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Table 2 Integrated factors for malaria hotspot identification (Weight Matrix)

From: Socio-economic, epidemiological and geographic features based on GIS-integrated mapping to identify malarial hotspots

Factors Standard * weight (%) Class interval # Ranks Degrees of Vulnerability
Socio-economics
Population 8 0 – 130,000 1 Low
130,001 – 300,000 2 Moderate
300,001 – 450,000 3 High
450,001 – 1,019,383 4 Very high
Child Population (0-6 years) 6 0 – 25,000 1 Low
25,001 – 60,000 3 High
60,001 – 154,532 4 Very high
Work Force Participation 3 0 – 45,000 4 Very high
45,001 – 80,000 2 Moderate
80,001 – 330,209 1 Low
Literacy 4 0 – 100,000 4 Very high
100,001 – 150,000 2 Moderate
150,001 – 579,2280 1 Low
Epidemiology
API 12 0.00 – 0.06 1 Low
0.07 – 0.10 2 Moderate
0.11 – 0.22 3 High
0.23 – 1.05 4 Very high
Slides Collected & Examined 8 397 – 2,326 1 Low
2,327 – 2,541 2 Moderate
2,542 – 2,862 3 High
2,863 – 7,203 4 Very high
Geographical Features
Forest Cover 11 Non Forested Area 1 Low
Plantation/Grass lands 2 Moderate
Wet Tarai Swamp 3 High
Moist deciduous 4 Very high
Settlements (%) 5 Low (1.2-1.93) 1 Low
Moderate (1.94-3.25) 2 Moderate
High (3.26-8.08) 3 High
Very high (8.09-38.51) 4 Very high
Temperature (°C) 8 23.5 – 25.2 3 High
25.3 – 25.8 2 Moderate
25.9 – 26.7 1 Low
Rainfall (mm) 13 61.9 – 73.9 1 Moderate
74.0 – 85.3 2 Very High
85.4 – 105.4 3 High
105.5 – 119.7 4 Low
Water Bodies 12 Water logged 4 Very high
River/canals etc 3 High
Other Regions 1 Low
Relative Humidity (%) 10 <60 1 Low
61-70 3 High
>70 4 Very High
  1. *Based on empirical observations guided by expert’s opinion.
  2. #Natural breaks method based.