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...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-09-01
|
| Series: | Frontiers in Research Metrics and Analytics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frma.2022.1003972/full |
| _version_ | 1798033822462246912 |
|---|---|
| author | Lateef Babatunde Amusa Hossana Twinomurinzi Chinedu Wilfred Okonkwo |
| author_facet | Lateef Babatunde Amusa Hossana Twinomurinzi Chinedu Wilfred Okonkwo |
| author_sort | Lateef Babatunde Amusa |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-11T20:35:47Z |
| format | Article |
| id | doaj.art-10d9a50041174a499a7cc23fcd8a1c9a |
| institution | Directory Open Access Journal |
| issn | 2504-0537 |
| language | English |
| last_indexed | 2024-04-11T20:35:47Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Research Metrics and Analytics |
| spelling | doaj.art-10d9a50041174a499a7cc23fcd8a1c9a2022-12-22T04:04:22ZengFrontiers Media S.A.Frontiers in Research Metrics and Analytics2504-05372022-09-01710.3389/frma.2022.10039721003972Modeling COVID-19 incidence with Google TrendsLateef Babatunde AmusaHossana TwinomurinziChinedu Wilfred OkonkwoInfodemiologic 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.https://www.frontiersin.org/articles/10.3389/frma.2022.1003972/fullBig DataGoogle TrendsARIMACOVID-19infectious disease modeling |
| spellingShingle | Lateef Babatunde Amusa Hossana Twinomurinzi Chinedu Wilfred Okonkwo Modeling COVID-19 incidence with Google Trends Frontiers in Research Metrics and Analytics Big Data Google Trends ARIMA COVID-19 infectious disease modeling |
| 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 | Big Data Google Trends ARIMA COVID-19 infectious disease modeling |
| url | https://www.frontiersin.org/articles/10.3389/frma.2022.1003972/full |
| work_keys_str_mv | AT lateefbabatundeamusa modelingcovid19incidencewithgoogletrends AT hossanatwinomurinzi modelingcovid19incidencewithgoogletrends AT chineduwilfredokonkwo modelingcovid19incidencewithgoogletrends |