Delivering Tropical Medicine Solutions with Integrated Open Source GIS and Statistics.
Presentation | Presented
- Barry Rowlingson, Lancaster University
Complex statistical and data analysis tools are useless unless they can get into the hands of people who need to use them. Sixteen years ago integrating spatial statistical methods into GIS meant kludging something like ESRI's Arc/Info GIS to talk to Insightful's Splus statistics package using a mix of Arc Macro Language and shell scripts. Such systems were weakly integrated between the packages and often slow and difficult to use.
These days with open source solutions we can do much better. Using Quantum GIS for desktop mapping and R for the statistics engine connected by Python, we have developed "Arlat", a tool for fieldworkers in Africa to rapidly assess the risk of Mectizan medication side-effects due to the presence of Loa-loa parasites.
This work, funded by the World Health Organisation through the APOC programme, gives field workers the power to combine satellite environmental data and medical surveys to predict the risk of adverse reactions to Mectizan treatments. Maps of these areas allow workers to identify low-risk regions where treatments can be freely given, high-risk regions where treatments must be made under more careful supervision, and uncertain regions where more survey data may need to be collected.
We will give an outline of how GIS and spatial statistics has changed over the years and show how we developed the Arlat system.