Georgetown University researchers have generated an ensemble of eight statistical models that predict host-virus associations in order to better determine which viruses could infect humans, which animals host them, and where they could emerge.
“If you want to find these viruses, you have to start by profiling their hosts—their ecology, their evolution, even the shape of their wings,” explains Colin Carlson senior author of the study published recently in Lancet Microbe. “Artificial intelligence lets us take data on bats and turn it into concrete predictions: where should we be looking for the next SARS?”
Despite global investments in disease surveillance, it remains difficult to identify and monitor wildlife reservoirs of viruses that could someday infect humans. Statistical models are increasingly being used to prioritize which wildlife species to sample in the field, but the predictions being generated from any one model can be highly uncertain. Scientists also rarely track the success or failure of their predictions after they make them, making it hard to learn and make better models in the future. Together, these limitations mean that there is high uncertainty in which models may be best suited to the task.
In the first quarter of 2020, the researcher team trained eight different statistical models that predicted which kinds of animals could host betacoronaviruses. Over more than a year, the team then tracked discovery of 40 new bat hosts of betacoronaviruses to validate initial predictions and dynamically update their models. The researchers found that models harnessing data on bat ecology and evolution performed extremely well at predicting new hosts. In contrast, cutting-edge models from network science that used high-level mathematics, but less biological data, performed roughly as well or worse than expected at random.
“One of the most important things our study gives us is a data-driven shortlist of which bat species should be studied further,” says first author Daniel Becker. “After identifying these likely hosts, the next step is then to invest in monitoring to understand where and when betacoronaviruses are likely to spill over.”