A fully GIS-integrated simulation approach for analyzing the spread of epidemics in urban areas
Bibliographic record
Abstract
Human-to-human communicable diseases can be devastating in urban areas where large heterogeneous population groups are living in restricted spaces, causing serious concerns for public health, especially during epidemic outbreaks. Even though Geographic Information Systems (GIS) have been used to study a variety of public health issues in the last decade, their use to study human communicable diseases has been limited to the development of disease clustering, mapping and surveillance systems. These systems don't provide ways to understand and predict the dynamics of diseases spread across an urban region, taking into account the dynamics of human contacts and mobility, which are the main widely recognized mechanisms responsible for diseases' spread. In this paper we address such limits by presenting a GIS-based spatial-temporal simulation approach and software to support public health decision making in the context of communicable diseases in urban areas. The approach fully integrates epidemiological, mobility and GIS-data models at an aggregate population level in order to support spatialized interventions.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".