Interactive tool for clustering and forecasting patterns of Taiwan COVID-19 spread.

ABSTRACT: The COVID-19 data analysis is essential for policymakers to analyze the outbreak and manage the containment. Many approaches based on traditional time series clustering and forecasting methods, such as hierarchical clustering and exponential smoothing, have been proposed to cluster and forecast the COVID-19 data. However, most of these methods do not scale up with the high volume of cases. Moreover, the interactive nature of the application demands further critically complex yet compelling clustering and forecasting techniques. In this paper, we propose a web-based interactive tool to cluster and forecast the available data of Taiwan COVID-19 confirmed infection cases. We apply the Model-based (MOB) tree and domain-relevant attributes to cluster the dataset and display forecasting results

SUBMITTER: Ashouri M 

PROVIDER: S-EPMC9246234 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature