AIMC Topic: Zoonoses
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Mapping global risk of bat and rodent borne disease outbreaks to anticipate emerging threats.
Scientific reports
Jul 1, 2025
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
Apr 23, 2025
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
Feb 11, 2025
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
Jan 16, 2025
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)
Aug 14, 2024
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
Jun 25, 2021
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
Feb 5, 2020
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
Dec 4, 2019
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.
A comparison of three data mining time series models in prediction of monthly brucellosis surveillance data.
Zoonoses and public health
Jul 15, 2019
The early and accurately detection of brucellosis incidence change is of great importance for implementing brucellosis prevention strategic health planning. The present study investigated and compared the performance of the three data mining techniqu...
Patchy promiscuity: machine learning applied to predict the host specificity of and .
Microbial genomics
Oct 3, 2017
and are bacterial species that colonize different animal hosts with sub-types that can cause life-threatening infections in humans. Source attribution of zoonoses is an important goal for infection control as is identification of isolates in reserv...
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