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Spatial Science & Health

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Spatial Science & Health

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  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)

  4. 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 >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

  5. 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) • If the air is too dry the insect will die – it uses night-time feeding and vegetation microhabitat strategies for survival

  6. 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

  7. Factors affecting components of malaria transmission HOST Genetic Immunological Behavioural PARASITE Genetic (inc. drug susceptibility) Immunological Ecological VECTOR Genetic (inc. insecticide susceptibility) Behavioural Ecological

  8. 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

  9. Most layers of biologically relevant environmental information are combined within a Geographical Information System (GIS)

  10. NOAA-AVHRR METEOSAT

  11. Meteosat

  12. Radiance & Temperature

  13. Environmental Cause and Effect (Malarial)

  14. 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

  15. 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

  16. 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

  17. 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)

  18. 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

  19. The future….. • Malaria Vaccine Initiative (MVI) • Funded by Bill & Melinda Gates • Artemesin based prophylactics • Improved education • Bednets and control meaures • DDT spraying

  20. Malaria Model prevalence and ERA rainfall University of Liverpool