Toward malaria risk_prediction_in_afghanistan

Najibullah Safi, MD, MSc. HPM

Malaria causes more than one million deaths every year

The spread of multi drug resistant malaria has also greatly compounded the problem

Malaria is one of the major public health problem in Afghanistan

Since 2001, new strategies for malaria control

Significant reduction in number of cases between 2002 - 2009

Tuesday, April 11, 2023 2ISPRS, Comission VIII - WG 2

Tuesday, April 11, 2023ISPRS, Comission VIII - WG 2 3

Malaria date:◦ Surveillance data collected through Health Post◦ Passive case detection◦ Parasite species were not differentiated◦ Out of 31 only 23 provinces were included in the

study ◦ Provincial malaria data spans from 2003 to 2007◦ For each province, the last 6 months data is

reserved for prediction


In this study we used three satellite-derived data:◦ Precipitation: was measured from the Tropical

Rainfall Measuring Mission◦ Land surface temperature: measured by

Moderate Resolution Imaging Spectroradiometer ◦ Normalized difference vegetation index:

measured by Moderate Resolution Imaging Spectroradiometer


Two modeling approaches were used:1. Neural Network: consists of interconnected

nodes arranged in 3 major layers: ◦ Input: 3 nodes – environmental variables ◦ Hidden: 2 nodes◦ Output: represents the level of monthly malaria

case◦ The Neural Network performance was measured

by the Root Mean Square Error


2. General Linear Model: Linear regression is widely used to predict the risk

of infectious diseases Stepwise regression method was employed – to

eliminate insignificant environmental variable predictors (P-value greater than 0.05)


Using this approach, monthly malaria cases for each province can be written as:

4 4

0 1

( ) ( ) ( ) ( ) ( )C t EV t AR t S t f t

Where, EV (t) = T (t) + NDVI (t) + P(t)AR (t) = C (t)S(t) = Sin (2∏t/T) + Cos (2Πt/T) T (t) = Average temperature in month tNDVI (t) = Average NDVI in month tP (t) = Total precipitation in month tC (t) = Total cases in month t


Neural Networks were developed for each of selected provinces

All the environmental input combinations were explored

The predicted malaria cases in general show a good agreement with the data

Example


Results show that precipitation is not a significant predictor for malaria

Normalized Difference Vegetation Index seemed to be a stronger indicator for malaria in most provinces

This result implies that malaria risk in Afghanistan is driven by irrigation, not rainfall


Using remote sensing for malaria risk prediction is an achievable goal even in a resource constrained country

Assuming the epidemiological data is reliable – models can predict cases with high accuracy

It help malaria control program for more effective malaria prevention and control

This capability can help malaria control program to efficiently allocate/use resources


Toward malaria risk_prediction_in_afghanistan

Health & Medicine

Transcript of Toward malaria risk_prediction_in_afghanistan