Memory-based meso-scale modeling of Covid-19: County-resolved timelines in Germany

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• The COVID-19 pandemic necessitated robust data gathering and modeling to understand viral spread.
• Accurate spatio-temporal models are crucial for societal recovery and returning to normalcy.
• Traditional SIR-type models have limitations in incorporating detailed medical data on disease progression.

Purpose of the Study:

• To develop a spatially resolved infection model for Germany.
• To integrate medical data on disease course using a memory-based integro-differential approach.
• To provide county- and city-level estimates for public health decision-making.

Main Methods:

• Combined a spatially resolved county-level infection model with a memory-based integro-differential approach.
• Calibrated the model using cumulative infection and death data from the Robert-Koch Institute.
• Utilized the model to estimate new infections, hospitalization rates, and intensive care unit demands.

Main Results:

• The model provides county- and city-level estimates of key epidemiological indicators.
• Demonstrated the capability to directly include medical data on disease progression, overcoming limitations of traditional models.
• Successfully calibrated with real-world data from Germany.

Conclusions:

• The developed model offers a more accurate and detailed approach to understanding and predicting viral spread.
• It can assist decision-makers in locally tailoring responses to potential future outbreaks.
• The integration of medical data enhances the model's utility for public health management.