Integrated epidemiologic simulation for person to person contagion through urban mobility within GIS
Bibliographic record
Abstract
In recent years, advances in Health Geography, Geographical Epidemiology and Public Health Informatics have led to an extensive use of Geographic Information Systems (GIS) to study a variety of public health issues. Considering infectious disease outbreaks, time becomes a critical factor and Public Health officers require tools to support rapid decision making. In this context, GIS technology presents some limits. Mainly, the study of communicable diseases requires the development of complicated spatial-temporal models which is often time and effort consuming. In addition, this type of dynamic analysis is hard to realize by means of the GIS functionalities commonly available. Addressing such limits, we present in this paper a new GIS-based spatial-temporal simulation approach and software to support public health decision making in the context of communicable diseases. Our approach stands out by the integrative perspective and the explicit spatial aspect that it offers. On the one hand, it fully integrates epidemiological, mobility and GIS-data models at an aggregate population level in order to support public health decision making. This is made possible because our approach is built on data automatically processed from transportation surveys that are widely available, at least in North America and Europe. Our approach is thus simple and can be promptly put into use. On the other hand, our approach particularly aims at supporting decision makers with respect to "spatialized" intervention policies. Mainly, it allows for the assessment of different public intervention actions in different spatial locations of the studied area and the evaluation of their effects on the disease spatial evolution and distribution.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 0.001 |
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".