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Lyme disease, Ebola and malaria all developed in animals before making the leap to infect humans. Predicting when such a 'zoonotic' disease will spark an outbreak remains difficult, but a new study suggests that artificial intelligence could give these efforts a boost. A computer model that incorporates machine learning can pinpoint, with 90% accuracy, rodent species that are known to harbour pathogens that can spread to humans, researchers report this week in the Proceedings of the National Academy of Sciences1. The model also identified more than 150 species that are likely to be disease reservoirs but have yet to be confirmed as such.

The world is becoming more urban every day, and the process has been ongoing since the industrial revolution in the 18th century. The United Nations now estimates that 3.9 billion people live in urban centres. The rapid influx of residents is however not universal and the developed countries are already urban, but the big rise in urban population in the next 30 years is expected to be in Asia and Africa. Urbanization leads to many challenges for global health and the epidemiology of infectious diseases. New megacities can be incubators for new epidemics, and zoonotic diseases can spread in a more rapid manner and become worldwide threats. Adequate city planning and surveillance can be powerful tools to improve the global health and decrease the burden of communicable diseases.

Now eight years old, USAID PREDICT is a collaborative effort to monitor, predict and prevent emerging diseases, many of them zoonotic, from becoming devastating global pandemics the likes of Ebola. It's led by Jonna Mazet, an epidemiologist at the University of California at Davis's One Health Institute and School of Veterinary Medicine, and also partners with the Wildlife Conservation Society, Metabiota, EcoHealth Alliance and the Smithsonian Institution's Global Health Program.

Although rabies still causes thousands of deaths globally every year, it has essentially been eradicated from most industrialized countries. Part of the success story is due to an unusual project undertaken by the Swiss prompted by a series of large outbreaks in the 1970s: the large-scale vaccination of wild foxes to stop the epidemic. In this directed case study, students use an easily accessible MS Excel-based model to understand key epidemiological parameters of rabies outbreaks in wild foxes. The simple model allows students also to predict what proportion of foxes needs to be vaccinated to eradicate the virus from the population. The case fits into both ecological and microbiological (epidemiological) courses and offers opportunities to explore zoonotic diseases and "One Health" questions. The mathematical basis of the differential equations in the models is explained, although prior knowledge of calculus is not essential. A key learning outcome is the critical understanding of both the power and limitations of simple epidemiological models. This case was written for an online course but could also be used as a face-to-face activity if students have access to computers in class.