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Spatial Science

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Spatial Science & Health Malaria epidemiology Dr Mark Cresswell Topics The problem of malaria & health end-users Malaria background GIS & Remote Sensing ... – PowerPoint PPT presentation

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Title: Spatial Science


1
Spatial Science Health
  • Malaria
  • epidemiology
  • Dr Mark Cresswell

2
Topics
  • The problem of malaria health end-users
  • Malaria background
  • GIS Remote Sensing
  • Spatial and Temporal change
  • MARA
  • The future..

3
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 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 die it
    uses night-time feeding and vegetation
    microhabitat strategies for survival

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  • The following 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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Factors affecting components of malaria
transmission HOST Genetic Immunological Beh
avioural PARASITE Genetic (inc. drug
susceptibility) Immunological Ecological VEC
TOR Genetic (inc. insecticide susceptibility) Be
havioural Ecological
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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)
34
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

35
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 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

41
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
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