Time‐distributed effect of exposure and infectious outbreaks
Bibliographic record
Abstract
Extreme weather affects the timing and intensity of infectious outbreaks, the resurgence and redistribution of infections, and it causes disturbances in human-environment interactions. Environmental stressors with high thermoregulatory demands require susceptible populations to undergo physiological adaptive processes potentially compromising immune function and increasing susceptibility to infection. In assessing associations between environmental exposures and infectious diseases, failure to account for a latent period between time of exposure and time of disease manifestation may lead to severe underestimation of the effects. In a population, health effects of an episode of exposure are distributed over a range of time lags. To consider such time-distributed lags is a challenging task given that the length of a latent period varies from hours to months and depends on the type of pathogen, individual susceptibility to the pathogen, dose of exposure, route of transmission, and many other factors. The two main objectives of this communication are to introduce an approach to modeling time-distributed effect of exposures to infection cases and to demonstrate this approach in an analysis of the association between high ambient temperature and daily incidence of enterically transmitted infections. The study is supplemented with extensive simulations to examine model sensitivity to response magnitude, exposure frequency, and extent of latent period.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".