used to enhance modeling infectious diseases. It is of interest to verify the utility of these methods using a Nigerian case study. We used Google Trends data to track COVID-19 incidences and assessed whether they could complement traditional data based solely on reported case numbers. Data on the Nigerian weekly COVID-19 cases spanning through March 1, 2020, to May 31, 2021, were matched with internet search data from Google Trends. The reported weekly incidence numbers and the GT data were split into training and testing sets. ARIMA models were fitted to describe reported weekly COVID cases using the training set. Several COVID-related search terms were theoretically and empirically assessed for initial screening. The utilized Google Trends (GT) variable was added to the ARIMA model as a regressor. Model forecasts, both with and without GTD, were compared with weekly cases in the test set over 13 weeks. Forecast accuracies were compared visually and using RMSE (root mean square error) and MAE (mean average error). Statistical significance of the difference in predictions was determined with the two-sided Diebold-Mariano test. Preliminary results of contemporaneous correlations between COVID-related search terms and weekly COVID cases reveal “loss of smell,” “loss of taste,” “fever” (in order of magnitude) as significantly associated with the official cases. Predictions of the ARIMA model using solely reported case numbers resulted in an RMSE (root mean squared error) of 411.4 and mean absolute error (MAE) of 354.9. The GT expanded model achieved better forecasting accuracy (RMSE: 388.7 and MAE = 340.1). Corrected Akaike Information Criteria also favored the GT expanded model (869.4 vs. 872.2). The difference in predictive performances was significant when using a two-sided Diebold-Mariano test (DM = 6.75, p < 0.001) for the 13 weeks. Google trends data enhanced the predictive ability of a traditionally based model and should be considered a suitable method to enhance infectious disease modeling.
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spelling pubmed-95206002022-09-30 Modeling COVID-19 incidence with Google Trends Amusa, Lateef Babatunde Twinomurinzi, Hossana Okonkwo, Chinedu Wilfred Front Res Metr Anal Research Metrics and Analytics Infodemiologic methods could be used to enhance modeling infectious diseases. It is of interest to verify the utility of these methods using a Nigerian case study. We used Google Trends data to track COVID-19 incidences and assessed whether they could complement traditional data based solely on reported case numbers. Data on the Nigerian weekly COVID-19 cases spanning through March 1, 2020, to May 31, 2021, were matched with internet search data from Google Trends. The reported weekly incidence numbers and the GT data were split into training and testing sets. ARIMA models were fitted to describe reported weekly COVID cases using the training set. Several COVID-related search terms were theoretically and empirically assessed for initial screening. The utilized Google Trends (GT) variable was added to the ARIMA model as a regressor. Model forecasts, both with and without GTD, were compared with weekly cases in the test set over 13 weeks. Forecast accuracies were compared visually and using RMSE (root mean square error) and MAE (mean average error). Statistical significance of the difference in predictions was determined with the two-sided Diebold-Mariano test. Preliminary results of contemporaneous correlations between COVID-related search terms and weekly COVID cases reveal “loss of smell,” “loss of taste,” “fever” (in order of magnitude) as significantly associated with the official cases. Predictions of the ARIMA model using solely reported case numbers resulted in an RMSE (root mean squared error) of 411.4 and mean absolute error (MAE) of 354.9. The GT expanded model achieved better forecasting accuracy (RMSE: 388.7 and MAE = 340.1). Corrected Akaike Information Criteria also favored the GT expanded model (869.4 vs. 872.2). The difference in predictive performances was significant when using a two-sided Diebold-Mariano test (DM = 6.75, p < 0.001) for the 13 weeks. Google trends data enhanced the predictive ability of a traditionally based model and should be considered a suitable method to enhance infectious disease modeling. Frontiers Media S.A. 2022-09-15 /pmc/articles/PMC9520600/ /pubmed/36186843 http://dx.doi.org/10.3389/frma.2022.1003972 Text en Copyright © 2022 Amusa, Twinomurinzi and Okonkwo. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Research Metrics and Analytics
Amusa, Lateef Babatunde
Twinomurinzi, Hossana
Okonkwo, Chinedu Wilfred
Modeling COVID-19 incidence with Google Trends
title Modeling COVID-19 incidence with Google Trends
title_full Modeling COVID-19 incidence with Google Trends
title_fullStr Modeling COVID-19 incidence with Google Trends
title_full_unstemmed Modeling COVID-19 incidence with Google Trends
title_short Modeling COVID-19 incidence with Google Trends
title_sort modeling covid-19 incidence with google trends
topic Research Metrics and Analytics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9520600/
https://www.ncbi.nlm.nih.gov/pubmed/36186843
http://dx.doi.org/10.3389/frma.2022.1003972
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