Modelling the spread of COVID-19 in Peninsular Malaysia using geographically weighted logistic regression.

The whole world has faced different obstacles facing the coronavirus disease (COVID-19) since the first day of infectious contagion crisis in Wuhan, Hubei, China in late 2019. It has had a huge impact on society and the economy across the country. According to the World Health Organization (WHO), COVID-19 is a disease caused by a new coronavirus known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 virus). Most people who are infected will get a mild to moderate respiratory illness. The government took drastic measures on March 16th by implementing the Movement Control Order (MCO), and the Ministry of Health (MOH) has issued a standard operating procedure (SOP). The study's objective is to create a geographically weighted logistic regression model (GWLR) to determine the relationship between COVID-19 disease and climate and social-demographic factors. Maximum temperature, minimum temperature, average temperature, humidity, wind speed and rainfall are all climate variables. Meanwhile, population, elderly population, urbanization and number of hospitals are socio-demographic variables. This study uses data with spatial non-stationarity and a binary predictor variable. The status of the redzone in each district is the predictor variable in this study, with 1 (≥40 14 days COVID-19 cases) indicating a redzone and 0 (<40 14 days COVID-19 cases) indicating a non-redzone. As a result, GWLR is the best model for computing the data in this study. All climate and social-demographic data were chosen from February 23rd to September 20th 2021 at which point, following the occurrence of the third wave of COVID-19, the government is conducting a immunization programme. With this analysis, we can determine which variables affecting the growth of COVID-19 cases based on the odds ratio. [ABSTRACT FROM AUTHOR]