The ZoonosisMAGS Project (Part 1): Population‐Based Geosimulation of Zoonoses in an Informed Virtual Geographic Environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To overcome the limits of current epidemiological models and simulation of zoonosis spread in which the spatial/geographic dimension is either missing or quite limited, we propose a geosimulation approach integrating population modeling, patch modeling, and the simulation of population interactions and mobility, using georeferenced data. In this chapter, we present the foundations of our population-based approach integrating the spatial and mobility dimensions, with a special emphasis on the extended compartment model that we propose to model populations’ interaction and evolution. The associated simulation tool uses the new concept of Informed Virtual Geographic Environment. We illustrate the application of this approach to the case of Lyme disease spread. The usefulness of this new model is illustrated by a series of simulations over long periods (40 years) using realistic climatic scenarios (observed global increase of temperatures between 1970 and 2010). The simulation results allow for the study of the establishment of tick colonies in noninfected areas at different latitudes (represented by a difference of their mean annual temperatures), as well as the extent of the infection spread when it starts in an area with already established tick colonies, from which birds migrate to northern and colder areas in spring and to which they return in fall.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it