EG2234 Earth Observation

1. EG2234Earth Observation Health and Epidemiology

2. Topics • Analysis of a problem – e.g. malaria • Layers of information - GIS • Generation of a solution - risk mapping • MARA • Other risk maps and GIS implementations • 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. Many countries are vulnerable to diseases directly influenced by the environment • Vector-borne diseases (like malaria) • Respiratory illnesses (like meningitis) • Water-borne diseases (like cholera) • Stress illnesses (heat-stroke or hypothermia) • Illnesses caused by “mechanical” effects of extreme weather events

5. 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)

7. Anopheles!!

8. Mosquito larvae developing in water

10. Opencast mining – use of water jets

11. Irrigation for agriculture – rice cultivation

14. NOAA-AVHRR station: Addis Ababa (Ethiopia)

15. Rainfall maps from Cold Cloud Duration - Meteosat

16. NDVI and proportion of children testing positive for P. falciparum

17. Ancilliary geographical information

18. Early attempt to create risk map for malaria in Namibia

21. GIS • 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

22. Population size, location of clinics, prevalence, morbidity, mortality….etc Radiance and temperature Real-time rainfall and forecasts Vegetation types, soils and DEM

23. Risk Maps • Why create risk maps of disease? • Visual information better than tables of numbers • Transcends language and numeracy barriers • Easier to convince people • GIS maps can be used in other models • Can be updated and disseminated easily • Useful to plan future mitigation • “resource allocations for malaria interventions remain driven by perceptions and politics, rather than an objective assessment of need” (Hay and Snow, 2006: The Malaria Atlas Project)

29. Risk Map Formulation • Various factors are given a weighting according to their impact • Some information is derived from satellite images (physical and weather parameters) • Socio-economic information converted to gridded surfaces via kriging • Factors summed to generate overall risk and categories chosen to match end user

31. Russell Index • Sufficient rainfall to generate pools of water for breeding sites • Too much rainfall in a short period is likely to prevent an epidemic by destroying larvae • Russell formula used to calculate distribution of rainfall: Total Rainfall × Number of Rainy Days Number of days in the month • Quantities of rainfall required will vary according to environmental temperature (affecting rate of evaporation), as well as the surface topography and interception by vegetation.

32. Example To assess the risk weighting of NDVI according to set criteria:

33. Example Our risk criteria can be encoded into the Idrisi RECLASS function to create a new image called VEGRISK

34. Example This process can then be repeated for EACH Of your criteria……

35. Example Once an environmental risk image (env_risk) and a socio-economic risk image have been created by combining their parameters you simply combine the two to create your overall risk map

36. Malsat Map, 1999

37. Parasite rate survey results (Hay and Snow, 2006) Malaria Atlas Project

38. From Hay et al, 2004

39. Traditional active surveillance methods

40. From: Srivastava et al, 2003

41. MARA • Based in South Africa • Have been using GIS to map malaria throughout Kwa-zulu-natal district and later the continent • Information used by WHO-AFRO • Postcode-level malaria mapping • http://www.mara.org.za/

44. The Future • New satellite systems (MSG2, EnviSat, Ikinos etc) • New seasonal climate prediction models (DEMETER and Hadley Centre) • More GIS/RS skilled people in the scientific community willing to work in health!

45. Epidemiological Model • University of Liverpool has developed a malaria model driven by climate data and basic biological variables • Estimates prevalence (proportion of individuals within a population having malaria) • Written in C++ and designed to interface with existing reanalysis fields

46. Malaria Model simplified schematic of Liverpool model death death death Uninfected Infected Infectious Uninfected Infected Infectious Maturing larvae (Sporogonic cycle) Mosquito Infection Infection Human • Underlying model is similar to that described by Aron and May (1982) • Model assumes no immunity, no superinfection

47. Malaria Model prevalence and ERA rainfall University of Liverpool – Dept of Geography

48. Other maps using point  Surface interpolation is canine heartworm infections. Weightings of meteorological station data and heartworm in mosquito larvae are combined in a linear kriging interpolation scheme using ESRI ArcMap. Genchi et al, 2005

49. Meningitis mapping • Looking at past patterns of disease can provide a useful indication of existing and future risk • This ‘base’ map of relative epidemic risk (or epidemic potential) is altered by weighted evidence from up to date observations • For example, changes to humidity which is associated with bacterial transmission

50. An historical overview of meningococcal meningitis risk based on a compilation of historical epidemic information (evidence!)