PowePoint - Early epidemic detection

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PowePoint - Early epidemic detection

  1. 1. MODELS FOR EARLY DETECTION OF MALARIA EPIDEMIC IN EAST AFRICA Dr. A. K. Githeko PhD Principal Investigator
  2. 2. Epidemic detection Intervention Epidemic early prediction Risk communication Intervention Time Outbreak Epidemic Epidemic detection using clinical data Early epidemic prediction using climate data Epidemic prevented
  3. 3. Climate Drivers Increase in mean temperature Temperature variability Deforestation Swamp reclamation Typical impact on ambient temperature 0.5-1oC 1-5oC 1-2oC Water temp 2-5oC Ambient temp 1oC Effects on malaria transmission Additive effect to threshold transmission temperature 18oC 18.5-19oC 19-23oC 19-20oC 19oC Enable Epidemic Enable Enable Spatial impacts of drivers Increase in mean temp Temp variability Deforestation Swamp reclamation Regional Local Greatest impact of drivers on Plasmodium falciparum malaria occurs in the exponential phase 18-22oC of parasite development in the vector
  4. 4. NOTES  Changes in temperature affects both Plasmodium falciparum and Anopheles gambiae rates of development  Anopheles gambiae the major African malaria vector in Kenya requires at least 150 mean monthly rainfall for its population to increase in poorly drained ecosystem and 250-300 mm in well drained ecosystems. Rainfall has no effects on Plasmodium falciparum development rates.  The highlands lie in the temperature range of 16-19oC. The most sensitive development phase of Plasmodium falciparum in the mosquito lies between 18-22oC (exponential phase).  The macroclimate and microclimate drivers can interact and enhance malaria transmission.
  5. 5. MODELS DETECT CHANGES IN MEAN MONTHLY TEMPERATURE AND RAINFALL IN THE HIGHLANDS. THESE CHANGE CAN INITIATE MALARIA EPIDEMICS
  6. 6. 100 2 xER mm ii MT RT     100 2 xER mm ii xRT xRT   10018 xER mm ii xRT xRT  Additive model Multiplicative model Models using climate data 18+C model
  7. 7. KAKAMEGA 1997: EARLY EPIDEMIC PREDICTION SEQUENCE 4oC Rainfall 100.0% Epidemic risk 145.6% Case increase 1 First signal Temperature anomaly Epidemic risk confirmed EPIDEMIC OCCURES FEB-97 MAR APR MAY JUN
  8. 8. KAKAMEGA 1998: EARLY EPIDEMIC PREDICTION SEQUENCE 4OC Rainfall 81.8% Epidemic risk 330.1% Case increase First signal Temperature anomaly Epidemic risk confirmed EPIDEMIC OCCURES AUG-97 SEP OCT NOV DEC JAN_98
  9. 9. KAKAMEGA 1999: EARLY EPIDEMIC PREDICTION SEQUENCE 2OC Rainfall 40.9% Epidemic risk 272.7% Case increas e First signal Temperatur e anomaly Epidemic risk confirmed EPIDEMIC OCCURES JAN_99 FEB MAR APR MAY
  10. 10. KAKAMEGA: ADDITIVE MODEL Sensitivity 1 Specificity 1 Positive predictive value 1
  11. 11. NANDI: MULTIPLICATIVE MODEL Sensitivity 0.78 Specificity 0.99 Positive predictive value 0.86
  12. 12. AUTOMATED KAKAMEGA MODEL FOR USERS OUTPUT IN GRAPH: INPUTS ARE MEAN MONTHLY TEMPERATURE AND RAINFALL KAKAMEGA MALARIA EPIDEMIC EARLY DETECTION SYSTEM YEAR MAX TEMP INPUT COLUMM LTM max temp MAX TEMP ANOMALY RAINFALL INPUT COLUMM RAINFALL CODES TEMP ANOMALY CODES ADDITIVE MODEL: Percent risk JAN 09 29.5 28.3 1.2 163.8 1 4 #REF! FEB 30.6 29.2 1.4 8.0 0 4 18.2 MAR 31.1 29.1 2.0 125.7 0 4 18.2 APR 27.6 27.3 0.3 267.0 5 1 40.9 MAY 27.2 26.4 0.8 210.2 3 1 18.2 JUN 27.7 25.8 1.9 132.1 0 4 4.5 JUL 26.9 25.6 1.2 91.0 0 4 18.2 AUG 27.6 26.1 1.5 180.5 0 4 18.2 SEP 27.4 26.9 0.5 227.4 2 1 27.3 OCT 26.7 27.0 -0.3 124.9 4 0 22.7 NOV 27.5 26.9 0.7 98.2 0 1 0.0 DEC 29.7 27.5 2.1 178.3 0 9 4.5 JAN 10 28.8 28.3 0.6 38.7 2 1 50.0 FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Malaria early epidemic prediction: Kakamega 0 10 20 30 40 50 60 JAN 09 MAR MAY JUL SEP NOV JAN 10 MAR MAY JUL SEP NOV Time (Month) Epidemicrisk
  13. 13. OTHER DRIVERS OF MALARIA Topography and drainage
  14. 14. FLAT BOTTOMED “U” SHAPED VALLEY POOR DRAINAGE, GOOD FOR MOSQUITO BREEDING
  15. 15. 3D SATELLITE IMAGE OF “U” SHAPED VALLEY
  16. 16. NARROW “V” SHAPED VALLEY WITH FAST FLOWING STREAM
  17. 17.
  18. 18. “U” SHAPED VALLEY HAVE 2.9-FOLD MORE MALARIA MOSQUITOES THAN “V” SHAPED VALLEYS THIS AFFECTS MALARIA TRANSMISSION AND IMMUNITY TO MALARIA
  19. 19. ASSESSING MALARIA PREVALENCE AND IMMUNE RESPONSE IN “U” AND “V” SHAPED ECOSYSTEMS
  20. 20. MANY EPISODES OF MALARIA PER YEAR IN “U” SHAPED VALLEY
  21. 21. FEW EPISODES OF MALARIA IN “V” SHAPED VALLEY ECOSYSTEM
  22. 22. CSP-MSP Antibody positive children 0 0.1 0.2 0.3 0.4 0.5 0.6 MAY JUNE JULY AUG SEPT OCT NOV DEC JAN Time Proportion+ve MARANI "V" FORTTERNAN "V" SHIKONDI "plateau" IGUHU "U" EMUTETE "U"
  23. 23. SENSITIVITY OF HIGHLAND ECOSYSTEMS TO MALARIA EPIDEMIC: RAINFALL  “U” shaped ecosystems require mean rainfall of 150mm/month for mosquito populations to increase  “V” shaped valleys require mean rainfall of 250-300mm/month for mosquito population to increase
  24. 24. Vector breeding and population size in the U and V shaped valleys  The U shaped valleys has more that twice the size of breeding habitats compared to the V shaped valley  The U shaped valley has 3 time more adult Anopheles gambiae females that the V shaped valley
  25. 25. SENSITIVITY OF HIGHLAND ECOSYSTEMS TO MALARIA EPIDEMICS IMMUNE PROFILE  The ratio of immune response to malaria parasites antigens (CSP & MSP) is 1:2.2 between the “V” shaped and “U” shaped valleys ecosystem  2.2 more people in the “U” shaped valley have an immune response to malaria compared to the “V” shaped valley ecosystem
  26. 26. CONCLUSION  Human populations in the “V” shaped valleys are less immune to malaria due to lower transmission rates and low immunity  Heavy rains are required to precipitate epidemics in the “V” shaped ecosystems  Plateau ecosystems have a similar response to malaria as the “V” shaped ecosystems
  27. 27. TRAINING AND CAPACITY BUILDING FOR USE OF THE MODELS: NATIONAL EXPERTS TRAINING WORKSHOP, NAIROBI
  28. 28. PROVINCIAL TRAINING WORKSHOP: KISUMU
  29. 29. DISTRICT LEVEL TRAINING: DAR ES SALAAM TANZANIA
  30. 30. District level end user training workshop Uganda
  31. 31. MODELS USED AT THE SEASONAL CLIMATE OUTLOOK FORUM OF THE GREATER HORN OF AFRICA TO FOR SIMULATION OF MALARIA EPIDEMICS
  32. 32. MSC TRAINING  2 MSc students from Tanzania  1 MSc student from Uganda  1 MSc student from Kenya
  33. 33. Collaborators • Dr. Andrew K. Githeko (KEMRI) • Dr. Martha Lemnge (NIMR, Tanzania) • Mr. Michael Okia (MOH, Uganda) • Prof. Laban Ogallo (ICPAC) • COHESU (NGO) • Dr. John Waitumbi (WRP) • Dr. John I Githure (ICIPE) Collaborating Institutions 1. Kenya Medical Research Institute 2. National Institute for Medical Research (NIMR) Tanzania 3. Ministry of Health Uganda 4. Igad Climate Prediction and Application Centre (ICPAC) 5. Community Health Support Program (COHESU) 6. Walter Reed Project, (WRAIR) Kenya International Centre for 7. Insect Physiology and Ecology (ICIPE)

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