TW-SIR: time-window based SIR for COVID-19 forecasts
Abstract Since the outbreak of COVID-19, many COVID-19 research studies have proposed different models for predicting the trend of COVID-19. Among them, the prediction model based on mathematical epidemiology (SIR) is the most widely used, but most of these models are adapted in special situations b...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2020-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-020-80007-8 |
| _version_ | 1818851240699756544 |
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| author | Zhifang Liao Peng Lan Zhining Liao Yan Zhang Shengzong Liu |
| author_facet | Zhifang Liao Peng Lan Zhining Liao Yan Zhang Shengzong Liu |
| author_sort | Zhifang Liao |
| collection | DOAJ |
| description | Abstract Since the outbreak of COVID-19, many COVID-19 research studies have proposed different models for predicting the trend of COVID-19. Among them, the prediction model based on mathematical epidemiology (SIR) is the most widely used, but most of these models are adapted in special situations based on various assumptions. In this study, a general adapted time-window based SIR prediction model is proposed, which is characterized by introducing a time window mechanism for dynamic data analysis and using machine learning method predicts the basic reproduction number and the exponential growth rate of the epidemic. We analyzed COVID-19 data from February to July 2020 in seven countries–––China, South Korea, Italy, Spain, Brazil, Germany and France, and the numerical results showed that the framework can effectively measure the real-time changes of the parameters during the epidemic, and error rate of predicting the number of COVID-19 infections in a single day is within 5%. |
| first_indexed | 2024-12-19T07:01:53Z |
| format | Article |
| id | doaj.art-f0dce2406c424478ae7f89545f5b4922 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-19T07:01:53Z |
| publishDate | 2020-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-f0dce2406c424478ae7f89545f5b49222022-12-21T20:31:23ZengNature PortfolioScientific Reports2045-23222020-12-0110111510.1038/s41598-020-80007-8TW-SIR: time-window based SIR for COVID-19 forecastsZhifang Liao0Peng Lan1Zhining Liao2Yan Zhang3Shengzong Liu4School of Computer Science and Engineering, Central South UniversitySchool of Computer Science and Engineering, Central South UniversityNuffield Health Research Group, Nuffield HealthDepartment of Computing, School of Computing, Engineering and Built Environment, Glasgow Caledonian UniversityDepartment of Information Management, Hunan University of Finance and EconomicsAbstract Since the outbreak of COVID-19, many COVID-19 research studies have proposed different models for predicting the trend of COVID-19. Among them, the prediction model based on mathematical epidemiology (SIR) is the most widely used, but most of these models are adapted in special situations based on various assumptions. In this study, a general adapted time-window based SIR prediction model is proposed, which is characterized by introducing a time window mechanism for dynamic data analysis and using machine learning method predicts the basic reproduction number and the exponential growth rate of the epidemic. We analyzed COVID-19 data from February to July 2020 in seven countries–––China, South Korea, Italy, Spain, Brazil, Germany and France, and the numerical results showed that the framework can effectively measure the real-time changes of the parameters during the epidemic, and error rate of predicting the number of COVID-19 infections in a single day is within 5%.https://doi.org/10.1038/s41598-020-80007-8 |
| spellingShingle | Zhifang Liao Peng Lan Zhining Liao Yan Zhang Shengzong Liu TW-SIR: time-window based SIR for COVID-19 forecasts Scientific Reports |
| title | TW-SIR: time-window based SIR for COVID-19 forecasts |
| title_full | TW-SIR: time-window based SIR for COVID-19 forecasts |
| title_fullStr | TW-SIR: time-window based SIR for COVID-19 forecasts |
| title_full_unstemmed | TW-SIR: time-window based SIR for COVID-19 forecasts |
| title_short | TW-SIR: time-window based SIR for COVID-19 forecasts |
| title_sort | tw sir time window based sir for covid 19 forecasts |
| url | https://doi.org/10.1038/s41598-020-80007-8 |
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