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Volume 3 Issue 3
Jul.  2023
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Article Navigation >  Frigid Zone Medicine  > 2023  >  3(3): 161-166
Yulu Zheng, Zheng Guo, Zhiyuan Wu, Jun Wen, Haifeng Hou. Comparisons of different statistical models for analyzing the effects of meteorological factors on COVID-19[J]. Frigid Zone Medicine, 2023, 3(3): 161-166. doi: 10.2478/fzm-2023-0020
Citation: Yulu Zheng, Zheng Guo, Zhiyuan Wu, Jun Wen, Haifeng Hou. Comparisons of different statistical models for analyzing the effects of meteorological factors on COVID-19[J]. Frigid Zone Medicine, 2023, 3(3): 161-166. doi: 10.2478/fzm-2023-0020
shu

Comparisons of different statistical models for analyzing the effects of meteorological factors on COVID-19

doi: 10.2478/fzm-2023-0020
  • 1.

    Centre for Precision Health, Edith Cowan University, Joondalup 6027, Australia

  • 2.

    Beijing Municipal Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing 100069, China

  • 3.

    School of Business and Law, Edith Cowan University, Joondalup 6027, Australia

  • 4.

    School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan 250117, China

  • 5.

    School of Medical and Health Sciences, Edith Cowan University, Joondalup 6027, Australia

Funds:

the National Natural Science Foundation of China 8177120753

the China-Australia International Collaborative Grant NHMRC APP1112767

the China-Australia International Collaborative Grant NSFC 81561128020

Zheng Y L and Guo Z were supported by the Edith Cowan University Higher Degree by Research Scholarship ECU-HDR ST10469322

Zheng Y L and Guo Z were supported by the Edith Cowan University Higher Degree by Research Scholarship ST10468211

More Information
  • Corresponding author: Jun Wen, E-mail address: j.wen@ecu.edu.au; Haifeng Hou, E-mail address: hfhou@sdfmu.edu.cn
  • Received Date: 2022-10-19
  • Accepted Date: 2023-04-03
    • Available Online: 2023-07-25
    Abstract
  •   Objective   This general non-systematic review aimed to gather information on reported statistical models examing the effects of meteorological factors on coronavirus disease 2019 (COVID-19) and compare these models.   Methods   PubMed, Web of Science, and Google Scholar were searched for studies on "meteorological factors and COVID-19" published between January 1, 2020, and October 1, 2022.   Results   The most commonly used approaches for analyzing the association between meteorological factors and COVID-19 were the linear regression model (LRM), generalized linear model (GLM), generalized additive model (GAM), and distributed lag non-linear model (DLNM). In addition to these classical models commonly applied in environmental epidemiology, machine learning techniques are increasingly being used to select risk factors for the outcome of interest and establishing robust prediction models.   Conclusion   Selecting an appropriate model is essential before conducting research. To ensure the reliability of analysis results, it is important to consider including non-meteorological factors (e.g., government policies on physical distancing, vaccination, and hygiene practices) along with meteorological factors in the model.

     

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