ABSTRACT
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. © 2023 Author(s).
ABSTRACT
Coronavirus disease 2019, also known as Covid-19, is caused by a novel coronavirus called the severe acute respiratory syndrome coronavirus. The Covid-19 disease has become a severe threat to all countries globally, and the World Health Organization has labeled it a global pandemic. This study employs a modern statistical method known as functional data analysis, to examine the pattern of Covid-19 incidents in Malaysia. Smoothing functional data, which includes the summary of functional data, functional principal component, and functional outliers, are used to investigate the disease's characteristics. The functional principal component will capture the variation of the smoothing curves while their scores are utilised to cluster the Covid-19 cases. In addition, the functional graphical methods consist of rainbow plot, functional bagplot, and functional high density region are then used to identify outliers that were not visible in the original data plot. Functional data analysis provides more comprehensive methods than conventional statistical methods in identifying the pattern of Covid-19 incidence particularly in considering their temporal dynamics. © 2023 Author(s).