Model Highlights New Parasitic Infection Risk Factor

Using an innovative predictive model, researchers at the University of Adelaide in Australia discovered that urban sprawl increased the transmission of parasitic worms found in rats and the parasites to humans and other forms of wildlife.

Using an innovative predictive model, researchers at the University of Adelaide in Australia discovered that urban sprawl increased the transmission of parasitic worms found in rats and the parasites to humans and other forms of wildlife.

Their study, published online in Diversity and Distributions, looked at 242 different parasitic worms in Rattus rattus species complex and R. norvegicus, 2 common species of rats. From there, the investigators analyzed which of the 242 parasites were known to infect other mammals, including humans.

Through a process called inverse Bayesian modelling, the researchers identified 781 species sharing the same parasites as the studied rats in addition to 77 parasites that were in both humans and rats.

“We discovered 32% of parasites found in rats are also found in humans. With both rat and human populations on the rise, there is concern that urban sprawl and global spread of invasive species will expose formerly isolated wildlife and their parasites to people and vice versa,” said, the study’s leader, Konstans Wells in a University of Adelaide release.

The statement also noted that some of the highlighted parasites had the potential to jump species, meaning infection is possible through the consumption of contaminated food as well as achieving infection simply by being close to an animal.

The authors praised their new model, as Wells claimed it will help predict widespread infections in disease in both humans and animals worldwide.

“We developed a model concept that allows us to link the probability of worm species occurring in wildlife and occurring in rats, and linked them to the probability of this occurring in a certain geographical area,” he said.

In light of their findings, the authors concluded that considering both changes in local pools of host species and the global distributions of parasite and pathogen diversity in consistent model frameworks may, therefore, advance the forecasting of species-level infestation patterns and the possible risk of disease emergence from local to global scale.