Najibullah Safi, MD, MSc. HPM
<ul><li>Malaria causes more than  one million deaths  every year </li></ul><ul><li>The spread of  multi drug resistant  ma...
Sunday, July 10, 2011 ISPRS, Comission VIII - WG 2
Sunday, July 10, 2011 ISPRS, Comission VIII - WG 2
Sunday, July 10, 2011 ISPRS, Comission VIII - WG 2
<ul><li>Malaria date: </li></ul><ul><ul><li>Surveillance data collected through Health Post </li></ul></ul><ul><ul><li>Pas...
<ul><li>In this study we used three satellite-derived data: </li></ul><ul><ul><li>Precipitation: was measured from the Tro...
<ul><li>Two modeling approaches were used: </li></ul><ul><li>1. Neural Network: consists of interconnected nodes arranged ...
<ul><ul><li>2. General Linear Model: </li></ul></ul><ul><ul><li>Linear regression is widely used to predict the risk of in...
<ul><li>Using this approach, monthly malaria cases for each province can be written as: </li></ul>Where,    EV (t) = T (t)...
<ul><li>Neural Networks were developed for each of selected provinces </li></ul><ul><li>All the environmental input combin...
<ul><li>Results show that precipitation is not a significant predictor for malaria </li></ul><ul><li>Normalized Difference...
<ul><li>Using  remote sensing  for malaria risk prediction is an  achievable goal  even in a resource constrained country ...
Sunday, July 10, 2011 ISPRS, Comission VIII - WG 2
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Toward malaria risk_prediction_in_afghanistan

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Toward malaria risk_prediction_in_afghanistan

  1. 1. Najibullah Safi, MD, MSc. HPM
  2. 2. <ul><li>Malaria causes more than one million deaths every year </li></ul><ul><li>The spread of multi drug resistant malaria has also greatly compounded the problem </li></ul><ul><li>Malaria is one of the major public health problem in Afghanistan </li></ul><ul><li>Since 2001, new strategies for malaria control </li></ul><ul><li>Significant reduction in number of cases between 2002 - 2009 </li></ul>Sunday, July 10, 2011 ISPRS, Comission VIII - WG 2
  3. 3. Sunday, July 10, 2011 ISPRS, Comission VIII - WG 2
  4. 4. Sunday, July 10, 2011 ISPRS, Comission VIII - WG 2
  5. 5. Sunday, July 10, 2011 ISPRS, Comission VIII - WG 2
  6. 6. <ul><li>Malaria date: </li></ul><ul><ul><li>Surveillance data collected through Health Post </li></ul></ul><ul><ul><li>Passive case detection </li></ul></ul><ul><ul><li>Parasite species were not differentiated </li></ul></ul><ul><ul><li>Out of 31 only 23 provinces were included in the study </li></ul></ul><ul><ul><li>Provincial malaria data spans from 2003 to 2007 </li></ul></ul><ul><ul><li>For each province, the last 6 months data is reserved for prediction </li></ul></ul>Sunday, July 10, 2011 ISPRS, Comission VIII - WG 2
  7. 7. <ul><li>In this study we used three satellite-derived data: </li></ul><ul><ul><li>Precipitation: was measured from the Tropical Rainfall Measuring Mission </li></ul></ul><ul><ul><li>Land surface temperature: measured by Moderate Resolution Imaging Spectroradiometer </li></ul></ul><ul><ul><li>Normalized difference vegetation index: measured by Moderate Resolution Imaging Spectroradiometer </li></ul></ul>Sunday, July 10, 2011 ISPRS, Comission VIII - WG 2
  8. 8. <ul><li>Two modeling approaches were used: </li></ul><ul><li>1. Neural Network: consists of interconnected nodes arranged in 3 major layers: </li></ul><ul><ul><li>Input: 3 nodes – environmental variables </li></ul></ul><ul><ul><li>Hidden: 2 nodes </li></ul></ul><ul><ul><li>Output: represents the level of monthly malaria case </li></ul></ul><ul><ul><li>The Neural Network performance was measured by the Root Mean Square Error </li></ul></ul>Sunday, July 10, 2011 ISPRS, Comission VIII - WG 2
  9. 9. <ul><ul><li>2. General Linear Model: </li></ul></ul><ul><ul><li>Linear regression is widely used to predict the risk of infectious diseases </li></ul></ul><ul><ul><li>Stepwise regression method was employed – to eliminate insignificant environmental variable predictors (P-value greater than 0.05) </li></ul></ul>Sunday, July 10, 2011 ISPRS, Comission VIII - WG 2
  10. 10. <ul><li>Using this approach, monthly malaria cases for each province can be written as: </li></ul>Where,   EV (t) = T (t) + NDVI (t) + P(t) AR (t) = C (t) S(t) = Sin (2∏t/T) + Cos (2 Π t/T) T (t) = Average temperature in month t NDVI (t) = Average NDVI in month t P (t) = Total precipitation in month t C (t) = Total cases in month t Sunday, July 10, 2011 ISPRS, Comission VIII - WG 2
  11. 11. <ul><li>Neural Networks were developed for each of selected provinces </li></ul><ul><li>All the environmental input combinations were explored </li></ul><ul><li>The predicted malaria cases in general show a good agreement with the data </li></ul><ul><li>Example </li></ul>Sunday, July 10, 2011 ISPRS, Comission VIII - WG 2
  12. 12. <ul><li>Results show that precipitation is not a significant predictor for malaria </li></ul><ul><li>Normalized Difference Vegetation Index seemed to be a stronger indicator for malaria in most provinces </li></ul><ul><li>This result implies that malaria risk in Afghanistan is driven by irrigation, not rainfall </li></ul>Sunday, July 10, 2011 ISPRS, Comission VIII - WG 2
  13. 13. <ul><li>Using remote sensing for malaria risk prediction is an achievable goal even in a resource constrained country </li></ul><ul><li>Assuming the epidemiological data is reliable – models can predict cases with high accuracy </li></ul><ul><li>It help malaria control program for more effective malaria prevention and control </li></ul><ul><li>This capability can help malaria control program to efficiently allocate/use resources </li></ul>Sunday, July 10, 2011 ISPRS, Comission VIII - WG 2
  14. 14. Sunday, July 10, 2011 ISPRS, Comission VIII - WG 2

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