The origin of the SARS-CoV-2 virus, which caused the latest pandemic, has not yet been determined, but hypotheses point to a market for the trade and consumption of wild animals in Wuhan, China.
Over 70% of zoonotic disease transmission events have been associated with wildlife and bushmeat. The hunting, preparation and sale of this type of fauna “has been associated with a high risk of spreading zoonotic pathogens due to contact with infectious materials from animals,” says the study led by Soushieta Jagadesh, from the Eidgenössische Technische Hochschule in Zurich, Swiss.
Therefore, the authors conducted a Mapping of Global Bushmeat Activities to improve surveillance of zoonotic spills through the use of geospatial models. And these are the places with the highest risk of spreading viruses and pathogens from animals to humans.
Map of risk of contagion of new diseases
After analyzing thousands of data, scientists discovered that the wild animals most consumed and/or illegally trafficked are even-toed ungulates (31%), primates (28%), bats (15%) and rodents.
And the red flags, with greater urgency for priority surveillance, for this type of activity are distributed in South America, the central region of Africa and Asia.
Thus, 100 urban areas with high risk were identified and it is suggested that the largest number of surveillance surveys should be carried out in Brazil, the Democratic Republic of the Congo, and Colombia. South America, with 34 surveys, had the highest projection of future priority research efforts, far more than Asia or Central America.
This is because areas like Brazil are not being policed enough. In fact, most wildlife-related activities continue to be in Central Africa and Southeast Asia.
The map, however, alerts precisely to the need for greater vigilance in the face of a latent risk of contagion of diseases from animals to humans. In fact, the participating scientists validated this map to predict Ebola risk based on previously established models, finding that bushmeat activity was an important covariate for the distribution of this disease in Africa.
In conclusion, our findings contribute to the modeling and prediction of emerging zoonoses on a global scale, indicates the study .