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Impact of Acute Respiratory Infections on Medical Absenteeism Among Military Personnel: Retrospective Cohort Study

Impact of Acute Respiratory Infections on Medical Absenteeism Among Military Personnel: Retrospective Cohort Study

Despite ongoing surveillance, gaps remain in understanding how specific ARI pathogens impact medical absenteeism and readiness in high-density military environments. A comprehensive understanding of the full spectrum of ARI pathogens is critical. While influenza is known for causing significant morbidity and mortality, HRV is typically the most prevalent pathogen and can lead to chronic infections and secondary bacterial infections, particularly in immunocompromised individuals [4].

Premikha M, Jit Khong Goh, Jing Qiang Ng, Adeliza Mutalib, Huai Yang Lim

JMIR Form Res 2025;9:e69113

Assessing COVID-19 Mortality in Serbia’s Capital: Model-Based Analysis of Excess Deaths

Assessing COVID-19 Mortality in Serbia’s Capital: Model-Based Analysis of Excess Deaths

surveillanceInfoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management Statistical Methods for Surveillance and Population Health Innovative Methods in Public Health and Surveillance

Dane Cvijanovic, Nikola Grubor, Nina Rajovic, Mira Vucevic, Svetlana Miltenovic, Marija Laban, Tatjana Mostic, Radica Tasic, Bojana Matejic, Natasa Milic

JMIR Public Health Surveill 2025;11:e56877

Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study

Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study

Despite the abundance of evidence of conducting frailty assessments using machine learning globally and in Asia, we have found no integration of data science tools and frailty screening and surveillance studies in Thailand. Most studies focused on the risk factors and their association with frailty syndrome but failed to show application in real-world settings [12,13,35,36].

Natthanaphop Isaradech, Wachiranun Sirikul, Nida Buawangpong, Penprapa Siviroj, Amornphat Kitro

JMIR Aging 2025;8:e62942

Identifying Data-Driven Clinical Subgroups for Cervical Cancer Prevention With Machine Learning: Population-Based, External, and Diagnostic Validation Study

Identifying Data-Driven Clinical Subgroups for Cervical Cancer Prevention With Machine Learning: Population-Based, External, and Diagnostic Validation Study

SCREENing enables clinicians to perform effective, risk-based screening, followed by adequate diagnosis, surveillance, and management, while empowering policy makers to optimize public health policies and resource allocation. This approach has the potential to mitigate resource shortages in LMICs, reduce delays in diagnosis and treatment, and enhance screening efficiency by focusing efforts on high-risk populations, ultimately maximizing population-level benefits.

Zhen Lu, Binhua Dong, Hongning Cai, Tian Tian, Junfeng Wang, Leiwen Fu, Bingyi Wang, Weijie Zhang, Shaomei Lin, Xunyuan Tuo, Juntao Wang, Tianjie Yang, Xinxin Huang, Zheng Zheng, Huifeng Xue, Shuxia Xu, Siyang Liu, Pengming Sun, Huachun Zou

JMIR Public Health Surveill 2025;11:e67840

COVID-19 Public Health Communication on X (Formerly Twitter): Cross-Sectional Study of Message Type, Sentiment, and Source

COVID-19 Public Health Communication on X (Formerly Twitter): Cross-Sectional Study of Message Type, Sentiment, and Source

User category and bag of words used for classification. a ICGP: Irish College of General Practitioners. b WHO: World Health Organization. c NHS: National Health Service. d ECDC: European Centre for Disease Prevention and Control. e HSE: Health Service Executive f HPSC: Health Protection Surveillance Centre. g GP: General Practitioner. h MD: Doctor of Medicine. i MP: Member of Parliament. j TD: Teachta Dála.

Sana Parveen, Agustin Garcia Pereira, Nathaly Garzon-Orjuela, Patricia McHugh, Aswathi Surendran, Heike Vornhagen, Akke Vellinga

JMIR Form Res 2025;9:e59687

Monitoring Public Health Through a Comprehensive Primary Care Database in the Netherlands: Overview of the Nivel Syndromic Surveillance System

Monitoring Public Health Through a Comprehensive Primary Care Database in the Netherlands: Overview of the Nivel Syndromic Surveillance System

The broad scope of syndromic surveillance and the rapid data collection procedures can provide great flexibility for timely assessment of different health outcomes on a large population scale [6-8]. Many countries, also in Europe, have successfully incorporated syndromic surveillance into national surveillance systems [5,9-12], the role of which was crucial during the COVID-19 pandemic, despite inherent limitations [7,8,13,14].

Christos Baliatsas, Jojanneke van Summeren, Sander van Beusekom, Amy Matser, Mariette Hooiveld

JMIR Public Health Surveill 2025;11:e58767

Building and Developing a Tool (PANDEM-2 Dashboard) to Strengthen Pandemic Management: Participatory Design Study

Building and Developing a Tool (PANDEM-2 Dashboard) to Strengthen Pandemic Management: Participatory Design Study

Therefore, we chose a dashboard approach for its flexibility and adaptability across various public health scenarios, including surveillance and training. In addition, a key design requirement was the ability to customize the dashboard view and generate reports, which was seen as a more scalable and sustainable solution compared to data storytelling, given the diverse decision-making needs of different stakeholders.

Carlos Tighe, Lem Ngongalah, Alexis Sentís, Francisco Orchard, Gheorghe-Aurel Pacurar, Conor Hayes, Jessica S Hayes, Adrian Toader, Máire A Connolly

JMIR Public Health Surveill 2025;11:e52119

Recent Use of Novel Data Streams During Foodborne Illness Cluster Investigations by the United States Food and Drug Administration: Qualitative Review

Recent Use of Novel Data Streams During Foodborne Illness Cluster Investigations by the United States Food and Drug Administration: Qualitative Review

This allows public health entities to conduct disease surveillance to help mitigate this primary limitation of laboratory-based surveillance. Consumer complaint–based systems allow for the collection and storage of consumer reports that can be submitted in a range of formats, including in-person, phone, e-mail, and web form [5].

Michael C Bazaco, Christina K Carstens, Tiffany Greenlee, Tyann Blessington, Evelyn Pereira, Sharon Seelman, Stranjae Ivory, Temesgen Jemaneh, Margaret Kirchner, Alvin Crosby, Stelios Viazis, Sheila van Twuyver, Michael Gwathmey, Tanya Malais, Oliver Ou, Stephanie Kenez, Nichole Nolan, Andrew Karasick, Cecile Punzalan, Colin Schwensohn, Laura Gieraltowski, Cary Chen Parker, Erin Jenkins, Stic Harris

JMIR Public Health Surveill 2025;11:e58797