Data-assimilation and state estimation for contact-based spreading processes using the ensemble kalman filter:...

Area of Science:

• Epidemiology and Network Science
• Computational Biology and Disease Modeling

Background:

• Understanding disease transmission dynamics in complex networks is crucial.
• Existing models often struggle to account for asymptomatic infections and network complexity.
• Accurate real-time monitoring and uncertainty management are vital for effective public health interventions.

Purpose of the Study:

• To present an extended contact-based model for contagious disease spread in complex networks, including asymptomatic cases.
• To introduce a robust parametrization method for the model, validated using COVID-19 data from Germany, Mexico, and the USA.
• To demonstrate the integration of network spreading models with state estimation techniques for real-time monitoring and uncertainty handling.

Main Methods:

• Development of an extended contact-based network model representing disease spread.
• Application of a novel parametrization method, validated against real-world COVID-19 epidemiological data.
• Integration with modern state estimation and filtering techniques for dynamic analysis.

Main Results:

• The proposed model effectively describes contagious disease spread, including asymptomatic transmissions.
• Parametrization and validation using COVID-19 data confirmed the model's applicability across different regions.
• The combined approach enables efficient real-time monitoring and robust management of stochastic uncertainties.

Conclusions:

• The extended contact-based network model offers a powerful tool for understanding disease dynamics.
• The integration with state estimation enhances the practical utility of network models for public health.
• This approach provides a scalable framework for analyzing disease spread across various network levels.