Predicting Risks of a COVID-19 Outbreak by Using Outdoor Air Pollution Indicators and Population Flow with Queuing Theory
COVID-19 has been widespread in all countries since it was first discovered in December 2019. The high infectivity of COVID-19 is primarily transmitted between people via respiratory droplets on contact routes, which makes it more difficult to prevent it. Air quality has been considered to be highly...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/13/10/1727 |
| _version_ | 1797475338204217344 |
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| author | Yi-Fang Chiang Ka-Ui Chu Ling-Jyh Chen Yao-Hua Ho |
| author_facet | Yi-Fang Chiang Ka-Ui Chu Ling-Jyh Chen Yao-Hua Ho |
| author_sort | Yi-Fang Chiang |
| collection | DOAJ |
| description | COVID-19 has been widespread in all countries since it was first discovered in December 2019. The high infectivity of COVID-19 is primarily transmitted between people via respiratory droplets on contact routes, which makes it more difficult to prevent it. Air quality has been considered to be highly correlated with respiratory diseases. In addition, population movement increases contact routes, which increases the risk of COVID-19 outbreaks. For epidemic prevention, the government’s strategies are also one of the factors that affect the risk of outbreaks, including whether it is mandatory to wear masks, stay-at-home orders, or vaccination. Wearing masks can reduce the risk of droplet infection, while stay-at-home orders can reduce contact between people. In this study, the number of COVID-19 confirmed cases and active cases of COVID-19 will be estimated according to the population movement, outdoor air pollution, and vaccination rates. Using the estimated results, the average recovery time will be predicted by Queuing Theory. The predicted average recovery time will be brought into risk analysis to estimate the possible high-risk periods. We compare the estimated high-risk periods with epidemic-prevention measures to provide a reference to evaluate the epidemic prevention plans enforced by relevant government agencies to achieve an improved control measure over the epidemic situation. |
| first_indexed | 2024-03-09T20:42:45Z |
| format | Article |
| id | doaj.art-666f8ad802224b1d9d9bdadec4a38b2d |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-09T20:42:45Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-666f8ad802224b1d9d9bdadec4a38b2d2023-11-23T22:52:49ZengMDPI AGAtmosphere2073-44332022-10-011310172710.3390/atmos13101727Predicting Risks of a COVID-19 Outbreak by Using Outdoor Air Pollution Indicators and Population Flow with Queuing TheoryYi-Fang Chiang0Ka-Ui Chu1Ling-Jyh Chen2Yao-Hua Ho3Department of Computer Science and Information Engineering, National Taiwan Normal University, Taipei 11677, TaiwanDepartment of Computer Science and Information Engineering, National Taiwan Normal University, Taipei 11677, TaiwanDepartment of Computer Science and Information Engineering, National Taiwan Normal University, Taipei 11677, TaiwanDepartment of Computer Science and Information Engineering, National Taiwan Normal University, Taipei 11677, TaiwanCOVID-19 has been widespread in all countries since it was first discovered in December 2019. The high infectivity of COVID-19 is primarily transmitted between people via respiratory droplets on contact routes, which makes it more difficult to prevent it. Air quality has been considered to be highly correlated with respiratory diseases. In addition, population movement increases contact routes, which increases the risk of COVID-19 outbreaks. For epidemic prevention, the government’s strategies are also one of the factors that affect the risk of outbreaks, including whether it is mandatory to wear masks, stay-at-home orders, or vaccination. Wearing masks can reduce the risk of droplet infection, while stay-at-home orders can reduce contact between people. In this study, the number of COVID-19 confirmed cases and active cases of COVID-19 will be estimated according to the population movement, outdoor air pollution, and vaccination rates. Using the estimated results, the average recovery time will be predicted by Queuing Theory. The predicted average recovery time will be brought into risk analysis to estimate the possible high-risk periods. We compare the estimated high-risk periods with epidemic-prevention measures to provide a reference to evaluate the epidemic prevention plans enforced by relevant government agencies to achieve an improved control measure over the epidemic situation.https://www.mdpi.com/2073-4433/13/10/1727coronavirusCOVID-19risk predictionoutbreak controlmachine learningartificial (recurrent) neural network |
| spellingShingle | Yi-Fang Chiang Ka-Ui Chu Ling-Jyh Chen Yao-Hua Ho Predicting Risks of a COVID-19 Outbreak by Using Outdoor Air Pollution Indicators and Population Flow with Queuing Theory Atmosphere coronavirus COVID-19 risk prediction outbreak control machine learning artificial (recurrent) neural network |
| title | Predicting Risks of a COVID-19 Outbreak by Using Outdoor Air Pollution Indicators and Population Flow with Queuing Theory |
| title_full | Predicting Risks of a COVID-19 Outbreak by Using Outdoor Air Pollution Indicators and Population Flow with Queuing Theory |
| title_fullStr | Predicting Risks of a COVID-19 Outbreak by Using Outdoor Air Pollution Indicators and Population Flow with Queuing Theory |
| title_full_unstemmed | Predicting Risks of a COVID-19 Outbreak by Using Outdoor Air Pollution Indicators and Population Flow with Queuing Theory |
| title_short | Predicting Risks of a COVID-19 Outbreak by Using Outdoor Air Pollution Indicators and Population Flow with Queuing Theory |
| title_sort | predicting risks of a covid 19 outbreak by using outdoor air pollution indicators and population flow with queuing theory |
| topic | coronavirus COVID-19 risk prediction outbreak control machine learning artificial (recurrent) neural network |
| url | https://www.mdpi.com/2073-4433/13/10/1727 |
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