A comparative study of SIR Model, Linear Regression, Logistic Function and ARIMA Model for forecasting COVID-19 cases | Zendy

Saina Abolmaali | Zendy; Samira Shirzaei | Zendy

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A comparative study of SIR Model, Linear Regression, Logistic Function and ARIMA Model for forecasting COVID-19 cases

Author(s) -

Saina Abolmaali,

Samira Shirzaei

Publication year - 2021

Publication title -

aims public health

Language(s) - English

Resource type - Journals

ISSN - 2327-8994

DOI - 10.3934/publichealth.2021048

Subject(s) - logistic regression , covid-19 , autoregressive integrated moving average , epidemic model , econometrics , statistics , linear regression , regression analysis , computer science , mathematics , medicine , time series , virology , outbreak , environmental health , population , disease , infectious disease (medical specialty)

Starting February 2020, COVID-19 was confirmed in 11,946 people worldwide, with a mortality rate of almost 2%. A significant number of epidemic diseases consisting of human Coronavirus display patterns. In this study, with the benefit of data analytic, we develop regression models and a Susceptible-Infected-Recovered (SIR) model for the contagion to compare the performance of models to predict the number of cases. First, we implement a good understanding of data and perform Exploratory Data Analysis (EDA). Then, we derive parameters of the model from the available data corresponding to the top 4 regions based on the history of infections and the most infected people as of the end of August 2020. Then models are compared, and we recommend further research.

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