Predicting of COVID-19 Confirmed Cases in Different Countries with ARIMA Models in 2020
Preprint
- 18 March 2020
- preprint
- other
- Published by Cold Spring Harbor Laboratory
Abstract
The epidemic of a novel coronavirus illness (COVID-19) becomes as a global threat. The aim of this study is first to find the best prediction models for daily confirmed cases in countries with high number of confirmed cases in the world and second to predict confirmed cases with these models in order to have more readiness in healthcare systems. This study was conducted based on daily confirmed cases of COVID-19 that were collected from the official website of Johns Hopkins University from January 22th, 2020 to March 1th, 2020. Auto Regressive Integrated Moving Average (ARIMA) model was used to predict the trend of confirmed cases. Stata version 12 and R version 3.6.2 were used. Parameters used for ARIMA were (2,1,0) for Mainland China, ARIMA(1,0,0) for South Korea, and ARIMA(3,1,0) for Thailand. Mainland China and Thailand were successful in haltering COVID-19 epidemic. Investigating their protocol in this control like quarantine should be in the first line of other countries’ programKeywords
This publication has 12 references indexed in Scilit:
- Coronavirus Infections—More Than Just the Common ColdJAMA, 2020
- Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challengesInternational Journal of Antimicrobial Agents, 2020
- Quantifying bias of COVID-19 prevalence and severity estimates in Wuhan, China that depend on reported cases in international travelersPublished by Cold Spring Harbor Laboratory ,2020
- Real-Time Estimation of the Risk of Death from Novel Coronavirus (COVID-19) Infection: Inference Using Exported CasesJournal of Clinical Medicine, 2020
- The reproductive number of COVID-19 is higher compared to SARS coronavirusJournal of Travel Medicine, 2020
- Comparison of the Ability of ARIMA, WNN and SVM Models for Drought Forecasting in the Sanjiang Plain, ChinaNatural Resources Research, 2019
- Comparison of ARIMA and GM(1,1) models for prediction of hepatitis B in ChinaPLOS ONE, 2018
- Forecasting of demand using ARIMA modelInternational Journal of Engineering Business Management, 2018
- Application of a Combined Model with Autoregressive Integrated Moving Average (ARIMA) and Generalized Regression Neural Network (GRNN) in Forecasting Hepatitis Incidence in Heng County, ChinaPLOS ONE, 2016
- A hybrid seasonal prediction model for tuberculosis incidence in ChinaBMC Medical Informatics and Decision Making, 2013