COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population
The ongoing COVID-19 pandemic has caused worldwide socioeconomic unrest, forcing governments to introduce extreme measures to reduce its spread. Being able to accurately forecast when the outbreak will hit its peak would significantly diminish the impact of the disease, as it would allow governments...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/10/11/3880 |
| _version_ | 1797566202990559232 |
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| author | Vasilis Papastefanopoulos Pantelis Linardatos Sotiris Kotsiantis |
| author_facet | Vasilis Papastefanopoulos Pantelis Linardatos Sotiris Kotsiantis |
| author_sort | Vasilis Papastefanopoulos |
| collection | DOAJ |
| description | The ongoing COVID-19 pandemic has caused worldwide socioeconomic unrest, forcing governments to introduce extreme measures to reduce its spread. Being able to accurately forecast when the outbreak will hit its peak would significantly diminish the impact of the disease, as it would allow governments to alter their policy accordingly and plan ahead for the preventive steps needed such as public health messaging, raising awareness of citizens and increasing the capacity of the health system. This study investigated the accuracy of a variety of time series modeling approaches for coronavirus outbreak detection in ten different countries with the highest number of confirmed cases as of 4 May 2020. For each of these countries, six different time series approaches were developed and compared using two publicly available datasets regarding the progression of the virus in each country and the population of each country, respectively. The results demonstrate that, given data produced using actual testing for a small portion of the population, machine learning time series methods can learn and scale to accurately estimate the percentage of the total population that will become affected in the future. |
| first_indexed | 2024-03-10T19:24:19Z |
| format | Article |
| id | doaj.art-f2cc9eafed1441b5a55722236f167b69 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T19:24:19Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f2cc9eafed1441b5a55722236f167b692023-11-20T02:46:31ZengMDPI AGApplied Sciences2076-34172020-06-011011388010.3390/app10113880COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per PopulationVasilis Papastefanopoulos0Pantelis Linardatos1Sotiris Kotsiantis2Department of Mathematics, University of Patras, 26504 Patras, GreeceDepartment of Mathematics, University of Patras, 26504 Patras, GreeceDepartment of Mathematics, University of Patras, 26504 Patras, GreeceThe ongoing COVID-19 pandemic has caused worldwide socioeconomic unrest, forcing governments to introduce extreme measures to reduce its spread. Being able to accurately forecast when the outbreak will hit its peak would significantly diminish the impact of the disease, as it would allow governments to alter their policy accordingly and plan ahead for the preventive steps needed such as public health messaging, raising awareness of citizens and increasing the capacity of the health system. This study investigated the accuracy of a variety of time series modeling approaches for coronavirus outbreak detection in ten different countries with the highest number of confirmed cases as of 4 May 2020. For each of these countries, six different time series approaches were developed and compared using two publicly available datasets regarding the progression of the virus in each country and the population of each country, respectively. The results demonstrate that, given data produced using actual testing for a small portion of the population, machine learning time series methods can learn and scale to accurately estimate the percentage of the total population that will become affected in the future.https://www.mdpi.com/2076-3417/10/11/3880pandemicCOVID-19coronavirusmachine learningstatisticstime-series |
| spellingShingle | Vasilis Papastefanopoulos Pantelis Linardatos Sotiris Kotsiantis COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population Applied Sciences pandemic COVID-19 coronavirus machine learning statistics time-series |
| title | COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population |
| title_full | COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population |
| title_fullStr | COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population |
| title_full_unstemmed | COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population |
| title_short | COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population |
| title_sort | covid 19 a comparison of time series methods to forecast percentage of active cases per population |
| topic | pandemic COVID-19 coronavirus machine learning statistics time-series |
| url | https://www.mdpi.com/2076-3417/10/11/3880 |
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