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Epidemiology
Original research
Predicting the local COVID-19 outbreak around the world with meteorological conditions: a model-based qualitative study
  1. Biqing Chen1,
  2. Hao Liang2,
  3. Xiaomin Yuan3,
  4. Yingying Hu2,
  5. Miao Xu2,
  6. Yating Zhao4,
  7. Binfen Zhang2,
  8. Fang Tian1,
  9. Xuejun Zhu5
  1. 1Central Laboratory/ Research Center of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
  2. 2Department of Hematology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
  3. 3Department of Colorectal Surgery, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
  4. 4School of Atmospheric Sciences, Nanjing University, Nanjing, Jiangsu, China
  5. 5Department of Hematology, Research Center of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
  1. Correspondence to Dr Xuejun Zhu; zhuxuejun{at}njucm.edu.cn

Abstract

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.

  • epidemiology
  • epidemiology
  • public health
  • infection control
  • COVID-19
http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/bmjopen-2020-041397

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    Footnotes

    • Contributors BC, YZ and XZ designed and interpreted the reported analyses and results; HL, XY, YH, BZ and MX participated in the acquisition of data; BC analysed the data, drafted and revised the manuscript; HL and XZ revised the manuscript; YZ and FT provided technical support; XZ supervised the research and revised the manuscript.

    • Funding The Priority Academic Program Development of Jiangsu Higher Education Institutions – the third period (NO. 035062002003c), the National Natural Science Foundation of China (NO. 82001206), the development program of Jiangsu Provincial Hospital of Chinese Medicine (NO. Y19066).

    • Competing interests None declared.

    • Patient consent for publication Not required.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data availability statement Data are available in a public, open access repository. Data are available upon reasonable request. Weather data and epidemiological data is all obtained from public databases. Detailed modelling results are available upon request by emailing to Biqing Chen, bq_chen@qq.com.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.