Public Health

Data Resource Profile: Linking electronic health and social records to study and lower health inequalities in cardiovascular diseases (BIG-HEART)

The BIG-HEART cohort was established to study and reduce health inequalities in cardiovascular disease by linking rich, multidimensional electronic health and social data across Estonia. The dataset includes all individuals aged 36 and above residing in Estonia in 2012 (N= 770,323). Its full population coverage minimises sampling and healthy volunteer bias. Existing funding and permits will suppor...

Outbreak and Postnatal Antibiotic Exposures Drive the Development Trajectory of the Nasopharyngeal Microbiota in the First Year of Life

Early exposure to antibiotics and prolonged hospitalization in preterm infants may perturb microbiome development and contribute to adverse health outcomes. Although nasopharyngeal microbiomes are linked to respiratory infections, their early development is underexplored and often assessed with 16S rRNA sequencing, which lacks species resolution. Here, we investigated nasopharyngeal microbiota dyn...

Dengue forecasting and outbreak detection in Brazil using LSTM: integrating human mobility and climate factors

Dengue fever is a major global health concern, with Brazil experiencing recurrent and severe outbreaks due to its favorable climate factors, socio-env...

Mapping Inequities in Global Vaccine Sentiment Research

Negative public sentiment towards vaccination (PSV) poses significant challenges to the effectiveness of immunization programs, with dramatic effects ...

Predicting clinical outcome of Escherichia coli O157:H7 infections using explainable Machine Learning

Shiga toxin-producing Escherichia coli (STEC) O157:H7 is a globally dispersed zoonotic pathogen capable of causing severe disease outcomes, including ...

Diagnostic accuracy of a high-throughput multiplex immunoassay for the detection of Mpox virus infection and MVA-BN vaccination up to two years after exposure

Mpox, caused by mpox virus (MPXV), has gained global attention following the 2022 Clade IIb outbreak and the emergence of two novel Clade I lineages i...

Granular Insights:A Wastewater-Based Machine Learning Approach for Localized COVID-19 Hospitalization Forecasting

Wastewater based epidemiology (WBE) is a valuable tool for monitoring emerging disease trends in a community. Specifically, early predictions of hospi...

Evaluation of the Bangkok Health Research and Ethics Interest Group: reflecting on the experiences of group members, researchers and facilitators participating in an urban community advisory board in Thailand

The Mahidol Oxford Tropical Medicine Research Unit (MORU), headquartered in Bangkok, conducts research on tropical medicine and global health. MORU wo...

Multi-model approach to understand and predict past and future dengue epidemic dynamics

Understanding the past, current, and future dynamics of dengue epidemics is challenging yet increasingly important for global public health. Using dat...

Enhancing Cause of Death Prediction: Development and Validation of ML Models Using Multimodal Data Across Multiple Healthcare Sites

Timely and accurate determination of causes of death (CoD) is essential for public health surveillance, epidemiological research, and healthcare polic...

Design and Implementation of an End-to-End AI-Driven Colonoscopy Recall Workflow at Scale

We present a real-world deployment of a large language model-powered colonoscopy recall pipeline that structured over 100,000 patient records during a...

Enhanced machine learning and hybrid ensemble approaches for coronary heart disease prediction

Coronary heart disease (CHD) remains the leading cause of mortality worldwide, disproportionately affecting low- and middle-income countries where dia...

Case-Control Matching Erodes Feature Discriminability for AI-driven Sepsis Prediction in ICUs: A Retrospective Cohort Study

Sepsis remains a leading cause of intensive care unit (ICU) mortality worldwide, and early detection is essential for improving survival through timel...

Automating Handwritten Vaccination Record Transcription with Generative Multimodal AI Models: A Proof of Concept Study from The Gambia

Handwritten home-based vaccination records (HBRs) are a vital source of immunization data, yet manual transcription in household surveys is time-consu...

Longitudinal Prediction of BMI using Explainable AI: Integrating Polygenic Scores, Maternal, Early-Life and Familial Factors

This study aimed to predict body mass index (BMI) trajectories from childhood to early adulthood using explainable artificial intelligence, integratin...

Not So Fast, I’m Serofast: Using Innovative Education Techniques to Drive Management of People with Syphilis

An increase in syphilis cases in the United States and the global shortage of Benzathine Penicillin G (BPG) calls for evidence-based optimization. Con...

Stochastic Chaos in Influenza Data – An Application of Topological Methods

Empirical methods of chaos theory have been applied to epidemiological data, uncovering evidence of chaos. In the current work, we apply, to the weekl...

Characteristics and Early Diagnosis of Motor Neuron Disease (MND) in 67 million individuals in England: a comparative study on phenotyping models derived by AI, Knowledge Graphs and the MND Association

Motor neuron disease (MND) is a rapidly progressive and fatal neurodegenerative condition, making early diagnosis critical for optimizing patient outc...

Portability of an artificial intelligence model for self-harm detection across hospital settings

Adequate self-harm surveillance is a key part of suicide prevention efforts. Our prior work has demonstrated the efficacy of an artificial intelligenc...

Associations Between Meteorological Factors and Influenza A/B Incidence in Subtropical China: A Six-Year Surveillance Study with Deep Learning Modelling for Influenza Early Warning

Influenza burden in subtropical regions like southeastern China is shaped by meteorological factors-driven complex transmission patterns that differ f...