AIMC Topic: Disease Outbreaks

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An epidemiological knowledge graph extracted from the World Health Organization's Disease Outbreak News.

Scientific data
The rapid evolution of artificial intelligence (AI), together with the increased availability of social media and news for epidemiological surveillance, is marking a pivotal moment in epidemiology and public health research. By harnessing the capabil...

Application of Bioinformatics and Machine Learning Tools in Food Safety.

Current nutrition reports
PURPOSE OF REVIEW: Food safety is a fundamental challenge in public health and sustainable development, facing threats from microbial, chemical, and physical contamination. Innovative technologies improve our capacity to detect contamination early an...

Global Epidemiology of Outbreaks of Unknown Cause Identified by Open-Source Intelligence, 2020-2022.

Emerging infectious diseases
Epidemic surveillance using traditional approaches is dependent on case ascertainment and is delayed. Open-source intelligence (OSINT)-based syndromic surveillance can overcome limitations of delayed surveillance and poor case ascertainment, providin...

Cholera Outbreak in Nigeria: History, Review of Socioeconomic and Meteorological Drivers, Diagnostic Challenges, and Artificial Intelligence Integration.

Global health, epidemiology and genomics
Cholera continues to pose a significant public health challenge in Nigeria, driven by socioeconomic disparities, poor sanitation, and environmental factors such as recurrent flooding. This narrative review examines cholera outbreaks in Nigeria, explo...

An exploration of current and future vector-borne disease threats and opportunities for change.

Frontiers in public health
Vector-borne diseases, including dengue, threaten the health and livelihoods of over 80% of the world's population, particularly in tropical and subtropical regions. Environmental, ecological, climatic, and socio-economic factors are expected to driv...

Integrating Contact Tracing Data to Enhance Outbreak Phylodynamic Inference: A Deep Learning Approach.

Molecular biology and evolution
Phylodynamics is central to understanding infectious disease dynamics through the integration of genomic and epidemiological data. Despite advancements, including the application of deep learning to overcome computational limitations, significant cha...

Automated cooling tower detection through deep learning for Legionnaires' disease outbreak investigations: a model development and validation study.

The Lancet. Digital health
BACKGROUND: Cooling towers containing Legionella spp are a high-risk source of Legionnaires' disease outbreaks. Manually locating cooling towers from aerial imagery during outbreak investigations requires expertise, is labour intensive, and can be pr...

Predicting the transmission trends of COVID-19: an interpretable machine learning approach based on daily, death, and imported cases.

Mathematical biosciences and engineering : MBE
COVID-19 is caused by the SARS-CoV-2 virus, which has produced variants and increasing concerns about a potential resurgence since the pandemic outbreak in 2019. Predicting infectious disease outbreaks is crucial for effective prevention and control....