Predicting the local COVID-19 outbreak around the world with meteorological conditions : a model-based qualitative study
© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ..
OBJECTIVES: This study aims to investigate the relationship between daily weather and transmission rate of SARS-CoV-2, and to develop a generalised model for future prediction of the COVID-19 spreading rate for a certain area with meteorological factors.
DESIGN: A retrospective, qualitative study.
METHODS AND ANALYSIS: We collected 382 596 records of weather data with four meteorological factors, namely, average temperature, relative humidity, wind speed, and air visibility, and 15 192 records of epidemic data with daily new confirmed case counts (1 587 209 confirmed cases in total) in nearly 500 areas worldwide from 20 January 2020 to 9 April 2020. Epidemic data were modelled against weather data to find a model that could best predict the future outbreak.
RESULTS: Significant correlation of the daily new confirmed case count with the weather 3 to 7 days ago were found. SARS-CoV-2 is easy to spread under weather conditions of average temperature at 5 to 15°C, relative humidity at 70% to 80%, wind speed at 1.5 to 4.5 m/s and air visibility less than 10 statute miles. A short-term model with these four meteorological variables was derived to predict the daily increase in COVID-19 cases; and a long-term model using temperature to predict the pandemic in the next week to month was derived. Taken China as a discovery dataset, it was well validated with worldwide data. According to this model, there are five viral transmission patterns, 'restricted', 'controlled', 'natural', 'tropical' and 'southern'. This model's prediction performance correlates with actual observations best (over 0.9 correlation coefficient) under natural spread mode of SARS-CoV-2 when there is not much human interference such as epidemic control.
CONCLUSIONS: This model can be used for prediction of the future outbreak, and illustrating the effect of epidemic control for a certain area.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
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| Enthalten in: |
BMJ open - 10(2020), 11 vom: 16. Nov., Seite e041397 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Chen, Biqing [VerfasserIn] |
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| Links: |
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| Themen: |
COVID-19 |
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| Anmerkungen: |
Date Completed 04.12.2020 Date Revised 30.03.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1136/bmjopen-2020-041397 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 245 | 1 | 0 | |a Predicting the local COVID-19 outbreak around the world with meteorological conditions |b a model-based qualitative study |
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| 520 | |a © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. | ||
| 520 | |a OBJECTIVES: This study aims to investigate the relationship between daily weather and transmission rate of SARS-CoV-2, and to develop a generalised model for future prediction of the COVID-19 spreading rate for a certain area with meteorological factors | ||
| 520 | |a DESIGN: A retrospective, qualitative study | ||
| 520 | |a METHODS AND ANALYSIS: We collected 382 596 records of weather data with four meteorological factors, namely, average temperature, relative humidity, wind speed, and air visibility, and 15 192 records of epidemic data with daily new confirmed case counts (1 587 209 confirmed cases in total) in nearly 500 areas worldwide from 20 January 2020 to 9 April 2020. Epidemic data were modelled against weather data to find a model that could best predict the future outbreak | ||
| 520 | |a RESULTS: Significant correlation of the daily new confirmed case count with the weather 3 to 7 days ago were found. SARS-CoV-2 is easy to spread under weather conditions of average temperature at 5 to 15°C, relative humidity at 70% to 80%, wind speed at 1.5 to 4.5 m/s and air visibility less than 10 statute miles. A short-term model with these four meteorological variables was derived to predict the daily increase in COVID-19 cases; and a long-term model using temperature to predict the pandemic in the next week to month was derived. Taken China as a discovery dataset, it was well validated with worldwide data. According to this model, there are five viral transmission patterns, 'restricted', 'controlled', 'natural', 'tropical' and 'southern'. This model's prediction performance correlates with actual observations best (over 0.9 correlation coefficient) under natural spread mode of SARS-CoV-2 when there is not much human interference such as epidemic control | ||
| 520 | |a CONCLUSIONS: This model can be used for prediction of the future outbreak, and illustrating the effect of epidemic control for a certain area | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Multicenter Study | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a epidemiology | |
| 650 | 4 | |a infection control | |
| 650 | 4 | |a public health | |
| 700 | 1 | |a Liang, Hao |e verfasserin |4 aut | |
| 700 | 1 | |a Yuan, Xiaomin |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Yingying |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Miao |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Yating |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Binfen |e verfasserin |4 aut | |
| 700 | 1 | |a Tian, Fang |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, Xuejun |e verfasserin |4 aut | |
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