Visualisation System of COVID-19 Data in Malaysia - KHAN, REHMAN ULLAH; SYAMIMI, NOR SYAZA; MAMBANG, CLADIA SIMBUT ANAK; THOMAS, IVY ANAK; WEE, TZI NI (Trends in Undergraduate Research, 2021)
Pandemics are highly unlikely events, therefore, we need a system to understand the statistics about the pandamic. Machine learning algorithms can analyse the data and then we can plan for handling the pandamic. To date, many people are suffering because of the lack of reliable information system. The problem is that there is no integrated system to use the data and plan for pandemic management to minimise social panic. This study aims to provide a system, using COVID-19 data as a sample to visualise and analyse cases, deaths, discharged ICU cases updates in Malaysia as a whole state wise of COVID-19 daily statistics. The results provide visualisation and case comparison among states in Malaysia to easily and quickly understand the situation. This will help and assist the management in decision-making.