case study
Predicting Pandemic Evolution
Necessary information /
Context / Client information
The COVID-19 pandemic highlighted the need for powerful tools to simulate the spread of disease as closely as possible to reality. The solution needed to model contact networks between individual agents and be ready for simulation of other population diseases in the future.
We set three main goals:
- Realistic Simulation: Base predictions on a synthetic population reflecting detailed demographic, social, and economic characteristics of various regions.
- Simple Deployment: Create a system usable in computing centers or the cloud despite computationally demanding tasks.
- User-friendly Interface: Enable easy manipulation of data and definition of parameters.
How we solved the problem /
Solution /
We used the existing Covasim agent-based model and the Synthpops synthetic population generator developed by the Institute for Disease Modeling. These tools offered extensive simulation capabilities but lacked a user-friendly interface and support for new features.
We enhanced Covasim and Synthpops with a user-friendly interface for importing data in different formats. The system simulates interregional travel and the emergence of new disease variants.
Extensive documentation guides users through system operation and data entry. Components can run individually or be chained together, and the system generates customizable tabular reports.
Used technologies /
Technologies /
The solution combines the Covasim agent-based model with the Synthpops synthetic population generator, a user-friendly data-import interface, interregional travel and disease-variant simulation, modular processing, documentation, and customizable tabular reporting.
Direct outcome /
RESULT /
We developed a comprehensive system for realistic simulation of the spread of diseases with a user-friendly interface and extended functions. It offers valuable tools for research institutions, healthcare organizations, and government agencies to fight pandemics and plan preventive measures.
The new features can simulate other illnesses with basic parameters such as transmissibility and severity. The interface and detailed documentation make it easier to operate the system and enter data; the model can be further calibrated, tested, and developed.
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