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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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    • References(28)
  • [1]
    Wang Y, Hou H, Wang W. Combination of CT and RT-PCR in the screening or diagnosis of COVID-19. J Glob Health, 2020: 10(1): 010347. doi: 10.7189/jogh.10.010347
    [2]
    World Health Organization. Coronavirus (COVID-19) Dashboard. Available from: https://covid19.who.int/. Accessed on 07 October 2022.
    [3]
    Wang W, Yan Y, Guo Z, et al. All around suboptimal health—a joint position paper of the suboptimal health study consortium and European association for predictive, preventive and personalised medicine. EPMA J, 2021: 12(4): 403–433. doi: 10.1007/s13167-021-00253-2
    [4]
    Wang W. Cardiovascular health in China: low level vs high diversity. The Lancet Regional Health–Western Pacific, 2020: 3: 100038. doi: 10.1016/j.lanwpc.2020.100038
    [5]
    Hou H, Yang H, Liu P, et al. Profile of immunoglobulin G N-glycome in COVID-19 patients: a case-control study. Front Immunol, 2021: 12: 748566. doi: 10.3389/fimmu.2021.748566
    [6]
    Dowell S F, Ho M S. Seasonality of infectious diseases and severe acute respiratory syndrome–what we don't know can hurt us. Lancet Infect Dis, 2004: 4(11): 704–708. doi: 10.1016/S1473-3099(04)01177-6
    [7]
    Yuan J, Yun H, Lan W, et al. A climatologic investigation of the SARS-CoV outbreak in Beijing, China. Am J Infect Control, 2006: 34(4): 234–236. doi: 10.1016/j.ajic.2005.12.006
    [8]
    Loché Fernández-Ahúja J M, Fernández Martínez J L. Effects of climate variables on the COVID-19 outbreak in Spain. Int J Hyg Environ Health, 2021: 234: 113723. doi: 10.1016/j.ijheh.2021.113723
    [9]
    Pan J, Yao Y, Liu Z, et al. Warmer weather unlikely to reduce the COVID-19 transmission: An ecological study in 202 locations in 8 countries. Sci Total Environ, 2021: 753: 142272. doi: 10.1016/j.scitotenv.2020.142272
    [10]
    Hamdan M, Dabbour L, Abdelhafez E. Study of climatology parameters on COVID-19 outbreak in Jordan. Environ Earth Sci, 2022: 81(8): 228. doi: 10.1007/s12665-022-10348-2
    [11]
    Mehmood K, Bao Y, Abrar M M, et al. Spatiotemporal variability of COVID-19 pandemic in relation to air pollution, climate and socioeconomic factors in Pakistan. Chemosphere, 2021: 271: 129584. doi: 10.1016/j.chemosphere.2021.129584
    [12]
    Moazeni M, Maracy M R, Dehdashti B, et al. Spatiotemporal analysis of COVID-19, air pollution, climate, and meteorological conditions in a metropolitan region of Iran. Environ Sci Pollut Res Int, 2022: 29(17): 24911–24924. doi: 10.1007/s11356-021-17535-x
    [13]
    Nottmeyer L N, Sera F. Influence of temperature, and of relative and absolute humidity on COVID-19 incidence in England - A multi-city time-series study. Environ Res, 2021: 196: 110977. doi: 10.1016/j.envres.2021.110977
    [14]
    Liu Y, Lin X, Qin S. The short-term seasonal analyses between atmospheric environment and covid-19 in epidemic areas of cities in Australia, South Korea, and Italy. preprint arXiv: 2022: 194(3): 195.
    [15]
    Nevels M, Si X, Bambrick H, et al. Weather variability and transmissibility of COVID-19: a time series analysis based on effective reproductive number. Exp Results, 2021: 2: e15. doi: 10.1017/exp.2021.4
    [16]
    Liu M, Li Z, Liu M, et al. Association between temperature and COVID-19 transmission in 153 countries. Environ Sci Pollut Res Int, 2022: 29(11): 16017–16027. doi: 10.1007/s11356-021-16666-5
    [17]
    Zhang X, Maggioni V, Houser P, et al. The impact of weather condition and social activity on COVID-19 transmission in the United States. J Environ Manage, 2022: 302(Pt B): 114085.
    [18]
    Han Y, Huang J, Li R, et al. Impact analysis of environmental and social factors on early-stage COVID-19 transmission in China by machine learning. Environ Res, 2022: 208: 112761. doi: 10.1016/j.envres.2022.112761
    [19]
    Auler A, Cássaro F, Silva V, et al. Evidence that high temperatures and intermediate relative humidity might favor the spread of COVID-19 in tropical climate: a case study for the most affected Brazilian cities. Sci Total Environ, 2020: 729: 139090. doi: 10.1016/j.scitotenv.2020.139090
    [20]
    Su X, Yan X, Tsai CL. Linear regression. WIREs, 2012: 4(3): 275–294.
    [21]
    Grissom R J, Kim J J. Effect sizes for research: univariate and multivariate applications. Routledge, 2012.
    [22]
    Gotway C A, Stroup W W. A generalized linear model approach to spatial data analysis and prediction. J Agric Biol Environ Stat, 1997: 157–178.
    [23]
    Nelder J A, Wedderburn R W. Generalized linear models. J R Stat Soc Ser A, 1972: 135(3): 370–384. doi: 10.2307/2344614
    [24]
    Karadağ M, Kulb S, Yoloğluc S, et al. Comparison of GAM and DLNM Methods for Disease Modeling in Environmental Epidemiology. Turkiye Klinikleri Journal of Biostatistics, 2021: 13(1).
    [25]
    Liu H. Generalized additive model., Duluth, USA: Department of Mathematics and Statistics, University of Minnesota, 2008: 55812.
    [26]
    Gasparrini A. Distributed lag linear and non-linear models in R: the package dlnm. J Stat Softw, 2011: 43(8): 1.
    [27]
    Zheng Y L, Guo Z, Zhang Y B, et al. Rapid triage for ischemic stroke: a machine learning-driven approach in the context of predictive, preventive and personalize medicine. EPMA J, 2022: 1–14.
    [28]
    Wu Z, Li L, Jin R, et al. Texture feature-based machine learning classifier could assist in the diagnosis of COVID-19. Eur J Radiol, 2021: 137: 109602. doi: 10.1016/j.ejrad.2021.109602
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