Modelling the transmission dynamics of acute haemorrhagic conjunctivitis: application to the 2003 outbreak in Mexico
Bibliographic record
Abstract
We model an outbreak of acute haemorrhagic conjunctivitis (AHC) using a simple epidemic model that includes susceptible, infectious, reported, and recovered classes. The model's framework considers the impact of underreporting and behaviour changes on the transmission rate and is applied to a recent epidemic of AHC in Mexico, using a fit to the cumulative number of cases to estimate model parameters, which agree with those derived from clinical studies. The model predicts a 'mean time from symptomatic onset to diagnosis' of 1.43 days (95 per cent CI: 1-2.5) and that the final size of the Mexican epidemic was underreported by 39 per cent. We estimate that a primary infectious case generates approximately 3 secondary cases (R0* = 2.64, SD 0.65). We explore the impact of interventions on the final epidemic size, and estimate a 36 per cent reduction in the transmission rate due to behaviour changes. The effectiveness of the behaviour changes in slowing the epidemic is evident at 21.90 (SD 0.19) days after the first reported case. Results therefore support current public health policy including expeditious announcement of the outbreak and public health information press releases that instruct individuals on avoiding contagion and encourage them to seek diagnosis in hospital clinics.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".