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Changes in Epidemiological Characteristics of Varicella and Breakthrough Cases in Ningbo, China, From 2010 to 2023: Surveillance Study

Changes in Epidemiological Characteristics of Varicella and Breakthrough Cases in Ningbo, China, From 2010 to 2023: Surveillance Study

The incidence rate significantly increased from 35.50 per 100,000 in 2016 to 70.14 per 100,000 in 2019, surpassing other legally notifiable infectious diseases like measles and rubella. Since varicella is not a notifiable infectious disease in China, the actual incidence rate is probably higher than the reported rate. This underscores the pressing need for effective prevention strategies to tackle the significant public health impact of varicella infection.

Xingqiang Pan, Yan Zhang, Xuefei Zhao, Dandan Zhang

JMIR Public Health Surveill 2025;11:e71691

Social Media and the Evolution of Vaccine Preferences During the COVID-19 Pandemic: Discrete Choice Experiment

Social Media and the Evolution of Vaccine Preferences During the COVID-19 Pandemic: Discrete Choice Experiment

Reference 20: Social media and vaccine hesitancy: new updates for the era of COVID-19 and globalized infectiousinfectious

Robbie Maris, Zack Dorner, Stephane Hess, Steven Tucker

JMIR Infodemiology 2025;5:e66081

Statistical Relationship Between Wastewater Data and Case Notifications for COVID-19 Surveillance in the United States From 2020 to 2023: Bayesian Hierarchical Modeling Approach

Statistical Relationship Between Wastewater Data and Case Notifications for COVID-19 Surveillance in the United States From 2020 to 2023: Bayesian Hierarchical Modeling Approach

The practice of using wastewater data to track pathogens has gained significant interest as an innovative method of infectious disease surveillance, with nearly 80% of the US population connected to public wastewater systems [1,2]. During the COVID-19 crisis, local health agencies started to gather and report data on COVID-19 concentrations in wastewater, using the trends in these data as an indirect indicator of SARS-Co V-2 transmission patterns.

Masahiko Haraguchi, Fayette Klaassen, Ted Cohen, Joshua A Salomon, Nicolas A Menzies

JMIR Public Health Surveill 2025;11:e68213

Dermatologic Research in Displaced Populations: Importance, Challenges, and Proposed Solutions

Dermatologic Research in Displaced Populations: Importance, Challenges, and Proposed Solutions

Dermatologic conditions, not frequently prioritized in acute care settings, represent significant disease burden and often serve as visible markers of hygiene-related issues, systemic illness, or infectious outbreaks [3]. Infectious diseases such as malaria, measles, acute respiratory infections, and diarrheal illnesses are among the major causes of morbidity and mortality [4]. Along with malnutrition (particularly in children), these problems account for the majority of deaths among displaced persons [4].

Derek Maas, Jackleen S Marji

JMIR Dermatol 2025;8:e64828

Use of Retrieval-Augmented Large Language Model for COVID-19 Fact-Checking: Development and Usability Study

Use of Retrieval-Augmented Large Language Model for COVID-19 Fact-Checking: Development and Usability Study

The success of the RAG-equipped LLMs in enhancing the accuracy and efficiency of COVID-19 fact-checking suggests that similar approaches could be adapted and refined for broader applications, including emerging infectious diseases, cancer, cardiovascular health, and dietary behaviors [33,34]. Future research should explore the effectiveness of knowledge-graph RAG systems and the comparative performance of fine-tuned versus RAG-equipped LLMs in enhancing automated health fact-checking.

Hai Li, Jingyi Huang, Mengmeng Ji, Yuyi Yang, Ruopeng An

J Med Internet Res 2025;27:e66098

Oxidative Stress Markers and Prediction of Severity With a Machine Learning Approach in Hospitalized Patients With COVID-19 and Severe Lung Disease: Observational, Retrospective, Single-Center Feasibility Study

Oxidative Stress Markers and Prediction of Severity With a Machine Learning Approach in Hospitalized Patients With COVID-19 and Severe Lung Disease: Observational, Retrospective, Single-Center Feasibility Study

As a second analytical finding, we found that the SVM model could provide a prediction of the level of severity based on a biological analysis of the level of OS in a relatively limited cohort and during an epidemic of respiratory infectious disease, with only 7% misclassified cases in the training dataset. The originality of this work is the determination of the grade of clinical severity through the analysis of specific OS biomarkers using a machine learning model.

Olivier Raspado, Michel Brack, Olivier Brack, Mélanie Vivancos, Aurélie Esparcieux, Emmanuelle Cart-Tanneur, Abdellah Aouifi

JMIR Form Res 2025;9:e66509