Analogies between SARS-CoV-2 infection dynamics and batch chemical reactor behavior

Area of Science:

• Epidemiology
• Chemical Kinetics
• Mathematical Modeling

Background:

• The SARS-CoV-2 pandemic presented significant challenges for public health.
• Traditional epidemiological models may not fully capture complex population dynamics.
• Understanding infection spread is crucial for effective emergency response.

Purpose of the Study:

• To develop a novel predictive model for infectious disease dynamics.
• To leverage analogies between chemical reactor behavior and pandemic spread.
• To provide reliable predictions for infection peak, magnitude, and resolution.

Main Methods:

• Modeled susceptible (A), infected (B), recovered (C), and deceased (D) populations as chemical species.
• Applied chemical kinetics and physical principles to model population dynamics.
• Regressed kinetic parameters globally and locally for accurate predictions.
• Validated models using data from Chinese provinces with completed infection cycles.

Main Results:

• The chemical reactor analogy provided a reliable framework for pandemic modeling.
• Model predictions demonstrated accuracy in forecasting infection peak time and entity.
• Validated predictions against real-world data from completed outbreaks.
• Established a continuously updated pandemic prediction database.

Conclusions:

• Chemical and physical principles offer a robust approach to pandemic forecasting.
• Predictive models based on these analogies can significantly support emergency planning.
• The developed model provides reliable insights into infection dynamics.
• Ongoing re-regression and daily updates enhance prediction accuracy for ongoing pandemics.