1. Richard Kiang NASA Goddard Space Flight Center Greenbelt, MD 20771 Malaria Modeling for Thailand & Korea — NASA Techniques and Call for Validation Partners

2. Acknowledgement AFRIMS Dr. Jame Jones WRAIR Dr. Russell Coleman Dr. R. Sithiprasasna Dr. Gabriella Zollner USU Dr. Donald Roberts NDVECC Dr. David Claborn Dr. Richard Andre Dr. Leon Robert Ms. Penny Masuoka NGA Mr. John Doty DOS Mr. Andrew Herrup UC Davis Dr. John Edman Cornell Univ. Dr. Laura Harrington Mahidol Univ. Dr. S. Looareesuwan Thai MOPH Dr. J. Sirichaisinthop Dr. P. SinghasivanonMr. S. Nutsathapana Dr. S. Leemingsawat Dr. C. Apiwathnasorn RTSD Gen. Ronnachai Thai Army Lt. P. Samipagdi Dr. Kanok

3. Mekong Malaria & Filariasis Richard.Kiang@nasa.gov Malaria Cases Tak Kanchanaburi Ratchaburi Narathiwat Ban Kong Mong Tha Test Sites Ikonos Filariasis poster Field work / MahidolField work / AFRIMS Kanchanaburi Ban Kong Mong Tha Source: SEATMJ

4. DECISION SUPPORT Vector Habitat Identification: Determine when and where to apply larvicide and insecticide Identification of Key Factors that Sustain or Intensify Transmission: Determine how to curtail ongoing transmission cost effectively Risk Prediction: Predict when and where transmission may occur and how intense it may be VALUE & BENEFITS Increased warning time Optimized utilization of pesticide and chemoprophylaxis Reduced likelihood of pesticide and drug resistance Reduced damage to environment Reduced morbidity and mortality for US overseas forces and local population Mekong Malaria and Filariasis Richard.Kiang@nasa.gov Dat a -temperature -precipitation -humidity -surface water -wind speed & direction -land cover -vegetation type -transportation network -population density MEASUREMENTS Ikonos ASTER Landsat MODIS etc. MODELS Vector Habitat Model Malaria Transmission Model Risk Prediction Model

5. HABITAT IDENTIFICATION V & V TRANSMISSION PREDICTION V & V RISK PREDICTION V & V RISK ASSESSMENT SURVEILLANCE MONITORING CONTROL Vector Control Personnel Protection PROJECT OBJECTIVES INTEGRATED PEST MANAGEMENT FOR DOD INTEGRATED PEST MANAGEMENT FOR DOD

6. Objectives, Approaches & Preliminary Results Habitat identification Identifying key factors that sustain or intensify transmission Risk prediction Primary schizogony Asexual erythrocytic cycle Hypnozoites  relapses Gametocytes HUMAN VECTOR PARASITE Fertilization Oocysts Sporozoites  blood meal  oviposition  eggs  larvae  pupae  adults  destroyed  pre-patent  incubation  delay  treatment  infectious  relapse  immunity Textural-contextual classifications significantly increase landcover mapping accuracy using high resolution data such as Ikonos. Discrete Wavelet Transform is used to differentiate confusion vegetation types. Local environment Landcover Satellite & meteor. data Population database Dwelling Vector control Microepidemiology data Medical care Vector ecology Host behaviors Evaluated Thail military airborne data and established neural network rectification capability. Richard.Kiang@nasa.gov Nonparametric model computes the likelihood of disease outbreak using meteorological and epidemiological time series as input. Wavelet Transform and Hilbert-Huang Transform Empirical Mode Decomposition identify the driving variables that lead to disease outbreaks and provide more accurate predictions. Spatio-temporal distribution of disease cases Mode 1 Mode 2 Mode 3

8. Bamboo Cups Kanchanaburi

9. Washington, D.C. Space Imaging’s Ikonos imagery

10. Steps in Performing Discrete Wavelet Transform image low pass on rows high pass on rows down sample cols low pass on cols high pass on cols down sample rows approx vertical edges horizontal edges diagonal edges

11. Textural Feature Extraction using Discrete Wavelet Transform Horizontal Edges Vertical Edges Diagonal Edges H V D A square neighborhood in the imagery data A square neighborhood in the imagery data n-D entropy vector n-D entropy vector Approx

12. Class Separability with Textural Features extracted by Discrete Wavelet Transform

13. Entropy Derived from DWT as Textural Measure to Aid Classification Last 8x8 neighborhood Its WC from DWT Largest entropy 2nd largest entropy Combined with panchromatic Ikonos 1m resolution

14. North Korea – Malaria Transmission

15. Camp Greaves and Surrounding Area Kyunggi, South Korea Space Imaging’s Ikonos imagery kr4_truecolor_brightened.jpg

16. Pseudo Ground Truth Kr34_pseudogt.jpg

17. (R+G+B)/3 (N+R+B)/3 Panchromatic Intensity Space Imaging’s Ikonos imagery

18. From Cook et al. “Ikonos Technical Performance Assessment” 2001 SPIE Proceedings, Algorithms for Multispectral, Hyperspectral,..., p.94.

19. Classification Accuracy using Pan-Sharpened Ikonos Data ( 1 meter resolution) Classification Accuracy using Pan-Sharpened Ikonos Data ( 1 meter resolution)

20. Detection of Ditches using 1-meter Data (Larval Habitats of An. sinensis)

21. NDVI from AVHRR Measurements NDVI = Normalized Difference Vegetation Index AVHRR = Advanced Very High Resolution Radiometer Compiled by NOAA/NESDIS for Feb. 13, 2001 NDVI = (near infrared – red) ÷ (near infrared + red) Can be used to infer ground cover and rainfall. Can be derived from other sensors as well.

22. Post-Processing with Class Frequency Filters

23.  From a Beechcraft B200 Super King Air  Effective surface resolution approx. 1.5m Sample Image of Royal Thai Survey Department’s Airborne Instrument

24. Simulated Measurements Generated by Scanner Model Rectified open squares = real positions shaded squares = fitted positions Using Neural Network to Rectify Aircraft Measurements

25. Ban Kong Mong Tha Sanghlaburi, Kanchanaburi, Thailand Ban Kong Mong Tha Sanghlaburi, Kanchanaburi, Thailand

26. Anopheles dirus forest; shaded pools; hoofprints in or at the edge of forests; with increasing deforestation, adapting to orchards, tea, rubber and other plantations. An. minimus forest fringe; flowing waters (foothill streams, springs, irrigation ditches, seepages, borrow pits, rice fields); shaded areas; grassy and shaded banks of stable, clear, slow moving streams. An. maculatus seepage waters; streams pools; pond edges; ditches and swamps with minimal vegetation; sunlit areas.

27. An. dirus An. minimus

29. TRANSMISSION MODEL Local EnvironmentLandcover Satellite & Meteor. Data Population Database Dwelling Vector Control Microepidemiology Data Medical Care Vector Ecology Host Behaviors Primary Schizogony Asexual Erythro. Cycle Hypnozoites  Relapses Gametocytes HUMAN VECTOR PARASITE Fertilization Oocysts Sporozoites  blood meal  oviposition  eggs  larvae  pupae  adults  destroyed  pre-patent  incubation  delay  treatment  infectious  relapse  immunity Spatio-Temporal Distribution of Disease Cases

30. hx, hy, hproof rsex, rage, rimmune, revout, rgamet bx, by tegg, tlarva, tpupa, tmate, tovi, tspor wbtoh, whtoh, whtob mage, mspor tincub, twait, tgamet, theal, tpost, trelapse

33. 2-Year Prediction of Malaria Cases Based on Environmental Parameters (temperature, precipitation, humidity, vegetation index) Tak, Thailand

34. Landsat TM Image over Mae La

35. Mae La Camp

37. Sources: CDC DVBID Rutgers Univ. Entomology Dept./NJMCA

38. Airborne Remote Sensing ER-2 Fleet In late 19 th Century … ProteusHelios Altair

39. Neural Network Classification of GER 63-channel Scanner Data ArchitectureTraining Acc. Rel. Classif. Acc. 1 hidden layer with 1 node 88.4185.52 1 hidden layer with 3 nodes 99.0797.93 1 hidden layer with 5 nodes 98.8697.52 2 hidden layers each with 3 nodes 99.0797.62 2 hidden layers each with 5 nodes 99.3897.83

40. 1985-1999 SIESIP ½°×½° temp, precip 1985-2003 NCEP 2½°×2½° rel. humidity 1985-2000 AVHRR PF 8×8 km² NDVI 1999-2003 MODIS 8×8 km² NDVI 1999-2003 MODIS 5×5 km² surface temp, lifted index, moist., etc. 2000-2003 SIESIP ½°×½° temp, precip 1998-2003 TRMM ½°×½° precip

41. Time-Frequency Decompositions Dengue Cases – Kuala Lumpur Fourier Transform Hilbert-Huang Transform Wavelet Transform

42. RISK PREDICTION MODEL Nonparametric model computes the likelihood of disease outbreak using meteorological and epidemiological time series as input. Wavelet Transform and Hilbert- Huang Transform Empirical Mode Decomposition identify the driving variables that lead to disease outbreaks and provide more accurate predictions.

43. NASA Goddard Space Flight Center Landsat-1 MSS Space Imaging’s Ikonos imagery

44. NASA/GSFC – Close-Up Pan: 1m MS: 4m Space Imaging’s Ikonos imagery

45. 2-Year Prediction of Malaria Cases Based on Environmental Parameters (temperature, precipitation, humidity, vegetation index) Ratchaburi, Thailand

46. Ban Kong Mong Tha Sanghlaburi, Kanchanaburi, Thailand