COVID-19 Epidemic Process Simulation Using ARIMA Model

Mohammadi, Alireza; Chumachenko, Dmytro

doi:10.1007/978-3-030-94259-5_31

Cited by 1 publication

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Krivtsov et al ( 6 ) is a relatively recent example of research based on more complex Neural Network (NN) methods using data for the same countries. Mohammadi and Chumachenko ( 7 ) employ ARIMA methods to forecast cases for Ukraine. Other examples of ARIMA based studies at the country level include Dansana et al ( 8 ), Singh et al ( 9 ), and Sahai et al ( 10 ).…”

Section: Introductionmentioning

confidence: 99%

Forecasting daily COVID-19 cases with gradient boosted regression trees and other methods: evidence from U.S. cities

Sen,

Stevens,

Tran

et al. 2023

Front. Public Health

View full text Add to dashboard Cite

IntroductionThere is a vast literature on the performance of different short-term forecasting models for country specific COVID-19 cases, but much less research with respect to city level cases. This paper employs daily case counts for 25 Metropolitan Statistical Areas (MSAs) in the U.S. to evaluate the efficacy of a variety of statistical forecasting models with respect to 7 and 28-day ahead predictions.MethodsThis study employed Gradient Boosted Regression Trees (GBRT), Linear Mixed Effects (LME), Susceptible, Infectious, or Recovered (SIR), and Seasonal Autoregressive Integrated Moving Average (SARIMA) models to generate daily forecasts of COVID-19 cases from November 2020 to March 2021.ResultsConsistent with other research that have employed Machine Learning (ML) based methods, we find that Median Absolute Percentage Error (MAPE) values for both 7-day ahead and 28-day ahead predictions from GBRTs are lower than corresponding values from SIR, Linear Mixed Effects (LME), and Seasonal Autoregressive Integrated Moving Average (SARIMA) specifications for the majority of MSAs during November-December 2020 and January 2021. GBRT and SARIMA models do not offer high-quality predictions for February 2021. However, SARIMA generated MAPE values for 28-day ahead predictions are slightly lower than corresponding GBRT estimates for March 2021.DiscussionThe results of this research demonstrate that basic ML models can lead to relatively accurate forecasts at the local level, which is important for resource allocation decisions and epidemiological surveillance by policymakers.

show abstract