Prediction of the Impact of the End of year Festivities on the Local Epidemiology of COVID-19 Using Agent-Based Simulation with Hidden Markov Models
Bibliographic record
Abstract
Abstract Towards the end of 2020, as people changed their usual behavior due to end of year festivities, increasing the frequency of meetings and the number of people who attended them, the COVID-19 local epidemic’s dynamic changed. Since the beginnings of this pandemic, we have been developing, calibrating and validating a local agent-based model (AbcSim) that can predict intensive care unit and deaths’ evolution from data contained in the state electronic medical records and sociological, climatic, health and geographic information from public sources. In addition, daily symptomatic and asymptomatic cases and other epidemiological variables of interest disaggregated by age group can be forecast. Through a set of Hidden Markov Models, AbcSim reproduces the transmission of the virus associated with the movements and activities of people in this city, considering the behavioral changes typical of local holidays. The calibration and validation were performed based on official data from La Rioja city in Argentina. With the results obtained, it was possible to demonstrate the usefulness of these models to predict possible outbreaks, so that decision-makers can implement the necessary policies to avoid the collapse of the health system.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".