University of Florida Homepage

Fall Course: GEO3452 GEO6421 Medical Geography

This course provides a comprehensive survey of geographic topics and approaches in medical sciences, including: human ecology of diseases, research methods, geospatial techniques, landscape epidemiology, weather and climate, pollution issues, urban health, administration and planning, etc. What you will learn: -Basic concepts, principles, and methods that are widely used in medical geography studies; -A variety […]

Dr. Mao receives UF Informatics Institute Seed Fund grant

Dr. Liang Mao just received a UF Informatics Institute Seed Fund 2016 grant for his new project “Developing temporally comparable high resolution rurality maps for social and health sciences”. The project will run from September 2016 to August 2017. Dr. Mao will investigate the concept of ‘rurality’ has been widely used by policy makers and governments at […]

Potential selection bias associated with using geocoded birth records for epidemiologic research

MAO – Potential selection bias associated with using geocoded birth records for epidemiologic research Sandie Ha, Hui Hu, Liang Mao, Dikea Roussos-Ross, Jeffrey Roth, Xiaohui Xu Article first published online: 04 Feb 2016 Annals of Epidemiology DOI: 10.1016/j.annepidem.2016.01.002 ABSTRACT: Purpose There is an increasing use of geocoded birth registry data in environmental epidemiology research. Ungeocoded records […]

An individual-based rurality measure and its health application: A case study of Latino immigrants in North Florida, USA

MAO – An individual-based rurality measure and its health application: A case study of Latino immigrants in North Florida, USA Liang Mao, Jeanne-Marie R. Stacciarini, Rebekah Smith, Brenda Wiens Article first published online: 02 November 2015 Social Science & Medicine DOI: 10.1016/j.socscimed.2015.10.064 ABSTRACT: Rurality has been frequently noted by researchers as pathways to understand human health […]

Predicting Self-Initiated Preventive Behavior Against Epidemics with an Agent-Based Relative Agreement Model

MAO – Predicting Self-Initiated Preventive Behavior Against Epidemics with an Agent-Based Relative Agreement Model Liang Mao Article first published online: 31 Oct 2015 Journal of Artificial Societies and Social Simulation DOI: 10.18564/jasss.2892 ABSTRACT: Human self-initiated behavior against epidemics is recognized to have significant impacts on disease spread. A few epidemic models have incorporated self-initiated behavior, and most of […]