A multi-actor spatio-temporal interaction model used to geosimulate the zoonosis propagation (WIP)
Bibliographic record
Abstract
Several approaches and models have been proposed to simulate the spread of infectious diseases such as West Nile virus (WNV) or Lyme disease. However, these models such as mathematical models have some weaknesses when trying to simulate the influence of geographic features on the disease spread. In this context, we developed a new theoretical model called MASTIM to remedy some shortcomings of current methods. Our model is used to specify the spatio-temporal interactions of various kinds of actors (e.g. mosquitoes, ticks, birds, mammals, etc.) involved in the zoonosis propagation. We applied our model to the case of WNV and the case of Lyme disease in order to illustrate its genericity. Besides, we are currently using our model to develop Zoonosis-MAGS, a generic geosimulation tool for zoonoses. This tool aims at helping health policy makers to better understand the spread of zoonoses and the consequences of their 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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".