Forecasting SARS-CoV-2 epidemic dynamic in Poland with the pDyn agent-based model
Bibliographic record
Abstract
Abstract In this work, we describe and forecast the fourth wave of the SARS-CoV-2 epidemic, driven by the Delta variant, using pDyn — a detailed epidemiological agent-based model. It is designed to explain the spatiotemporal dynamics of the SARS-CoV-2 spread across Polish society, predicting the number and locations of disease-related states for agents living in the virtual society in response to varying properties of the pathogen and the social structure and behavior. We evaluate the validity of the dynamics generated by the model, including the succession of pathogen variants, immunization dynamics, and the ratio of vaccinated individuals among confirmed cases. Additionally, we assess the model’s predictive power in estimating pandemic dynamics (peak timing, peak value, and wave length) of disease-related states (number of confirmed cases, hospitalizations, ICU hospitalizations, and deaths) both at the national level and in the highest administrative units in Poland (voivodships). When testing the model’s validity, we compared real-world data (excluding data used for calibration) to our model estimates to evaluate whether pDyn accurately reproduced the epidemic dynamics up to the simulation time (October 28, 2021). To assess the accuracy of pDyn’s predictions, we retrospectively compared simulation results with real-world data acquired after the simulation date, evaluating pDyn as a tool for predicting future epidemic spread. Our results indicate that pDyn accurately predicts and can help us better understand the mechanisms underlying the SARS-CoV-2 dynamics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".