An Integrated Approach for Communicable Disease Geosimulation Based on Epidemiological, Human Mobility and Public Intervention Models
Bibliographic record
Abstract
We propose a new GIS-based spatial–temporal simulation approach and a tool that fully integrates human epidemiological, human mobility, and public intervention models in a GIS system to support public health decision-making in relation to communicable disease spread. Data about human population, activities, and mobility are systematically compiled from enriched transportation surveys, which enable the simulator to take into account the spatial locations of residence and usual activities (work, study, shopping, leisure, etc.) of different population groups (characterized by age groups), making possible the rapid exploration of intervention scenarios in the first days of an infectious disease's outbreak. The full integration of our simulator in a GIS allows a public health decision-maker to simply set intervention scenarios (i.e., vaccination, closure of different types of establishments, public transit etc.) and to visualize and assess the spread of a contagious disease in a geographic area displayed in a GIS. Our approach and tool allow for the rapid exploration of intervention scenarios in the first days of an infectious disease's outbreak.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".