Transcript EG3213 Spatial Science & Health

Vulnerability &
Health
Climate &
Climate Change
Dr Mark Cresswell
Topics
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The ‘problem’ of malaria & health end-users
Malaria – background
GIS & Remote Sensing
Spatial and Temporal change
MARA
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The future………..
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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
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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)
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Health End Users
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The health community are better
informed about remote sensing
and climate model technologies
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Many see RS and climate
modelling as a means of
improving cost-effectiveness
>1M deaths a year
Up to 500M cases of acute illness a year
Up to 50K cases of neurological damage
a year
Up to 400K episodes of severe anaemia
in pregnancy
Up to 300K low-birthweight babies
B Greenwood (2004) – Nature Vol 430, 2004
The most fundamental environmental controlling factors
are:
Temperature (development and survival)
Rainfall (needed for mosquito breeding cycle)
Humidity (often a threshold of 60%RH is quoted)
Vegetation (linked to humidity in some ways)
the air is too dry the insect will desiccate – it uses nighttime feeding and vegetation microhabitat strategies for
survival
If
The following projected changes to our
climate will make the prevalence of diseases
such as malaria more acute:
•Enhanced precipitation in wet season
•Warmer temperatures in upland areas as temperatures rise
•Changes in vegetation patterns
•Floods in lowland areas
•Migration of refugees as a result of extreme weather
In the 2080s it is estimated that some
290 million additional people
worldwide will be exposed to malaria
due to climate change
(McMichael et al, 2003)
GIS and Remote Sensing
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
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Most layers of biologically
relevant environmental
information are combined within a
Geographical Information System
(GIS)
NOAA-AVHRR
METEOSAT
Meteosat
Radiance &
Temperature
>23º C
Gonotrophic cycle is completed within 48
hours
Oviposition, and Host seeking repeats every 2
- 3 nights
31º C
Egg  Adult cycle (Anopheles) takes 7 days
Shorter development period
20º C
Egg  Adult cycle (Anopheles)
takes 20 days
Longer development period
>35º C
Anopheles longevity is drastically reduced
Reduced lifespan of Anopheles, and fewer
eggs laid
27 - 31º C
Plasmodium species
development cycle
Plasmodium develops quickly
15 - 20º C
Plasmodium species have long development
cycle
Plasmodium develops slowly
<15º C
Plasmodium is unlikely to complete its
development cycle
No danger from Malaria parasites
22 - 30º C
Optimal temperature range for Anopheles
survivability
Lifespan of Anopheles high, so high frequency
of blood meals taken by females
Higher temperatures within optimal range
(above)
Shortens aquatic life-cycle of Anopheles from
20 to 7 days
Speeds up vector development, and so
increases chance of survival, and ability to
infect human
Higher temperatures within optimal range
(above)
Reduces time between Anopheles emergence,
and Oviposition
Permits Anopheles to lay eggs more quickly,
increasing population, and chance of epidemic
32º C
Maximum tolerable temperature
species of Plasmodium
Above this temperature, Malaria epidemics
are unlikely
have
the
shortest
for
all
Environmental Cause and Effect (Malarial)
Spatial & Temporal change
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Malaria transmission patterns follow
environmental conditions
Spatial limits set by rainfall, temperature and
vegetation
Seasonal nature of environmental factors
explains seasonal cyclicity of malaria
Malaria “season” follows rainy season
Risk Mapping
We can use a GIS to host a combined risk
model using a number of relevant
epidemiological equations – driven by
remotely sensed data
 Forecasts of possible outbreaks can be used to
assist mitigation activities
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MARA
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Mapping Malaria Risk in Africa
MARA/ARMA has provided the first
continental maps of malaria distribution and
the first evidence-base burden of disease
estimates
The Eco-System and Health Analysis
Workshop (ESHAW) in West Africa has
produced the first sub-continental malaria
transmission risk map in 1999
MARA Method
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Observed case data is collected from a wide a
geographical area as possible (historical records and
newly generated data)
All data is georeferenced and inserted into a
relational database
Geostatistical analyses are used in GIS linked to the
database to create spatial queries
Independent models are used to create a variety of
modelled indictors and risk factors
MARA Method
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Predictive modelling allows estimation of data
in areas where no empirical observations exist
Where gaps exist, interpolation methods are
used – sometimes with environmental
information as a means of weighting risk
Data used is primarily:
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Incidence
Entomological Inoculation Rate (EIR)
Parasite ratio (parasite prevalence)
MARA Method
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Objective is atlas providing seasonality,
endemicity and geographical specificity
A hierarchy of spatial scales is used:
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Continental scale (broad, climate based)
Sub-continental (uses ecological zones)
Regional or national scale (ecology and climate)
30 km2 scale at administrative units
The future…..
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Malaria Vaccine Initiative (MVI)
Funded by Bill & Melinda Gates
Artemesin based prophylactics
Improved education
Bednets and control meaures
DDT spraying
Malaria Model
prevalence and ERA rainfall
University of Liverpool