Vulnerability

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Vulnerability Health

• Climate
• Climate Change
• Dr Mark Cresswell

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Topics

• The problem of malaria health end-users
• Malaria background
• GIS Remote Sensing
• Spatial and Temporal change
• MARA
• 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

• The health community are better informed about
  remote sensing and climate model technologies
• Many see RS and climate modelling as a means of
  improving cost-effectiveness

gt1M 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
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• The most fundamental environmental controlling
  factors are
• Temperature (development and survival)
• Rainfall (needed for mosquito breeding cycle)
• Humidity (often a threshold of 60RH is quoted)
• Vegetation (linked to humidity in some ways)
• If the air is too dry the insect will desiccate
  it uses night-time feeding and vegetation
  microhabitat strategies for survival

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• 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

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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)
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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)
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NOAA-AVHRR
METEOSAT
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Meteosat
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Radiance Temperature
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gt23º 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
gt35º C Anopheles longevity is drastically reduced Reduced lifespan of Anopheles, and fewer eggs laid
27 - 31º C Plasmodium species have the shortest development cycle Plasmodium develops quickly
15 - 20º C Plasmodium species have long development cycle Plasmodium develops slowly
lt15º 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 for all species of Plasmodium Above this temperature, Malaria epidemics are unlikely
Environmental Cause and Effect (Malarial)
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Spatial Temporal change

• 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

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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

• 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

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MARA Method

• 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

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MARA Method

• 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
• Incidence
• Entomological Inoculation Rate (EIR)
• Parasite ratio (parasite prevalence)

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MARA Method

• Objective is atlas providing seasonality,
  endemicity and geographical specificity
• A hierarchy of spatial scales is used
• Continental scale (broad, climate based)
• Sub-continental (uses ecological zones)
• Regional or national scale (ecology and climate)
• 30 km2 scale at administrative units

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The future..

• Malaria Vaccine Initiative (MVI)
• Funded by Bill Melinda Gates
• Artemesin based prophylactics
• Improved education
• Bednets and control meaures
• DDT spraying

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Malaria Model prevalence and ERA
rainfall University of Liverpool