Each week, we will be publishing lay abstracts of new articles from our prestigious portfolio of journals in statistics. The aim is to highlight the latest research to a broader audience in an accessible format.

The article featured today is from Statistics in Medicine, with the full article now available to read  here.

Pabon-Rodriguez, FM, Brown, GD, Scorza, BM, Petersen, CA. Bayesian multivariate longitudinal model for immune responses to Leishmania: A tick-borne co-infection study. Statistics in Medicine. 2023; 1– 17. doi: 10.1002/sim.9837

This research focuses on understanding the progression of Leishmania infantum infections in dogs and the role of the immune system in controlling the disease. The study collected data from a group of dogs, including information on severity of infection, clinical symptoms, and other factors such as age and treatment with ectoparasiticide. The findings revealed that various factors influence how the disease develops, including co-infections and the dog’s age. Additionally, the study demonstrated that the infection level at one point in time can impact future progression, particularly depending on the stage of the disease. By analyzing the collected data, the researchers were able to predict individual and aggregate patterns of Canine Leishmaniasis progression. These findings have significant implications for veterinary practice, offering opportunities to improve prevention and treatment strategies for both dogs and humans, as Leishmaniasis is a zoonotic disease that can affect both species.