Application of the ARIMA model on the COVID-2019 epidemic dataset

TLDR

COVID‑19 is a global threat, and many studies use mathematical models to predict its evolution, though such models may be biased and require real‑time case definition and data collection for comparison and future analysis. The study proposes a simple econometric model to predict COVID‑19 spread. The authors applied an ARIMA model to Johns Hopkins epidemiological data to forecast COVID‑19 prevalence and incidence trends.

Abstract

Coronavirus disease 2019 (COVID-2019) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic. These mathematical models based on various factors and analyses are subject to potential bias. Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019. We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins epidemiological data to predict the epidemiological trend of the prevalence and incidence of COVID-2019. For further comparison or for future perspective, case definition and data collection have to be maintained in real time.