AIMC Topic: Disease Outbreaks

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Predicting Zoonotic Risk of Influenza A Viruses from Host Tropism Protein Signature Using Random Forest.

International journal of molecular sciences May 25, 2017
Influenza A viruses remain a significant health problem, especially when a novel subtype emerges from the avian population to cause severe outbreaks in humans. Zoonotic viruses arise from the animal population as a result of mutations and reassortmen...
Birds Influenza A virus Models, Theoretical Animals Disease Outbreaks Influenza in Birds Host-Pathogen Interactions Viral Proteins Orthomyxoviridae Infections Zoonoses Databases, Genetic Area Under Curve Influenza, Human Retrospective Studies Reproducibility of Results Humans Machine Learning Viral Tropism Host Specificity
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Support vector machine applied to predict the zoonotic potential of E. coli O157 cattle isolates.

Proceedings of the National Academy of Sciences of the United States of America Sep 19, 2016
Sequence analyses of pathogen genomes facilitate the tracking of disease outbreaks and allow relationships between strains to be reconstructed and virulence factors to be identified. However, these methods are generally used after an outbreak has hap...
United Kingdom Zoonoses Shiga Toxin 2 Support Vector Machine Escherichia coli O157 Escherichia coli Infections Animals Disease Outbreaks Humans Phylogeny Cattle Cattle Diseases
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Applying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza.

PloS one Jul 25, 2016
Traditional methods for monitoring influenza are haphazard and lack fine-grained details regarding the spatial and temporal dynamics of outbreaks. Twitter gives researchers and public health officials an opportunity to examine the spread of influenza...
Humans Geography, Medical Social Media United States Machine Learning Influenza, Human Public Health Surveillance Disease Outbreaks Geographic Information Systems
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Transforming Clinical Data into Actionable Prognosis Models: Machine-Learning Framework and Field-Deployable App to Predict Outcome of Ebola Patients.

PLoS neglected tropical diseases Mar 18, 2016
BACKGROUND: Assessment of the response to the 2014-15 Ebola outbreak indicates the need for innovations in data collection, sharing, and use to improve case detection and treatment. Here we introduce a Machine Learning pipeline for Ebola Virus Diseas...
User-Computer Interface Treatment Outcome Models, Statistical Hemorrhagic Fever, Ebola Sierra Leone Software Risk Assessment Disease Outbreaks Humans Machine Learning
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Salmonella infections modelling in Mississippi using neural network and geographical information system (GIS).

BMJ open Mar 3, 2016
OBJECTIVES: Mississippi (MS) is one of the southern states with high rates of foodborne infections. The objectives of this paper are to determine the extent of Salmonella and Escherichia coli infections in MS, and determine the Salmonella infections ...
Population Surveillance Models, Statistical Disease Outbreaks Humans Mississippi Social Class Geographic Information Systems Regression Analysis Salmonella Infections Socioeconomic Factors Foodborne Diseases Neural Networks, Computer
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The Large Scale Machine Learning in an Artificial Society: Prediction of the Ebola Outbreak in Beijing.

Computational intelligence and neuroscience Sep 20, 2015
Ebola virus disease (EVD) distinguishes its feature as high infectivity and mortality. Thus, it is urgent for governments to draw up emergency plans against Ebola. However, it is hard to predict the possible epidemic situations in practice. Luckily, ...
Hemorrhagic Fever, Ebola Models, Theoretical Humans Predictive Value of Tests Machine Learning Beijing Disease Outbreaks
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Quantifying the determinants of outbreak detection performance through simulation and machine learning.

Journal of biomedical informatics Nov 6, 2014
OBJECTIVE: To develop a probabilistic model for discovering and quantifying determinants of outbreak detection and to use the model to predict detection performance for new outbreaks.
Probability False Positive Reactions ROC Curve Humans Computational Biology Sensitivity and Specificity Algorithms Machine Learning Bayes Theorem Disease Outbreaks Computer Simulation
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Real-time FT-IR typing of Klebsiella pneumoniae: a flexible and rapid approach for outbreak detection and infection control.

The Journal of antimicrobial chemotherapy Jul 1, 2025
BACKGROUND: Expansion of carbapenemase-producing Klebsiella pneumoniae (CP-Kp) is driven by within-hospital transmission, requiring timely typing data for effective infection control.
Sensitivity and Specificity Disease Outbreaks Infection Control Humans Cross Infection Klebsiella pneumoniae Multilocus Sequence Typing Portugal Klebsiella Infections Bacterial Typing Techniques Spectroscopy, Fourier Transform Infrared
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An epidemiological knowledge graph extracted from the World Health Organization's Disease Outbreak News.

Scientific data Jun 10, 2025
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...
Disease Outbreaks World Health Organization Humans Artificial Intelligence
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Application of Bioinformatics and Machine Learning Tools in Food Safety.

Current nutrition reports May 19, 2025
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...
Proteomics Food Contamination Machine Learning Humans Food Microbiology Disease Outbreaks Computational Biology Foodborne Diseases Food Safety
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