Public Health

Optimising pandemic response through vaccination strategies using neural networks.

Epidemic risk assessment poses inherent challenges, with traditional approaches often failing to balance health outcomes and economic constraints. This paper presents a data-driven decision support tool that models epidemiological dynamics and optimises vaccination strategies to control disease spread while minimising economic losses. The proposed economic-epidemiological framework comprises three...

The plastisphere as a nexus for antimicrobial resistance: micro(nano)plastics in pathogen colonization, gene transfer, and global health risks.

Microplastics (MPs) and nanoplastics (NPs) have emerged as pervasive vectors of antimicrobial resistance (AMR), with the plastisphere being a microbial niche on plastic surfaces acting as a nexus for pathogen colonization, gene transfer, and global health risks. These particles adsorb antibiotics, transport pathogens, and serve as reservoirs for antibiotic resistance genes (ARGs), fostering pathog...

Severe COVID-19 in the Republic of Korea: Epidemiology, Risk Factors, Therapeutics, and Prognostic Models From Nationwide Data.

Severe coronavirus disease 2019 (COVID-19) has posed ongoing clinical and public health challenges worldwide, with Korea providing a unique perspectiv...

Artificial Intelligence Applications in COVID-19-Associated Coagulopathy: Lessons Learned.

Coronavirus disease 2019 (COVID-19)-associated coagulopathy (CAC) is a thromboinflammatory syndrome marked by endothelial injury, micro- and macrovasc...

Conditioning Public Opinion Perceptions by "Survey Methods 101": Informing, Engaging, and Motivating Individuals for Critical Processing of Public Opinion Polls.

Can we train individuals on survey methods to boost their critical processing of public opinion evidence? While polls are one of the most systematic a...

Validation of an AI-assisted screening tool for lameness detection in dairy cattle: A field-based study for preventive herd health management.

Lameness is a major welfare and economic challenge in the dairy industry, requiring effective surveillance tools for herd-level management. While visu...

Causal and spatiotemporal deep learning for dengue forecasting and extreme outbreak risk under climate variability: a framework from Vietnam.

Population-Level Digital Stroke Surveillance: Building a Fair and Accurate ICD-10 Detection Model.

Background The International Classification of Diseases, 10th Revision (ICD-10), is widely used for clinical care, quality assurance, and stroke resea...

Reusing health records from farm animal practices at scale: A potential complementary method of surveillance.

BACKGROUND: Disease in primary care frequently represents a surveillance blind spot, particularly for diseases affecting farm animals. METHODS: Electr...

Challenges and Opportunities for Improved Tuberculosis and HIV Prevention in South America.

Tuberculosis (TB) and human immunodeficiency virus (HIV) cause significant morbidity and mortality in South America. Despite the advances in active ca...

Feasibility of short-term hospital mask demand forecasting using a backpropagation neural network under data scarcity.

Early-stage infectious disease outbreaks impose acute pressure on hospital supply systems, particularly for essential protective materials such as med...

Automated classification of site-specific cutaneous photodamage using a convolutional neural network and three-dimensional total body photography.

BACKGROUND: Early detection of melanoma presents a major public health challenge. Growing evidence supports targeted surveillance of individuals at hi...

The use of smart surveillance technologies for suicide prevention in public spaces: a professional stakeholder survey from the United Kingdom.

Operationalising One Health for epidermal parasitic skin diseases: threshold-triggered mass drug administration and a minimal indicator set.

Epidermal parasitic skin diseases (EPSDs)-including scabies, pediculosis, cutaneous leishmaniasis, tungiasis and cutaneous larva migrans-affect millio...

Genomic evolution of SARS-CoV-2 delta variants pre- and post-omicron emergence using alignment-free machine learning models.

The SARS-CoV-2 Delta variant (B.1.617.2), initially classified as a variant of concern due to its enhanced transmissibility and vaccine-escape mutatio...

Cough acoustic analysis using artificial intelligence for COVID-19 detection: A comparative study of patient cohorts from Lima, Peru and Montreal, Canada.

PURPOSE: Digital cough screening for COVID-19 detection shows promise, but population differences in cough acoustics and screening accuracy require in...

Quantile regression forest-based early warning of Salmonella foodborne risk using multi-source food safety surveillance data.

Salmonella is one of the most common foodborne pathogens worldwide, with transmission closely linked to contamination across multiple food products. E...

Integrating machine learning and artificial intelligence in the management of Acinetobacter infections: a narrative review.

Acinetobacter baumannii, particularly in its multidrug-resistant (MDR) and carbapenem-resistant (CRAB) forms, has become a major global health concern...

Geospatial insights and artificial intelligence in West Nile virus disease Epidemiology: The Italy case study.

OBJECTIVES: Although artificial intelligence, including machine learning offers transformative potential, its use in infectious disease epidemiology h...

Hybrid Modeling of Cercospora Leaf Spot Epidemiology: Integrating Mechanistic and Machine Learning Approaches Using Remote-Sensing and Environmental Data.

Despite advances in modeling and sensing, no study has previously integrated mechanistic, meteorological, and uncrewed aerial vehicle (UAV) data into ...