Satellite observed sensitivity of malaria to ENSO and AVHRR based vegetation health for short and long term forecasting in Bangladesh and India By

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1 Satellite observed sensitivity of malaria to ENSO and AVHRR based vegetation health for short and long term forecasting in Bangladesh and India By Leonid Roytman*, Mohammad Nizamuddin*, Mitch Goldberg** and Dan Mandl*** NOAA-CREST, CUNY*, NOAA-STAR**, NASA*** 1

2 Vector-Borne Diseases Malaria Dengue Dog heartworm Yellow fever 2

3 Malaria Parasite Plasmodium falciparum Plasmodium vivax Plasmodium malaria Plasmodium ovale 3

4 Malaria Vector Aedes Anopheles Culex Psorophora 4

5 Environmental factors for malaria Temperature ( C) Humidity (60%) Rainfall ( 50 mm monthly) 5

6 Impact of Malaria Bangladesh Growth penalty 1.3% Deter investments Poverty Loss of work 6

7 Goal of early detection and monitoring of malaria Use vegetation health indices to predict epidemics To assist governments To reduce the malaria risk Boosting economy 7

8 Malaria endemic districts of Bangladesh Bandarban 8

9 Climate of Bangladesh Wet hot -April to October Cool dry November to February Hot dry February to April 9

10 DATA Malaria statistics Satellite data 10

11 NOAA Operational Environmental Satellites polar orbiting satellites Geostationary satellites 11

12 Satellite Data NOAA afternoon polar orbiting satellites NOAA/NESDIS Global Vegetation Index (GVI) data set from 1992 through 2005 Spatial resolution of 4 km (sampled to 16 km) Daily temporal resolution sampled to 7-day 7 composite. 12

13 AVHRR Reflectance NDVI = (NIR-VIS)/(NIR + VIS) 13

14 Weather and Ecosystem Components in NDVI & BT 14

15 Vegetation Health Indices Normalized Difference Vegetation Index NDVI= (CH2- CH1)/(CH2+CH1) Vegetation Condition Index (VCI) VCI=100*(NDVI NDVI min)/ (NDVImax( NDVImin) Temperature Condition Index (TCI) TCI=100*(BTmax BT)/ (BTmax( BTmin) 15

16 Vegetation Health Indices Algorithm Ch1 & Ch2 Ch4 NDVI Brightness Temp Climatology VCI Vegetation Condition Index TCI Temperature Condition Index VHI Vegetation health Index 16

17 Use Vegetation Health Indices to Assess Moisture Condition (VCI) Thermal Condition (TCI) Vegetation Health (VHI) 17

18 Malaria in Bandarban Malaria Parasite Plasmodium falciparum (95%) Plasmodium vivax (5%) Female Anopheles Vectors An Dirus An minimus 18

19 Tools and Method Mat Lab SAS Trend Analysis Correlation Analysis Regression Analysis Principal Component Analysis 19

20 Annual malaria cases, and trend line Y trend = * Year DY= (Y / Y trend )*100 20

21 Correlation dynamics of DY versus TCI and VCI 21

22 Regression Analysis DY=b 0 + b 1 TCI 32 +b 2 TCI 33 + b 3 TCI 34 + b 4 TCI 35 + b 5 TCI b 22

23 Principal component analysis DY= TCI TCI TCI TCI TCI 36 23

24 Simulated and observed malaria 24

25 Annual malaria cases and trend Tripura State India Malaria cases departure from the climatology Dy =100* Actual % malaria / predicted %of malaria =100* (# of positive cases) / (# of Blood slide examined) Satellite data Box Satellite data collection

26 Correlation Dynamics of DY versus Vegetation Health Indices (Tripura) Correlation dynamics of Dy versus VH Indices VCI TCI Correlation coefficient Mar June Oct Week

27 Table 4: Observed and simulated values of Malaria in Tripura Year observed Simulated Observed Vs Simulated % of Malaria Obseved Simulated Year Independently simulated and observed percent of malaria Tripura (R 2 =.86). DY= TCI TCI TCI TCI TCI TCI 20

28 Correlation coefficient dynamics of the percent deviation of malaria from trend versus monthly SST anomaly at ENSO zone 3.4 Correlation coefficient dynamics of the percent deviation of malaria from trend versus monthly SOI anomaly (a) Correlation coefficient dynamics of TCI for 52 weeks to SST(3.4) for each month TCI-SST

29 b) Correlation coefficient dynamics of VCI for 52 weeks to SST for each month

30 (a) Correlation coefficient dynamics of TCI for 52 weeks to SOI (southern oscillation index) for each month b) Correlation coefficient dynamics of VCI for 52 weeks to SOI for each month

31 Conclusions Uses TCI and VCI for malaria prediction Model will allow to predict epidemics 1-21 months ahead (short term forecasting) ENSO can be used for long term forecasting malaria (six month ahead) Government will be able to plan ahead to fight epidemics 31

32 THANK YOU 32