Over the past three decades, the rate at which newly developed wildlife diseases infect humans has progressively climbed. Viruses like the current monkeypox outbreak and the worldwide coronavirus pandemic have made it even more important to find ways to predict when and where disease outbreaks from host-pathogen systems are likely to happen.
An approach that will do just that—predict disease transmission from wildlife to humans, from one animal species to another, and identify who is at risk of infection—was developed with assistance from an assistant professor at the University of South Florida.
A machine-learning methodology is used to determine how factors like geography and the environment affect identified diseases. The algorithm can locate community hot spots that are in danger of infection both locally and globally with very little information.
According to co-principal investigator Diego Santiago-Alarcon, an assistant professor of integrative biology at USF, our main objective is to create this tool for preventive actions. With this research, we get closer to our hard-to-achieve goal of making a model that can be used to predict infections in all of the different parasite systems.
Santiago-Alarcon examined three host-pathogen systems, avian malaria, birds with West Nile virus, and bats with coronavirus—to test the dependability and accuracy of the models produced by the methodology. She did this with assistance from researchers at the Universidad Veracruzana and the Instituto de Ecologia, both in Mexico.
The researchers discovered that for the three systems, the species that was infected the most frequently was not always the one that was most susceptible to the illness. It was crucial to establish pertinent elements, such as environmental and evolutionary links, in order to more accurately identify hosts who were at a higher risk of infection.
By combining geographic, environmental, and evolutionary development characteristics, the researchers were able to find host species that had never been reported as being affected by the parasite they were studying. This gives us a way to find susceptible species and, in the long run, reduce the risk of disease.
According to Santiago-Alarcon, “We are sure that the methodology is effective and that it can be broadly used for a variety of host-pathogen systems.” We are now moving into a time of development and enhancement. ”
The findings, which were reported in the Proceedings of the National Academy of Sciences, demonstrate that even with limited data, the methodology can produce accurate global forecasts for the host-pathogen systems under study. This new idea will help focus fieldwork and monitoring activities for infectious diseases. It will also provide a practical way to use limited illness resources in the best way possible.
It is difficult but vital to predict what kind of pathogen will cause the subsequent human or veterinary infection. As human impact on natural environments accelerates, so will the potential for new diseases.
According to Andrés Lira-Noriega, research fellow at the Instituto de Ecologia, “Humanity, and indeed biodiversity in general, are experiencing more and more infectious disease challenges as a result of our incursion and destruction of the natural order worldwide through things like deforestation, global trade, and climate change.” This makes it important to have tools, like the one we are putting out, that can help predict where new disease risks might appear or start to spread.
The group intends to carry out more research to extend the investigation of disease transmission to forecast future outbreaks and test the methodology on new host-pathogen systems. By the end of 2022, scientists should be able to use an app to easily connect to the instrument.
