The dynamics of the risk perception on a social network and its effect on disease dynamics
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
The perceived infection risk changes individual behaviors, which further affects the disease dynamics. This perception is influenced by social communication, including surveying their social network neighbors about the fraction of infected neighbors and averaging their neighbors' perception of the risk. We model the interaction of disease dynamics and risk perception on a two-layer random network that combines a social network layer with a contact network layer. We found that if information spreads much faster than disease, then all individuals converge on the true prevalence of the disease. On the other hand, if the two dynamics have comparable speeds, the risk perception still converges to a value uniformly on the network. However, the perception lags behind the true prevalence and has a lower peak value. We also study the behavior change caused by the perception of infection risk. This behavior change may affect the disease dynamics by reducing the transmission rate along the edges of the contact network or by breaking edges and isolating the infectious individuals. The effects on the basic reproduction number, the peak size, and the final size are studied. We found that these two effects give the same basic reproduction number. We find edge-breaking has a larger effect on reducing the final size, while reducing the transmission rate has a larger effect on reducing the peak size, which is true for both scale-free and Poisson networks.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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