AIMC Topic: Disease Outbreaks

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Feature selection based multivariate time series forecasting: An application to antibiotic resistance outbreaks prediction.

Artificial intelligence in medicine
Antimicrobial resistance has become one of the most important health problems and global action plans have been proposed globally. Prevention plays a key role in these actions plan and, in this context, we propose the use of Artificial Intelligence, ...

Integration of shared-pathogen networks and machine learning reveals the key aspects of zoonoses and predicts mammalian reservoirs.

Proceedings. Biological sciences
Diseases that spread to humans from animals, zoonoses, pose major threats to human health. Identifying animal reservoirs of zoonoses and predicting future outbreaks are increasingly important to human health and well-being and economic stability, par...

Forecast of Dengue Cases in 20 Chinese Cities Based on the Deep Learning Method.

International journal of environmental research and public health
Dengue fever (DF) is one of the most rapidly spreading diseases in the world, and accurate forecasts of dengue in a timely manner might help local government implement effective control measures. To obtain the accurate forecasting of DF cases, it is ...

Location of sources in reaction-diffusion equations using support vector machines.

PloS one
The reaction-diffusion equation serves to model systems in the diffusion regime with sources. Specific applications include diffusion processes in chemical reactions, as well as the propagation of species, diseases, and populations in general. In som...

Attention-based recurrent neural network for influenza epidemic prediction.

BMC bioinformatics
BACKGROUND: Influenza is an infectious respiratory disease that can cause serious public health hazard. Due to its huge threat to the society, precise real-time forecasting of influenza outbreaks is of great value to our public.

Spatiotemporal dengue fever hotspots associated with climatic factors in Taiwan including outbreak predictions based on machine-learning.

Geospatial health
Early warning systems (EWS) have been proposed as a measure for controlling and preventing dengue fever outbreaks in countries where this infection is endemic. A vaccine is not available and has yet to reach the market due to the economic burden of d...

Machine-learning algorithms to identify key biosecurity practices and factors associated with breeding herds reporting PRRS outbreak.

Preventive veterinary medicine
Investments in biosecurity practices are made by producers to reduce the likelihood of introducing pathogens such as porcine reproductive and respiratory syndrome virus (PRRSv). The assessment of biosecurity practices in breeding herds is usually don...

Complementing the power of deep learning with statistical model fusion: Probabilistic forecasting of influenza in Dallas County, Texas, USA.

Epidemics
Influenza is one of the main causes of death, not only in the USA but worldwide. Its significant economic and public health impacts necessitate development of accurate and efficient algorithms for forecasting of any upcoming influenza outbreaks. Most...

Regional level influenza study based on Twitter and machine learning method.

PloS one
The significance of flu prediction is that the appropriate preventive and control measures can be taken by relevant departments after assessing predicted data; thus, morbidity and mortality can be reduced. In this paper, three flu prediction models, ...

Booster immunity - diagnosis of chronic hepatitis B viral infection.

Journal of infection in developing countries
INTRODUCTION: Diagnosis of chronic hepatitis B virus (HBV) infection particularly its occult form requires monitoring and repeat serological and molecular studies. The aim of the study was to investigate the possible relation between the case of a fa...