AIMC Topic: Zoonoses

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Machine learning based on pangenome-wide association studies reveals the impact of host source on the zoonotic potential of closely related bacterial pathogens.

Communications biology
Variations in host species significantly impact bacterial growth traits and antibiotic resistance, making it essential to consider host origin when evaluating the zoonotic potential of pathogens. This study focuses on multiple Brucella species, which...

"The emerging pandemic threat of H5N1: Evolutionary adaptations for human transmission, zoonotic spillovers and surveillance gaps".

Gene
The increasing zoonotic potential of highly pathogenic avian influenza (HPAI) H5N1 poses a growing threat to global public health. This review examines the molecular and evolutionary mechanisms facilitating H5N1 adaptation in mammalian hosts, focusin...

Mapping global risk of bat and rodent borne disease outbreaks to anticipate emerging threats.

Scientific reports
Future epidemics and/or pandemics may likely arise from zoonotic viruses with bat- and rodent-borne pathogens being among the prime candidates. To improve preparedness and prevention strategies, we predicted the global distribution of bat- and rodent...

Machine learning assessment of zoonotic potential in avian influenza viruses using PB2 segment.

BMC genomics
BACKGROUND: Influenza A virus (IAV) is a major global health threat, causing seasonal epidemics and occasional pandemics. Particularly, Influenza A viruses from avian species pose significant zoonotic threats, with PB2 adaptation serving as a critica...

Of Lyme disease and machine learning in a One Health world.

American journal of veterinary research
OBJECTIVE: Lyme disease is a vector-borne emerging zoonosis in Ontario driven by human population growth and climate change. Lyme disease is also a prime example of the One Health concept. While little can be done to immediately reverse climate chang...

Spatio-temporal risk prediction of leptospirosis: A machine-learning-based approach.

PLoS neglected tropical diseases
BACKGROUND: Leptospirosis is a neglected zoonotic disease prevalent worldwide, particularly in tropical regions experiencing frequent rainfall and severe cyclones, which are further aggravated by climate change. This bacterial zoonosis, caused by the...

Identifying the Drivers Related to Animal Reservoirs, Environment, and Socio-Demography of Human Leptospirosis in Different Community Types of Southern Chile: An Application of Machine Learning Algorithm in One Health Perspective.

Pathogens (Basel, Switzerland)
Leptospirosis is a zoonosis with global public health impact, particularly in poor socio-economic settings in tropical regions. Transmitted through urine-contaminated water or soil from rodents, dogs, and livestock, leptospirosis causes over a millio...

Divide-and-conquer: machine-learning integrates mammalian and viral traits with network features to predict virus-mammal associations.

Nature communications
Our knowledge of viral host ranges remains limited. Completing this picture by identifying unknown hosts of known viruses is an important research aim that can help identify and mitigate zoonotic and animal-disease risks, such as spill-over from anim...

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...

Epidemiological Features of Human Brucellosis in Iran (2011-2018) and Prediction of Brucellosis with Data-Mining Models.

Journal of research in health sciences
BACKGROUND: Brucellosis is known as the major zoonotic disease. We aimed to compare the performance of some data-mining models in predicting the monthly brucellosis cases in Iran.