Spatial Science & Health Risk Mapping Dr Mark Cresswell

Topics  Introduction  Risk map components  Risk map formulation  Types of map and the future

Introduction  Why create risk maps of disease? Visual information better than tables of numbers Transcends language and numeracy barriers Easier to convince people GIS maps can be used in other models Can be updated and disseminated easily Useful to plan future mitigation “resource allocations for malaria interventions remain driven by perceptions and politics, rather than an objective assessment of need” (Hay and Snow, 2006: The Malaria Atlas Project)

RISK MAP Physical Environment Socio- Economics Human population Unstable Factors

Physical Environment Rainfall Temperature Humidity Wind Vegetation Water bodies Soils Topography Latitude Distance from coast Slope/Aspect

Socio-Economics Disposable income GDP per head Choice of healthcare Availability of clinics Medical infrastructure Budget and costs Governmental priority Level of development Politics!

Human Population Mobility of people Religion Gender issues Herd immunity Age structure Household occupancy Education War and conflict Food & nutrition

Unstable Factors ENSO Flooding Drought Fire Storms Conflict Refugee movement Ethnic cleansing Politics! Economic collapse Social behavior & lifestyle

Risk Map Formulation  Various factors are given a weighting according to their impact  Some information is derived from satellite images (physical and weather parameters)  Socio-economic information converted to gridded surfaces via kriging  Factors summed to generate overall risk and categories chosen to match end user

Russell Index  Sufficient rainfall to generate pools of water for breeding sites  Too much rainfall in a short period is likely to prevent an epidemic by destroying larvae  Russell formula used to calculate distribution of rainfall: Total Rainfall × Number of Rainy Days Number of days in the month  Quantities of rainfall required will vary according to environmental temperature (affecting rate of evaporation), as well as the surface topography and interception by vegetation.

Example To assess the risk weighting of NDVI according to set criteria:

Example Our risk criteria can be encoded into the Idrisi RECLASS function to create a new image called VEGRISK

Example This process can then be repeated for EACH Of your criteria……

Example Once an environmental risk image (env_risk) and a socio-economic risk image have been created by combining their parameters you simply combine the two to create your overall risk map

Malsat Map, 1999

Malaria Atlas Project: Use parasite rate (PR) data: “the proportion of a sampled population that is confirmed positive for malaria parasites, by identification in blood samples”

Parasite rate survey results (Hay and Snow, 2006) Malaria Atlas Project

From Hay et al, 2004

Traditional active surveillance methods

From: Srivastava et al, 2003

Risk maps can be based on point  Surface interpolation. Exploratory relative risk maps of veterinary diseases is discussed in Berke O (2005) where pseudorabies in pig herds and small fox tapeworm infections in red foxes has been explored.

Risk maps can be based on point  Surface interpolation. Exploratory relative risk maps of veterinary diseases is discussed in Berke O (2005) where pseudorabies in pig herds and small fox tapeworm infections in red foxes has been explored.

Other maps using point  Surface interpolation is canine heartworm infections. Weightings of meteorological station data and heartworm in mosquito larvae are combined in a linear kriging interpolation scheme using ESRI ArcMap. Genchi et al, 2005

Meningitis mapping  Looking at past patterns of disease can provide a useful indication of existing and future risk  This ‘base’ map of relative epidemic risk (or epidemic potential) is altered by weighted evidence from up to date observations  For example, changes to humidity which is associated with bacterial transmission

An historical overview of meningococcal meningitis risk based on a compilation of historical epidemic information (evidence!)

Evidential maps combines with areas of low humidity to provide risk and likely changes to risk as humidity patterns are altered