GHI-Thailand 2016

Date         February 22-26, 2016

 

Venue      Faculty of Veterinary Medicine, Chiang Mai University

 

Instructors 

 

                Julio Alvarez, PhD
                         Assistant Professor – Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota

 

                Veerasak Punyapornwithaya, PhD, Ms, DVM
                        Assistant Professor, Faculty of Veterinary Medicine, Chiang Mai University

               

Course description

 

         The study of the spatio-temporal distribution of cases of disease is one of the basis of epidemiology, and ignoring the spatio-temporal dimension in disease investigations can lead to major biases and mistaken results. This course will provide the basis to understand the usefulness of Geographical Information Systems and spatiotemporal analyses to provide an increased precision to the qualitative and quantitative assessment of disease outbreaks, compare different populations/settings through the use of quantitative and objective criteria and detect significant phenomenon that may be imperceptible otherwise. Participants will be guided through the process of performing a complete outbreak investigation using GIS tools to describe a process, detect underlying spatio-temporal patterns in the presentation of disease using real and generated datasets and open-source software, and interpret the results to extract meaningful results in terms of disease management. Attendants will then replicate the whole process having to choose the most appropriate tools among those reviewed in the course, and interpreting their findings.

 

Course goals and objectives

 

          The goal of the course is to review the main principles of the most commonly used techniques for spatial and spatio-temporal clustering, and to provide the training on the use of different tools for visualizing and applying those techniques.

 

          Upon completion of this course, students will be able to:

           • Visualize spatial data using open-source software

           • Understand and apply techniques for detection of spatial and spatio-temporal clusters and spatial autocorrelation

           • Review critically spatial analyses published in the peer-reviewed literature

           • Practice writing, reporting and communicating the results of a spatial analysis

           • Practice problem solving with colleagues who have different backgrounds and areas of expertise 

 

Methods of instruction and work expectations
 

         This course combines lectures, practical exercises and one case-study. The case-study will comprise one final oral presentation. Students will be expected to spend about 1-2 hours reading materials and understanding how to use the different software tools before starting the course.

 

         Case studies will be assigned to different groups during the course. Last day all the groups will give an oral presentation.

 

     The goal of the presentation is to master the concepts learned to conduct a spatial analysis and help students practice the risk communication skills.