Analytic Platform on COVID-19 Data Cases in Malaysia

The COVID-19 pandemic has significantly impacted global public health systems which Malaysia has experienced notable case surges between 2020 and 2021. Presentation of Covid-19 cases and analytics is important where audience can easily read and understand the situation. This paper presents the development of a COVID-19 data analytic platform leveraging the Internet of Things (IoT) and Big Data Analytics (BDA) to monitor and visualize daily pandemic data. A dataset sourced from Kaggle, encompassing new cases, recoveries, ICU admissions, ventilator usage, and deaths, was processed using Google Colab and visualized via the ThingsBoard platform. Descriptive statistics and correlation analyses were conducted to extract meaningful patterns. Visual representations, including trendlines, bar charts, and scatter plots, revealed peak case incidences in early 2021 and provided insights into the healthcare burden during critical periods. Data from COVID-19 Malaysia for the years 2020 to 2021 show that 385 date of cases have not been correctly filtered, making it challenging to identify patterns or trends in the dataset. The data summation for new cases are 365946 and recovered cased are 348824 and summation 1340 is for death cases in that year. The integration of IoT and BDA in this study illustrates an effective approach for real-time public health monitoring and data-driven decision-making in response to pandemic scenarios.