Acevedo, Miguel A – Quantitative Disease Ecology

Dr. Acevedo looks at ecological problems from an interdisciplinary perspective borrowing tools developed in statistics, applied mathematics and computer science to answer questions in spatial disease ecology. He develops mechanistic and statistical models that help us better understand how spatial heterogeneity influences disease spread and how disease spread influences ecosystem processes. He works with a variety host-pathogen systems that include human malaria, lizard malaria, and influenza. Currently, he is collaborating with Dr. Pérez-Hernandez developing Bayesian models of flu spread in the United States using Google Flu data. Interdisciplinary groups of undergraduate students composed of biology, mathematics and computer science will further apply this models to assess the spatial drivers of influenza spread in the United States. He is interested in catalyzing new interdisciplinary collaborations between biology, mathematics and computer science that allow us to answer emergent questions in disease ecology. He foresees teaching an interdisciplinary class for undergraduate biology students in “Applied big data science in disease ecology and evolution”, in conjunction with faculty from mathematics and computer sciences.

Dr. Acevedo brings to the project his previous experience conducting interdisciplinary science.His previous training in interdisciplinary training will allow him to serve as a bridge between disease biology and other quantitative disciplines.