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

Domaine:

healthcare

Type de record:

paper
Créateur:
D.OS. O. I.P
Éditeur:
Afr
Hôte:
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.

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doi.org

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