Changes in Epidemiological Characteristics of Varicella and Breakthrough Cases in Ningbo, China, From 2010 to 2023: Surveillance Study
Notice bibliographique
Résumé
Background: Varicella is a prevalent respiratory infectious disease. Continuous monitoring is essential to understand evolving epidemiological patterns, particularly given the impact of vaccination and recent nonpharmacological interventions. Objective: This study aims to monitor the epidemiological characteristics of varicella and the changes in breakthrough cases to inform adjustments in immunization strategies and enhance prevention efforts. Methods: From 2010 to 2023, varicella incidence was monitored using active (2010-2011) and passive (2012-2023) surveillance methods. Data were obtained from the Chinese Center for Disease Prevention and Control's information system and Ningbo's Immunization Information System. The study period was divided into four intervals to analyze trends. A birth cohort (2009-2013) was established to examine breakthrough cases. A recurrent neural network model was constructed for deep learning analysis of incidence trends and the impact of nonpharmaceutical interventions. Results: Between 2010 and 2023, a total of 70,163 varicella cases were reported in Ningbo. Seasonal distribution indicated two incidence troughs before 2020 and only one from 2020 to 2023. The predominant age of onset was 10-14 years, accounting for 23.93% (16,795/70,163) of cases. From 2010 to 2013, the highest incidence was among children aged 5-9 years; from 2014 to 2019, it shifted to those aged 10-14 years; and from 2020 to 2023, it was primarily among individuals aged 15-19 years. Following the introduction of a second vaccine dose (2014-2019), incidence among children younger than 10 years of age decreased, notably by 59.54% in those aged 1-4 years. Conversely, incidence among individuals aged 10 years and older increased, particularly by 123.78% in the 15-19 years age group, with a significant upward trend (Ptrend<.001). From 2020 to 2023, although incidence rates increased across age groups 15 years and older, the rise was modest. The average annual incidence rate of breakthrough cases after one vaccine dose was 83.40/100,000 (range, 51.21-119.50/100,000), significantly higher than the 24.80/100,000 (range, 17.67-32.90/100,000) observed after two doses. However, the incidence of breakthrough cases after the first dose declined following the implementation of the 2-dose program. The median time from vaccination to breakthrough case occurrence was 27 (IQR 17.50-48) months. The recurrent neural network model demonstrated high accuracy (mean squared error, 49.96) and indicated that implementation of emergency response and community lockdown measures in early 2020 correlated with a divergence between predicted and actual case numbers, suggesting an impact of nonpharmaceutical interventions on varicella transmission. Conclusions: The significant shifts in varicella epidemiology between 2010 and 2023 highlight the importance of continuous monitoring and proactive immunization adjustments. We recommend enhanced varicella surveillance focusing on adult populations, and a targeted increase in 2-dose vaccine coverage, particularly in high-risk environments such as high schools and universities.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».