Image of Abolfazi Mollalo

Assistant Professor
Ph.D., University of Florida

Abolfazl (Abe) Mollalo

Image of Abolfazi MollaloAssistant Professor in Spatial Epidemiology

Ph.D. in Spatial Epidemiology (medical geography), Department of Geography, University of Florida, 2019

MSc. in GIS, K.N. Toosi University of Technology, 2014

Dr. Mollalo is an assistant professor in the department of public health and prevention science. He teaches a variety of courses for both undergraduate and graduate students, such as Introduction to Geographic Information System (GIS), Advanced GIS, Environmental Health, Spatial Epidemiology, Biostatistics, and Data Analysis with R programming language.

He has a methodology-oriented teaching style that helps students gain hands-on experience in implementing various tools and techniques in both ArcGIS and R environments useful for their future careers. His project-based teaching strategy uses audio-visual learning methods to solve real-world problems that keep students interested and help them gain a deeper understanding of materials taught in lectures.

Mollalo has over seven years of research experience in the application of GIS and spatial statistics for exploring geographic patterns of a variety of infectious diseases. His research interests lie primarily in spatial and space-time analysis and modeling of major respiratory diseases in the U.S., such as asthma, chronic obstructive pulmonary disease (COPD) and tuberculosis, using novel data science techniques.

In addition to his research interests on respiratory diseases, Mollalo is also interested in the geographic modeling and predictions of vector-borne diseases. His main research goal is to provide useful information for health decision-makers and local governments in monitoring and controlling the spread of infectious diseases from a geographic perspective.

Recent Publications

Mollalo, A., Mao, L., Rashidi, P., & Glass, G. E. (2019).A GIS-Based Artificial Neural Network Model for Spatial Distribution of Tuberculosis across the Continental United States. International Journal of environmental research and public health, 16(1), 157.

Mollalo, A., Sadeghian, A., Israel, G. D., Rashidi, P., Sofizadeh, A.,& Glass, G. E. (2018). Machine learning approaches in GIS-based ecological modeling of the sand fly Phlebotomus papatasi, a vector of zoonotic cutaneous leishmaniasis in Golestan province, Iran. Acta tropica, 188, 187-194.

Mollalo, A., Blackburn, J. K., Morris, L. R., & Glass, G. E. (2017). A 24-year exploratory spatial data analysis of Lyme disease incidence rate in Connecticut, USA. Geospatial health.

View Mollalo's research publications on PubMed