Forecasting SARS-CoV-2 epidemic dynamic in Poland with the pDyn agent-based model

Area of Science:

• Epidemiology
• Computational modeling
• Public health

Background:

• The SARS-CoV-2 pandemic, particularly the Delta variant, presented significant public health challenges.
• Accurate forecasting of epidemic waves is crucial for effective resource allocation and intervention strategies.

Purpose of the Study:

• To forecast the fourth wave of the SARS-CoV-2 epidemic in Poland, driven by the Delta variant, using the pDyn agent-based model.
• To validate the pDyn model's predictive accuracy for epidemic dynamics, including peak timing, magnitude, and duration.
• To enhance the understanding of SARS-CoV-2 epidemic mechanisms and spatiotemporal spread.

Main Methods:

• Utilized pDyn (pandemics dynamics), an agent-based epidemiological model, to simulate SARS-CoV-2 spread.
• Incorporated pathogen properties, behavioral factors, variant succession, and immunization levels into the model.
• Validated model predictions by comparing simulation outputs with real-world data, both before and after the simulation period.

Main Results:

• The pDyn model demonstrated accuracy in forecasting epidemic wave characteristics (peak timing, magnitude, duration) for confirmed cases, hospitalizations, ICU admissions, and deaths.
• Model validation confirmed its ability to reproduce observed epidemic dynamics nationally and regionally in Poland.
• The study affirmed the model's reliability in predicting future pandemic trends.

Conclusions:

• The pDyn model is a valid and accurate tool for forecasting SARS-CoV-2 epidemic waves.
• The model provides valuable insights into the spatiotemporal dynamics and mechanisms driving epidemic spread.
• Findings support the use of agent-based modeling for informing public health preparedness and response strategies.