Operator-modified SIR model for epidemic forecasting: A case study of COVID-19 in Nigeria

Domain:

healthcare

Record type:

paper

Creator:

D.OS. O. I.P

Publisher:

Afr

Host:

Accurate forecasting of COVID-19 dynamics is essential for timely intervention. We compare the classical SIR model of Kermack and McKendrick with an operator-modified SIR formulation based on the Makinde differential operator. Using weekly Nigerian data (April–May 2025) on COVID-19, we estimate classical transmission and recovery rates. We also derive curvature-corrected operator rates using second-order incidence changes, yielding smoother estimates. Each model is discretized to predict new cases and evaluated by root-mean-square error (RMSE), Akaike Information Criterion (AIC), and peak-magnitude comparison. The classical SIR exactly reproduces in-sample data, whereas the modified model yields a smoother predicted peak, indicating improved generalizability. These results affirm the utility of memory-augmented epidemic models in forecasting and intervention-sensitive policy design under data uncertainty.

Visit

doi.org

Simulations of the COVID-19 Epidemic in Nigeria Using SIR Model

Abstract This study aims to review some applications of SIR model in COVID-19 epidemic situations,

Predicting the COVID-19 epidemic in Algeria using the SIR model

Abstract The aim of this study is to predict the daily infected cases with Coronav

Analysis of the COVID-19 pandemic by SIR model and machine learning technics for forecasting

This work is a trial in which we propose SIR model and machine learning tools to analyze the coronav

Numerical Simulation of SIR Model for COVID-19 vaccination in Algeria

Forecasting the scale of the COVID-19 epidemic in Kenya

Abstract Background T

Analysis of a modified Covid-19 model in Nigeria and approximations using differential transformation method

Mathematical models has been useful over the years to understand the behavior and impacts of several