Forecasting Daily COVID-19 Case Counts Using Aggregate Mobility Statistics
The COVID-19 pandemic has impacted the whole world profoundly. For managing the pandemic, the ability to forecast daily COVID-19 case counts would bring considerable benefit to governments and policymakers. In this paper, we propose to leverage aggregate mobility statistics collected from Google’s C...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
|
| Series: | Data |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5729/7/11/166 |
| _version_ | 1797465587338706944 |
|---|---|
| author | Bulut Boru M. Emre Gursoy |
| author_facet | Bulut Boru M. Emre Gursoy |
| author_sort | Bulut Boru |
| collection | DOAJ |
| description | The COVID-19 pandemic has impacted the whole world profoundly. For managing the pandemic, the ability to forecast daily COVID-19 case counts would bring considerable benefit to governments and policymakers. In this paper, we propose to leverage aggregate mobility statistics collected from Google’s Community Mobility Reports (CMRs) toward forecasting future COVID-19 case counts. We utilize features derived from the amount of daily activity in different location categories such as transit stations versus residential areas based on the time series in CMRs, as well as historical COVID-19 daily case and test counts, in forecasting future cases. Our method trains optimized regression models for different countries based on dynamic and data-driven selection of the feature set, regression type, and time period that best fit the country under consideration. The accuracy of our method is evaluated on 13 countries with diverse characteristics. Results show that our method’s forecasts are highly accurate when compared to the real COVID-19 case counts. Furthermore, visual analysis shows that the peaks, plateaus and general trends in case counts are also correctly predicted by our method. |
| first_indexed | 2024-03-09T18:24:39Z |
| format | Article |
| id | doaj.art-9647610fb61c490f8cff60089716aec0 |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-03-09T18:24:39Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-9647610fb61c490f8cff60089716aec02023-11-24T08:03:42ZengMDPI AGData2306-57292022-11-0171116610.3390/data7110166Forecasting Daily COVID-19 Case Counts Using Aggregate Mobility StatisticsBulut Boru0M. Emre Gursoy1College of Engineering, Koc University, Rumelifeneri Yolu, Istanbul 34450, TurkeyCollege of Engineering, Koc University, Rumelifeneri Yolu, Istanbul 34450, TurkeyThe COVID-19 pandemic has impacted the whole world profoundly. For managing the pandemic, the ability to forecast daily COVID-19 case counts would bring considerable benefit to governments and policymakers. In this paper, we propose to leverage aggregate mobility statistics collected from Google’s Community Mobility Reports (CMRs) toward forecasting future COVID-19 case counts. We utilize features derived from the amount of daily activity in different location categories such as transit stations versus residential areas based on the time series in CMRs, as well as historical COVID-19 daily case and test counts, in forecasting future cases. Our method trains optimized regression models for different countries based on dynamic and data-driven selection of the feature set, regression type, and time period that best fit the country under consideration. The accuracy of our method is evaluated on 13 countries with diverse characteristics. Results show that our method’s forecasts are highly accurate when compared to the real COVID-19 case counts. Furthermore, visual analysis shows that the peaks, plateaus and general trends in case counts are also correctly predicted by our method.https://www.mdpi.com/2306-5729/7/11/166COVID-19forecastingregressionapplied machine learningdata sciencetime-series analysis |
| spellingShingle | Bulut Boru M. Emre Gursoy Forecasting Daily COVID-19 Case Counts Using Aggregate Mobility Statistics Data COVID-19 forecasting regression applied machine learning data science time-series analysis |
| title | Forecasting Daily COVID-19 Case Counts Using Aggregate Mobility Statistics |
| title_full | Forecasting Daily COVID-19 Case Counts Using Aggregate Mobility Statistics |
| title_fullStr | Forecasting Daily COVID-19 Case Counts Using Aggregate Mobility Statistics |
| title_full_unstemmed | Forecasting Daily COVID-19 Case Counts Using Aggregate Mobility Statistics |
| title_short | Forecasting Daily COVID-19 Case Counts Using Aggregate Mobility Statistics |
| title_sort | forecasting daily covid 19 case counts using aggregate mobility statistics |
| topic | COVID-19 forecasting regression applied machine learning data science time-series analysis |
| url | https://www.mdpi.com/2306-5729/7/11/166 |
| work_keys_str_mv | AT bulutboru forecastingdailycovid19casecountsusingaggregatemobilitystatistics AT memregursoy forecastingdailycovid19casecountsusingaggregatemobilitystatistics |