Simulating the Spread of an Infectious Disease in a Small Community
Bibliographic record
Abstract
Infectious diseases remain a formidable challenge to public health, requiring comprehensive understanding of their transmission dynamics for effective mitigation and containment. Drawing inspiration from the pivotal lessons learned during the COVID-19 pandemic, this research aims to address the pressing need for localized disease spread modeling within small communities. Motivated by the profound impacts of COVID19 on global health and societal stability, our research seeks to extend existing methodologies to enhance the comprehension of disease transmission patterns, speed, and intervention impacts. Our focus lies in leveraging agent-based simulation (ABS) to capture individual-level interactions and behaviors within the community. Each agent represents an individual, allowing for the manipulation of starting characteristics to compare their results. Additionally, we incorporated human behavior details to agents and geographic details to increase the accuracy of disease spread predictions by introducing a mix of homogeneous and heterogeneous agents. By computer simulation, our research provided practical insights for the development of targeted mitigation plans and containment strategies tailored to small community settings.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".