Scopus
YÖKSİS Eşleşti
A novel statistical modeling of air pollution and the COVID-19 pandemic mortality data by Poisson, geometric, and negative binomial regression models with fixed and random effects
Open Chemistry · Ocak 2023
YÖKSİS Kayıtları
A novel statistical modeling of air pollution and the COVID-19 pandemic mortality data by Poisson, geometric, and negative binomial regression models with fixed and random effects
Open Chemistry · 2023 SCI-Expanded
PROFESÖR NESLİHAN İYİT →
Makale Bilgileri
DergiOpen Chemistry
Yayın TarihiOcak 2023
Cilt / Sayfa21
Scopus ID2-s2.0-85171127180
Özet
The coronavirus disease 2019 (COVID-19) pandemic was defined by the World Health Organization (WHO) as a global epidemic on March 11, 2020, as the infectious disease that threatens public health fatally. In this study, the main aim is to model the impact of various air pollution causes on mortality data due to the COVID-19 pandemic by Generalized Linear Mixed Model (GLMM) approach to make global statistical inferences about 174 WHO member countries as subjects in the six WHO regions. "Total number of deaths by these countries due to the COVID-19 pandemic"until July 27, 2022, is taken as the response variable. The explanatory variables are taken as the WHO regions, the number of deaths from air pollution causes per 100.000 population as "household air pollution from solid fuels,""ambient particulate matter pollution,"and "ambient ozone pollution."In this study, Poisson, geometric, and negative binomial (NB) regression models with "country"taken as fixed and random effects, as special cases of GLMM, are fitted to model the response variable in the aspect of the above-mentioned explanatory variables. In the Poisson, geometric, and NB regression models, Iteratively Reweighted Least Squares parameter estimation method with the Fisher-Scoring iterative algorithm under the log-link function as canonical link function is used. In the GLMM approach, Laplace approximation is also used in the prediction of random effects. In this study, six different Poisson, geometric, and NB regression models with fixed and random effects are established for 174 countries all over the world to make global statistical inferences for investigating the relationships between "total number of deaths"by these countries due to the COVID-19 pandemic and "air pollution causes."As a result of this study, "NB mixed-effects regression model"as the most appropriate GLMM is used to make global statistical inferences about the impact of the various air pollution causes on the mortality data due to the COVID-19 pandemic.
Yazarlar (2)
1
Neslihan Iyit
2
Ferhat Sevim
ORCID: 0000-0001-8541-4808
Anahtar Kelimeler
air pollution
COVID-19 pandemic
fixed effect
generalized linear mixed model
geometric regression
negative binomial regression
Poisson regression
random effect
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey