Epidemic Modeling

The ICM Epidemiological Model was built upon research conducted at the University of Warsaw between 2008 and 2010 focused on influenza epidemic propagation. At the onset of the COVID-19 pandemic, this groundwork enabled the team to rapidly develop and operationally deploy an advanced, multi-faceted computational epidemiological model designed to forecast and analyze the trajectory of COVID-19. Over time, the model earned widespread acclaim among national and international experts. Throughout the peak pandemic years of 2020–2024, it served as the cornerstone for numerous high-impact analyses and predictive models delivered directly to top-tier state authorities.

Model Architecture

The system is constructed across two interconnected layers: a computational replica of the Polish population and a viral transmission module.

The Viral Transmission Layer: This module consists of a stochastic infection simulator that integrates multi-disciplinary insights from medicine, immunology, sociology, and social psychology to map the real-time spread of the virus.

The Synthetic Population Layer: This component instantiates over 38 million virtual agents within a computational environment, dynamically linked through households, schools, workplaces, and other social venues. Both the virtual individuals and their interaction spaces are spatially geolocated and mapped to a real-world transit network. To the best of our knowledge, this stands as the most advanced and granular model of Polish society ever engineered.

Potential Healthcare Applications

• A tool supporting the management of infectious disease epidemic crises – an essential element of an Epidemic Preparedness Plan
  The model enables a profound understanding of the course of infectious disease epidemics, such as COVID-19 or influenza, performing epidemiological predictions, and simulating the impact of restrictions and medical interventions on the subsequent trajectory of the epidemic. The model allows for the analysis and prediction of epidemic development at a high spatio-temporal resolution, incorporating scenario analyses based on multiple types of regional restrictions broken down by participating social groups.
• A model for the evolution of the health status of Polish society over time scales spanning the coming years and decades Thanks to its modular and two-layer design, the model can be used not only as an analytical tool in crisis situations, but also as an instrument for public health analysis and the spatial planning of healthcare system components nationwide. The model, combined with data obtained from cohort studies, i.e., Białystok+, enables multi-year simulations of the development of the population’s disease burden and risk factors, while allowing for scenario-based evaluations of how systemic interventions impact shifts in public health status.
• An analytical system for the accessibility of healthcare services and provisions
  The virtual society model of Poland, integrated with data on healthcare facilities and medical services, allows for an assessment of how the spatial distribution of healthcare facilities impacts the health situation of local communities, and supports the optimal planning of healthcare system development.

Social Impact and Scientific Visibility

The team developing the ICM Epidemiological Model originates from outside the medical epidemiology field—it comprises individuals with backgrounds in exact sciences, specializing in building mathematical and computational models of complex dynamic processes. However, the team and the model they created earned widespread recognition within the medical community, becoming an invaluable analytical asset during the COVID-19 crisis. The insights and results generated by the team were utilized by entities including the Ministry of Health (particularly DAIS), the Chancellery of the Prime Minister (KPRM), the Government Centre for Security (RCB), the Medical Research Agency (ABM), and the Health Council under the President of the Republic of Poland.

The team has a proven track record of collaboration with numerous computational epidemiology centers both domestically and internationally, including the European Centre for Disease Prevention and Control (ECDC), the Institute for Health Metrics and Evaluation at the University of Washington, the OptimAgent Consortium, Halle University in Germany, and the Białystok+ Project.

The team’s core mission is to seamlessly bridge scientific research with the development of advanced tools that deliver practical applications for public administration.