EG2234Earth Observation

Health and Epidemiology

Topics

Analysis of a problem – e.g. malaria Layers of information - GIS Generation of a solution - risk mapping MARA Other risk maps and GIS implementations The future

Problem - Health

Health and disease often has a spatial component

Climatic, environmental and socio-economic variables affect health

Epidemics and outbreaks spread across a region – either as a function of movement of people or environmental factors

Many countries are vulnerable to diseases directly influenced by the environment

Vector-borne diseases (like malaria)

Respiratory illnesses (like meningitis)

Water-borne diseases (like cholera)

Stress illnesses (heat-stroke or hypothermia)

Illnesses caused by “mechanical” effects of extreme weather events

Problem - malaria

Malaria is a tropical disease Symptoms are caused by a parasite (of the

genus Plasmodium) Parasite is transmitted by a Vector (female

mosquito of the genus Anopheles) Malaria kills mostly children (~2M/yr WHO

estimate)

Anopheles!!

Mosquito larvae developing in water

Opencast mining – use of water jets

Irrigation for agriculture – rice cultivation

NOAA-AVHRR station: Addis Ababa (Ethiopia)

Rainfall maps from Cold Cloud Duration - Meteosat

NDVI and proportion of children testing positive for P. falciparum

Ancilliary geographical information

Early attempt to create risk map for malaria in Namibia

GIS

The problem of tackling any spatially dependent disease is more easy with a GIS system

Malaria has many layers – both natural (environmental) and socio-economic

The GIS layers paradigm allows models to be run easily

Population size, location of clinics, prevalence, morbidity, mortality….etc

Radiance and temperature

Real-time rainfall and forecasts

Vegetation types, soils and DEM

Risk Maps

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 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.

ExampleTo assess the risk weighting of NDVI according to set criteria:

If NDVI = >0.7 then VegRisk = 1If NDVI = >0.5 and < 0.7 then VegRisk = 0.6If NDVI = >0.3 and < 0.5 then VegRisk = 0.3If NDVI = < 0.3 then VegRisk = 0.1

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 EACHOf 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

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

MARA

Based in South Africa Have been using GIS to map malaria

throughout Kwa-zulu-natal district and later the continent

Information used by WHO-AFRO Postcode-level malaria mapping http://www.mara.org.za/

The Future

New satellite systems (MSG2, EnviSat, Ikinos etc)

New seasonal climate prediction models (DEMETER and Hadley Centre)

More GIS/RS skilled people in the scientific community willing to work in health!

Epidemiological Model

•University of Liverpool has developed a malaria model driven by climate data and basic biological variables

•Estimates prevalence (proportion of individuals within a population having malaria)

•Written in C++ and designed to interface with existing reanalysis fields

Malaria Model simplified schematic of Liverpool model

Mosquito

Human

Uninfected Infected Infectious

Uninfected Infected Infectious

death death death

Maturing larvae

InfectionInfection

•Underlying model is similar to that described by Aron and May (1982)

•Model assumes no immunity, no superinfection

(Sporogonic cycle)

Malaria Model prevalence and ERA rainfall

University of Liverpool – Dept of Geography

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

http://www.healthmap.org/en

http://www.who.int/health_mapping/tools/healthmapper/en/