Biclustering Analysis of Countries Using COVID-19 Epidemiological Data

Dhamodharavadhani, S.; Rathipriya, R.

In this work, COVID-19 data were analyzed using the biclustering approach to gain insights such as which group of countries have similar epidemic trajectory patterns over the subset of COVID-19 pandemic outburst days (called bicluster). Countries within these groups (biclusters) are all in the same phase but with a slightly different trajectory. An approach based on the Greedy Two-Way KMeans biclustering algorithm is proposed to analyze COVID-19 epidemiological data, which identifies subgroups of countries that show a similar epidemic trajectory patterns over a specific period of time. To the best of authors’ knowledge, this is the first time that the biclustering approach has been applied to analyze COVID-19 data. In fact, these COVID-19 epidemiological data is not a real count because not all data can be tracked properly and other practical difficulties in collecting the data. Even in developed countries, it has huge practical problems. Therefore, if we can use the IoT-based COVID-19 monitoring system to detect the origin of the COVID-19 outbreak, we can identify the real situation in each country. Results confirm that the proposed approach can alert and helps the government authorities and healthcare professionals to know what to anticipate and which measures to implement to decelerate the COVID-19 spread. © 2021, Springer Nature Switzerland AG.