Michael Levy, PhD, assistant professor of Biostatistics and Epidemiology, at the Perelman School of Medicine, University of Pennsylvania, along with other collaborators from Penn, Johns Hopkins University, the Centers for Disease Control and Prevention, and Universidad Peruana Cayetano Heredia in Peru, are in the trenches combining tried-and-true epidemiological approaches with new statistical methods to learn more about the course of a dangerous, contagious disease epidemic. Their research was published last week in PLoS Computational Biology.
Chagas disease, primarily seen in South America, Central America, and Mexico, is the most deadly parasitic disease in the Americas. Caused by the protozoan parasite, Trypanosoma cruzi, it is spread chiefly via several species of blood-sucking triatomine insects. After an initial acute phase, the disease continues to lurk in the body and can eventually cause a variety of chronic life-threatening problems, particularly in the heart. Although there are some drugs to treat Chagas disease, they become less effective the longer a person is infected. The lack of a vaccine also means that the only effective way to control the disease is to control the disease vectors.
Because the chronic effects of Chagas disease can take decades to manifest themselves, tracking the development and progression of epidemics has been a challenging problem. In the past, Chagas disease was known mostly in rural regions, but has been spreading into more urbanized areas over the last 40 years. Levy’s team has been focusing on one of those areas in the city of Arequipa, Peru.
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