Advancing Pandemic Preparedness through a Data-Driven Hybrid Simulation Model
Bibliographic record
Abstract
The rise of new disease variants, such as COVID-19, influenza, and others, highlights the critical need for advanced epidemiological modeling to guide early-stage outbreak management, especially when vaccine options are not available or reliable. This paper presents a novel, hybrid, data-driven model that integrates Agent-Based Modeling (ABM) with an extended SEIHRD (Susceptible, Exposed, Infectious, Hospitalized, Recovered, and Dead) framework, enhanced by N-step Deep Q Reinforcement Learning (N-Step DQRL). This model merges ABM’s behavioral insights with the SEIHRD model’s progression dynamics, utilizing DQRL for adaptive, data-informed decision-making. It is particularly focused on enhancing non-pharmaceutical interventions, such as lockdown policies, which are crucial in managing outbreaks in the absence of vaccines. This approach strikes a balance between detailed analysis and scalability, vital for policymakers in responding to emerging disease variants. The model’s efficacy, as evidenced by an analysis of recent COVID-19 data, highlights its potential to significantly improve global pandemic preparedness and response, merging behavioral analysis with disease progression trends through the use of advanced deep learning techniques.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".