1. Vulnerability & Health Climate & Climate Change 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 - 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

4. 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)

5. 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 >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

8. 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 desiccate – it uses night- time feeding and vegetation microhabitat strategies for survival

9. 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

11. 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)

25. 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

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

27. NOAA-AVHRR METEOSAT

29. Meteosat

33. Radiance & Temperature

34. >23º CGonotrophic 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 CAnopheles longevity is drastically reducedReduced lifespan of Anopheles, and fewer eggs laid 27 - 31º CPlasmodium species have the shortest development cycle Plasmodium develops quickly 15 - 20º CPlasmodium species have long development cycle Plasmodium develops slowly <15º CPlasmodium is unlikely to complete its development cycle No danger from Malaria parasites 22 - 30º COptimal 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 CMaximum tolerable temperature for all species of Plasmodium Above this temperature, Malaria epidemics are unlikely Environmental Cause and Effect (Malarial)

35. 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

36. 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

40. 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

41. 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

42. 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)

43. 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 km 2 scale at administrative units

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

48. Malaria Model prevalence and ERA rainfall University of Liverpool