Comparison of Count Data Generalised Linear Models: Application to Air-Pollution Related Disease in Johor Bahru, Malaysia
نویسندگان
چکیده
Poisson regression is a common approach for modelling discrete data. However, due to characteristics of distribution, might not be suitable since most data are over-dispersed or under-dispersed. This study compared four generalised linear models (GLMs): negative binomial, Poisson, zero-truncated and binomial. An air-pollution-related disease, upper respiratory tract infection (URTI), its relationship with various air pollution climate factors were investigated. The obtained from Johor Bahru, Malaysia, January 1, 2012, December 31, 2013. Multicollinearity between the covariates independent variables was examined, model selection performed find significant each model. showed that binomial best determine association number URTI cases factors. Particulate Matter (PM10), Sulphur Dioxide (SO2) Ground Level Ozone (GLO) affect this disease significantly. do significantly influence cases. constructed in can utilised as an early warning system prevent mitigate involved parties, such local authorities hospitals, also employ when facing risk may occur
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ژورنال
عنوان ژورنال: pertanika journal of science and technology
سال: 2023
ISSN: ['0128-7680', '2231-8526']
DOI: https://doi.org/10.47836/pjst.31.4.16