Molecular epidemiology, coinfection, and diversity of enteroviruses detected in respiratory samples collected from patients with influenza-like illness in Hangzhou, China, in 2023
Bibliographic record
Abstract
The coronavirus pandemic in China ended in 2022, and stringent control measures were lifted in 2023. This study investigated the pathogen and endemic characteristics of enteroviruses (EVs) in patients with influenza-like illness (ILI) in Hangzhou, China, in 2023, providing a foundation for the prevention and control of EV infections. Throughout 2023, 3,480 throat swab samples were collected from hospitals across Hangzhou. Among these, 130 were positive for EVs, with a positivity rate of 3.74%. Successful sequencing and typing were achieved for 91 cases (70.00%, 91/130), which included four enterovirus A (EV-A), three enterovirus B (EV-B), and one enterovirus D (EV-D) species. EV-A was predominant in 94.51% (86/91) of the samples, followed by EV-B (4.40%, 4/91) and EV-D (1.10%, 1/91). Notably, coxsackievirus A6 was the dominant genotype detected, accounting for 60.44% (55/91) of the samples. Coinfection with 12 distinct non-EV respiratory pathogens was also observed in a significant proportion (75.82%, 69/91) of the samples. Summer and autumn represented peak periods for EV circulation, and children < 8 years of age constituted the primary demographic group affected. There were no sex-based differences in the positivity rate of EV detection (χ²=0.258, P = 0.655). Phylogenetic analyses based on partial VP1 sequences revealed that each representative strain clustered with its corresponding reference strain. Isolated CV-A4 strains were classified within subgenotype C2, CV-A6 strains within subgenotype D3, and CV-A10 strains within subgenotype C1. In conclusion, EV infections among ILI cases in Hangzhou during 2023 peaked in summer and autumn (July and October), with CV-A6 emerging as the predominant type. Children aged 0–7 years constituted the primary risk group for EV infection, with no significant sex-based differences observed. Incorporating EV screening into influenza sentinel surveillance would enhance understanding of endemic trends and pathogen characteristics in ILI patients, thereby informing evidence-based prevention policies. 1. Overall, 130 EV strains were detected in patients with influenza-like illness, and 91 strains were identified as having determined genotypes. 2. CV-A6 was the most prevalent type in 2023, followed by CV-A4. 3. EV endemics occurred mainly in the summer and autumn of 2023. 4. Children aged 0–7 years constitute the key population for the prevention and control of EV infection. 5. This study revealed 12 distinct respiratory pathogen types that exhibited coinfection with EVs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".