Age-specific transmission for different virus serotypes of hand, foot and mouth disease and the impact of interventions in East China, 2009–2015
Bibliographic record
Abstract
Background: Hand, foot and mouth disease (HFMD) remains an important public health problem in China. Understandings of age-specific transmission for different virus serotypes of the disease and assessment of non-pharmaceutical interventions (NPI) for HFMD are helpful for disease control, but they have been seldom considered. Here we further investigate transmission dynamics of HFMD and quantify the effects of NPIs and vaccination on the disease transmission. Methods: We extracted information of reported HFMD cases from 2009 to 2015 in East China. Age-specific force of infection (FoI) was used to describe the transmission characteristics for serotypes (EV-A71, CV-A16 and other enterovirus). We used an age-structured Susceptible-Exposed-Infectious-Removed (SEIR) model to simulate how interventions affect HFMD outbreaks. Results: 4,096,270 HFMD cases were included, and 160619 cases were confirmed for virus serotypes. The peaks of infections always occurred in even-numbered years. CV-A16 and EV-A71 showed a similar trend, children aged 1 or 2 years generally had the highest FoI, but there were no clear patterns for other enterovirus. Simulations showed that school break could dramatically decline the average incidence. When combined with social interventions, it would further reduce the incidence, but the effect is not apparent. When vaccine rate is over or equal to 20%, the incidence would be lower than taking NPIs. Conclusion: More attention should be paid to children under 2 years of age in the prevention and control of HFMD. Compared to NPIs, vaccination is more effective.
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.
How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".