A novel grey model based on Susceptible Infected Recovered Model: A case study of COVD-19

Area of Science:

• Epidemiology
• Mathematical Modeling
• Infectious Disease Dynamics

Background:

• The COVID-19 pandemic necessitates accurate forecasting for effective control measures.
• The Susceptible Infected Recovered (SIR) model is a foundational tool in epidemiological analysis.
• Existing grey prediction models may require enhancement for complex epidemic scenarios.

Purpose of the Study:

• To develop and validate a novel grey prediction model integrated with the SIR framework.
• To improve the accuracy and robustness of infectious disease forecasting, specifically for COVID-19.
• To provide a reliable tool for public health policy and intervention planning.

Main Methods:

• Analysis of the classic Susceptible Infected Recovered (SIR) model.
• Development of a grey prediction model leveraging SIR principles and grey system theory.
• Application of Laplace transform for model reduction and derivation of modeling steps.
• Validation using numerical cases of varying magnitudes and data lengths.

Main Results:

• The proposed grey-SIR model demonstrated significant superiority over three classical grey prediction models.
• The model proved effective in predicting the COVID-19 epidemic across diverse population sizes.
• Robustness was confirmed through successful modeling and prediction with datasets of varying lengths.

Conclusions:

• The novel grey-SIR model offers a powerful and accurate approach for COVID-19 epidemic prediction.
• The model's applicability extends to countries with different population scales and data characteristics.
• This research provides a valuable tool for enhancing epidemic preparedness and response strategies.