On COVID-19 Modelling.

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• The COVID-19 pandemic necessitated reliable epidemic forecasting.
• Standard epidemiological models like SIR often struggle with real-world data limitations.

Purpose of the Study:

• To develop a data-driven mathematical model for predicting COVID-19 epidemic peaks.
• To accurately forecast infection peaks and analyze the impact of non-pharmaceutical interventions.

Main Methods:

• Utilized a modified Susceptible-Infected-Recovered (SIR) model, closely adhering to available data.
• Integrated infection fatality rates and data-driven recovery rates to estimate unregistered infections.
• Employed mathematical and numerical methods for analysis and prediction.

Main Results:

• The modified SIR model accurately monitors the registered pandemic.
• Predictions of infection peaks were generated and illustrated with country-specific examples.
• The model demonstrated its ability to track the transition from uncontrolled outbreaks to mitigation phases.

Conclusions:

• The developed model provides a reliable technique for predicting epidemic peaks.
• It effectively reconstructs hidden infection data for more accurate forecasting.
• The approach is valuable for understanding pandemic dynamics and intervention effectiveness.