DDE: Deep Dynamic Epidemiological Modeling for Infectious Illness Development Forecasting in Multi-level Geographic...

Area of Science:

• Epidemiology
• Computational Biology
• Infectious Disease Dynamics

Background:

• Accurate epidemiological modeling is crucial for managing infectious diseases like COVID-19.
• Estimating parameters in traditional epidemiological equations (EE) is challenging due to variable interventions.
• Existing models struggle with precise real-world data fitting across diverse regions.

Purpose of the Study:

• To introduce a novel method, deep dynamic epidemiological modeling (DDE), for enhanced epidemiological parameter fitting.
• To improve the accuracy of disease spread simulations using deep learning.
• To develop adaptable models for various geographic contexts.

Main Methods:

• Developed the deep dynamic epidemiological modeling (DDE) approach, integrating EE with deep neural networks.
• Utilized neural ordinary differential equations to solve variant-specific epidemiological equations.
• Validated DDE performance against state-of-the-art methods using real-world data from five diverse geographic locations.

Main Results:

• DDE significantly improved the accuracy of parameter fitting compared to existing methods.
• Achieved an average fitting Pearson coefficient exceeding 0.97 across diverse geographic entities (USA, Colombia, South Africa, Wuhan, Italy).
• Demonstrated superior performance in fitting real-world infectious disease data.

Conclusions:

• The DDE method offers enhanced accuracy for parameter fitting in epidemiological models.
• DDE provides a foundation for developing simpler, adaptable models for different geographic areas.
• This approach facilitates more effective infectious disease management and intervention strategy development.