Multivariate time series analysis on variables that influence pandemic expansion

Abstract

The ongoing COVID-19 pandemic has wreaked havoc on social and economic systems worldwide. The variance in the rapidly increasing number of illnesses and deaths in each country is primarily due to national policies and actions. As a result, governments and institutions need to get insights into the critical factors influencing COVID-19 future case counts to properly manage the adverse effects of pandemics and promptly prepare appropriate measures. Thus, in this paper, we conduct extensive experiments on the real-world covid-19 datasets to examine the important factors influencing in the pandemic growth. In particular, we perform an exploratory data analysis to get the statistic and characteristics of multivariate time-series data on pandemic dynamic. Also, we utilize a statistical measure such as Pearson correlation to compute the relations of the past on the future daily new cases. The experimental results demonstrate that some restrictions have a positive effect on daily new confirmed cases at the early stage of the local pandemic transmission. Also, the results show that the early trend of COVID-19 can be explained well by human mobility in various categories. Thus, our proposed framework can be served as a guideline for future pandemic prevention and control decision-making.