AIMC Topic: Disease Outbreaks

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Developing a dengue forecast model using machine learning: A case study in China.

PLoS neglected tropical diseases
BACKGROUND: In China, dengue remains an important public health issue with expanded areas and increased incidence recently. Accurate and timely forecasts of dengue incidence in China are still lacking. We aimed to use the state-of-the-art machine lea...

Enhancing Seasonal Influenza Surveillance: Topic Analysis of Widely Used Medicinal Drugs Using Twitter Data.

Journal of medical Internet research
BACKGROUND: Uptake of medicinal drugs (preventive or treatment) is among the approaches used to control disease outbreaks, and therefore, it is of vital importance to be aware of the counts or frequencies of most commonly used drugs and trending topi...

Large-scale machine learning of media outlets for understanding public reactions to nation-wide viral infection outbreaks.

Methods (San Diego, Calif.)
From May to July 2015, there was a nation-wide outbreak of Middle East respiratory syndrome (MERS) in Korea. MERS is caused by MERS-CoV, an enveloped, positive-sense, single-stranded RNA virus belonging to the family Coronaviridae. Despite expert opi...

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

International journal of molecular sciences
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...

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
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...

Applying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza.

PloS one
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...

Transforming Clinical Data into Actionable Prognosis Models: Machine-Learning Framework and Field-Deployable App to Predict Outcome of Ebola Patients.

PLoS neglected tropical diseases
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...

Salmonella infections modelling in Mississippi using neural network and geographical information system (GIS).

BMJ open
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 ...

The Large Scale Machine Learning in an Artificial Society: Prediction of the Ebola Outbreak in Beijing.

Computational intelligence and neuroscience
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, ...

Quantifying the determinants of outbreak detection performance through simulation and machine learning.

Journal of biomedical informatics
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.