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Accepted for/Published in: JMIR Public Health and Surveillance

Date Submitted: Jun 8, 2020
Open Peer Review Period: Jun 8, 2020 - Jun 15, 2020
Date Accepted: Oct 24, 2020
Date Submitted to PubMed: Nov 24, 2020
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

The Influence of Average Temperature and Relative Humidity on New Cases of COVID-19: Time-Series Analysis

He Z, Chin Y, Yu S, Huang J, Zhang CJ, Zhu K, Azarakhsh N, Sheng J, He Y, Jayavanth P, Akinwunmi BO, Ming WK

The Influence of Average Temperature and Relative Humidity on New Cases of COVID-19: Time-Series Analysis

JMIR Public Health Surveill 2021;7(1):e20495

DOI: 10.2196/20495

PMID: 33232262

PMCID: 7836910

Average temperature and relative humidity and domestic new cases of coronavirus disease (COVID-19) in nine Asian cities: A time-series analysis

  • Zonglin He; 
  • Yiqiao Chin; 
  • Shinning Yu; 
  • Jian Huang; 
  • Casper JP Zhang; 
  • Ke Zhu; 
  • Nima Azarakhsh; 
  • Jie Sheng; 
  • Yi He; 
  • Pallavi Jayavanth; 
  • Babatunde O Akinwunmi; 
  • Wai-Kit Ming

ABSTRACT

Background:

The influence of meteorological factors on the transmission and spread of COVID-19 is of interest and not been elucidated.

Objective:

To investigate the associations of meteorological factors and the daily new cases of coronavirus disease (COVID-19) in nine Asian cities.

Methods:

Pearson’s correlation and generalized additive modeling were performed to assess the relationships between daily new COVID-19 cases and meteorological factors (daily average temperature and relative humidity) with the most updated data currently available.

Results:

The Pearson correlation showed that daily new confirmed cases of COVID-19 were more correlated with the average temperature than with relative humidity. Daily new confirmed cases were negatively correlated with the average temperature in Beijing (r=.565, P<.01), Shanghai (r=-.471, P<.01), and Guangzhou (r=-.530, P<.01) , yet in contrast, positively correlated with that in Japan (r=.441, P<.01). In most of the cities (Shanghai, Guangzhou, Hong Kong, Seoul, Tokyo, and Kuala Lumpur), generalized additive modeling analysis showed the number of daily new confirmed cases was positively associated with both average temperature and relative humidity, especially in lagged 3d model, where a positive influence of temperature on the daily new confirmed cases was discerned in 5 cities except in Beijing, Wuhan, Korea, and Malaysia. Moreover, sensitivity analysis by incorporating the city grade and public health measures into the model showed that high temperature can increase the daily new cases by 8% (OR=1.08, 95% confidence interval=1.06-1.09) in lagged 3d model.

Conclusions:

With increased temperature, the daily new cases of COVID-19 increases. Large-scale public health measures and expanded regional research are still required until a vaccine becomes available and herd immunity is established.


 Citation

Please cite as:

He Z, Chin Y, Yu S, Huang J, Zhang CJ, Zhu K, Azarakhsh N, Sheng J, He Y, Jayavanth P, Akinwunmi BO, Ming WK

The Influence of Average Temperature and Relative Humidity on New Cases of COVID-19: Time-Series Analysis

JMIR Public Health Surveill 2021;7(1):e20495

DOI: 10.2196/20495

PMID: 33232262

PMCID: 7836910

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