Systematic Comparison of Different Compartmental Models for Predicting COVID-19 Progression

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• The COVID-19 pandemic underscored the need for predictive models in public health and resource management.
• Evaluating the impact of model complexity on forecasting accuracy is crucial for pandemic preparedness.

Purpose of the Study:

• To assess how compartmental model complexity affects pandemic forecasting accuracy.
• To determine the utility of different models for healthcare resource planning during pandemics.

Main Methods:

• Compared various compartmental models (SIR, complex variants) using US COVID-19 data.
• Evaluated both adaptive and non-adaptive models for predicting infections, peaks, and resource needs.

Main Results:

• Simpler models often showed higher forecast accuracy, particularly in early stages and for peak predictions.
• Adaptive models excelled in short-term forecasting but were computationally intensive.
• Non-adaptive models provided stable long-term forecasts suitable for resource allocation.

Conclusions:

• Model selection should be tailored to the pandemic phase and decision-making timeline.
• Simpler models aid early interventions; adaptive models support short-term operations; non-adaptive models assist long-term planning.
• Informed model selection can enhance pandemic response effectiveness.