Songkhla's Battle Against COVID-19: An Interactive Dashboard for Assessing the Performance Management and the Prediction of New Cases
Conference proceedings article
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Publication Details
Author list: Arromrit, Trirat; Jackson Wongsaroj, Prince Paris; Phangsee, Thanaphon; Piboon, Manisara; Rungnisakorn, Chanakarn;
Prom-On, Santitham; Mahikul, Wiriya
Publication year: 2023
Start page: 502
End page: 507
Number of pages: 6
Languages: English-Great Britain (EN-GB)
Abstract
COVID-19 pandemic had a profound impact on multiple socioeconomic and public healthcare aspects in Songkhla, a province in Thailand including an economic recovery and tourism hub. To recover, appropriate management is required to prevent and control further outbreaks. This depends on available data and analysis and visualization for better policy-making and decisions. Thus, we develop an interactive dashboard for decision-making processes by analyzing multiple publicly available online data sources. To ensure accuracy and reliability, the data was cleaned and analyzed using three levels of analytics: description, diagnosis, and prediction of infected new cases using multiple linear regression. The result provides decision-makers with the necessary insights to make informed decisions. The analysis result also reveals a significant association between the total population, number of households, number of hospital beds, and proportion of vaccinated individuals with the number of new COVID-19 cases. The result of the Multiple linear regression had R-square and adjusted R-square values of 0.87 and 0.83 for the prediction model. The interactive dashboard assists local governments in evaluating the COVID-19 situation. The model is used for a preliminary assessment of the COVID-19 situation to more generalizable and accurate results. © 2023 IEEE.
Keywords
interactive dashboard, Performance Management
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