Modelling the COVID-19 pandemic in Peninsular Malaysia by using logistic regression model.

• Main Authors: Puslan, Ruzaini Zulhusni, Suhaila, Jamaludin, Mohd. Khalid, Khalid, ZarinaZarina
• Format: Conference or Workshop Item
• Published: 2023
• Subjects: QA Mathematics; QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/107814/; http://dx.doi.org/10.1063/5.0110121
• Institution: Universiti Teknologi Malaysia

The rise of COVID-19 disease has brought the world to a very worrisome stage. It started in Wuhan, China and numerous export cases had been verified in other provinces in China, as well as other countries. Considering the global threat, WHO has declared COVID-19 a public health emergency of international concern (PHEIC). The number of positive cases rose beyond 553 cases on 16th March 2020 and the Prime Minister of Malaysia declared a lockdown. By the end of 2020, Malaysia had 113 010 cases and 471 deaths. The aim of the present study is to employ a logistic regression model in handling COVID-19 cases and to find the association between COVID-19 cases and climate factors. Minimum temperature (C), maximum temperature (C), temperature (C), wind speed (m/s) are all climate components. The number of the population also will be included in this study. This study will only be conducted in Peninsular Malaysia, and all climate data will be collected from July to December 2020. The logistic regression model is employed in this study since the predictor variable is binary, with redzone (over 40 active cases) as 1 and non-red zone (under 40 active cases) as 0. Based on the results, only the population variables is denoted the growth of COVID-19 cases since the variables was significance almost every week. However, none of the climate variable were found to be related to the COVID-19 incidence.