Matt Krupoff

Final Project

Malaria Risk Map and Site Suitability for Malaria Clinics

In Northeast India

Abstract:

This study uses Geographic Information Systems (GIS) to assess high risk areas for

malaria in Northeast India given a set of mosquito vector habitat parameters. From this

assessment, Indian Census data is used to determine the number of towns that are located

within high-risk area, and designating them as suitable sites for malaria clinics. The model I

use is a weighted-index model that incorporates climate, land-use, topographic, and

hydrologic variables. The results indicate that 71 towns in the sample states were located

in high-risk areas. Malaria clinic sites should then be allocated to these towns in order to

maximize coverage.

Part I: Introduction

Malaria is an infectious disease that places a huge burden to developing countries

around the world. In 2013, there were about 198 million cases of malaria, and an estimated

5840,000 deaths, most being children under 5 years old. The disease is caused by

Plasmodium parasites that mature inside mosquito vectors of the genus Anopheles. The

disease is spread to humans exclusively through the bites of these “malaria vectors” (WHO,

2014). It is estimated that 3.4 billion people are at risk, of which 1.2 billion are at high risk-

defined as more than one malaria case per 1000 population.

In India there are over 2 million confirmed cases of malaria annually, and over 1,000

deaths. Out of 2.5 million cases in Southeast Asia; this amounts to around 80% of total

cases in the region (NIMR). It is one of the top causes of direct or indirect infant, child, and

adult mortality in the country. A treatise written by Sinton(1935), attributed malaria in

India as “the most important cause of economic misfortune, engendering poverty, lowering

the physical and intellectual standards of the nation, and hampering prosperity and

economic progress in every way.” The economic burden is indeed high because of disability

during attacks, relapses, and re-infections. Kumar et al (2007), showed that the disability

adjusted life years lost due to malaria in this region were 1.86 million years. By some

estimates, this can account for over US$1,800 million a year PPP.

Malaria can be prevented and eliminated with the right interventions. Insecticide

treated bed nets (ITN), are significant tools that have been used in aid interventions and

government sponsored elimination and prevention programs. They have been shown to

reduce the death of children 5 years and under by 20% (CDC, 2014). ITN’s prevent the

transmission of the disease by protecting the user, as well as eliminate the mosquito

vectors themselves. Thus ITN’s have positive externalities for the community. Prompt

access to effective treatment can further reduce deaths, however many people continue to

die because they are unable to access life-saving treatment within 24 hours of the onset of

symptoms. If there were more malaria clinics that offer these treatments and ITN

distribution, then the mortality rate will be greatly lowered. The issue is where the clinics

should be placed in order to maximize coverage benefits.

In order to know the most efficient placement of malaria clinics, it is important to

know which areas are most at risk given the climate, and proximity to wet areas, land-use,

and population. This study leverages Geographic Information Systems (GIS) and a weighted

index model that incorporates these variables to determine areas in northeast India that

are most suitable or malaria vector habitats. The riskiest areas will be where malaria

clinics should be placed in order to maximize coverage. The first part of the study will be a

general risk map of Northeast India for the states of Madhya Pradesh, Chhattisgarh, Uttar

Pradesh, Jharkhand, and Bihar. The second part will use the same variables plus the

population density from a set of geocoded towns in the state of Uttar Pradesh. This second

part will provide a more accurate representation of where malaria clinics should be placed.

Part II will discuss the data sets used for this analysis, including the variables used

to determine suitable habitats. Part III will be about the methods used, including

descriptions of the weights I used for the weighted index model. Part IV will discuss the

methods used to create the weighted index model. Part III will have the results.

Part 2: Data

The data chosen for this study was based on both the habitat preferences that

mosquito vectors have, and the conditions that the Plasmodium need to grow inside of

them. Temperature, land-use, proximity to wet areas, elevation, and vegetation were the

five variables used to meet these preferences. Precipitation was not used because more

studies did not find any correlation with malaria prevalence.

2.1 Temperature

According to Alemu, et al. (2011), both the daily survival of mosquito vector, and the

development of the parasite within the mosquito are dependent on temperature.

Therefore, mean temperature data for India was invaluable in evaluating risky areas.

The temperature data used in this study was acquired from WorldClim.org, an open

source database for climate data. It was generated through interpolation of average

monthly climate data from weather stations around the globe. The raster file was in 30 arc

second resolution, which is approximately 1 square kilometer resolution.

The optimal temperature range for mosquito vectors is 16 to 28 degrees Celsius

where the daily survival rate is measured to be about 90%. At 28 degrees Celsius, the

lifecycle for the parasite takes about 9 to 10 days, but development stops at temperatures

higher than 30 degrees Celsius and below 16 degrees Celsius (Alemu, et al., 2011). Average

monthly temperature was used to account for temporal changes in temperature

throughout the year. Figure 1 shows the temperature range for northeast India. The range

was from -22.2 degrees Celsius to 23.4 degrees Celsius. Of these, there were 639,849

square kilometers that were in the suitable range.

2.2 Landuse

India’s agricultural sector accounts for 13.7% of GDP, and employs 50% of the total

workforce. The largest crop is rice, which is grown in patties with a lot of standing water.

Since mosquitos prefer to lay eggs in standing water, this poses a huge risk to those areas,

and the people who live there. In addition, the growth of irrigation networks throughout

the country is increasing the number of suitable habitats as well. Therefore when creating a

risk map, it is essential to include information on the land-use across the country.

The land-use data in this study was acquired from NASA’s Anthropogenic Biomes of

the World, v1 (2001-2006). The mapping used a multi-stage procedure based on

population (urban, non-urban), land-use, and land-cover. The categories that are included

are: rice villages, urban areas, dense settlements, rain-fed villages, rain-fed mosaics,

irrigated villages, residential irrigated cropland, populated rain-fed cropland, residential

irrigated cropland, populated irrigated cropland, cropped pastoral land, populated forests,

remote forests, and remote rangeland (Figure 2). The largest land-use was rain-fed villages

at 13682 square kilometers, followed by irrigated villages at 8191 square kilometers, and

rice villages at 6877 square kilometers.

2.3 Vegetation

Another variable used in this study was vegetation, otherwise known as general

forest cover, or “greenness” of the given area. The data was acquired from the National

Remote Sensing Centre using an Oceansat-2 Ocean Color Monitor Sensor. The values

represent vegetation fractions that are different from the conventional Normalized

Difference Vegetation Index in that it is a scale of 0-250 instead of -1 to +1. Areas with no

vegetation cover had a value of zero. The resolution was 30 arc seconds, re-projected to

1000 meters (Figure 3).

Intuitively you would think that the more forested the area, the more suitable the

area is for malaria vectors. However, forested areas tend to lower water temperature that

slows the development of for mosquito larvae. Therefore, heavily forested areas are not

conducive to higher malaria risk.

2.4 Elevation and TWI

The elevation data used in this study was acquired from the Shuttle Radar

Topography Mission (SRTM), at 30 arc seconds. The raster was for the Southeast Asian

region, and the values represented meters above sea level (Figure 4).

Elevation data is valuable for two reasons. The first is that malaria vectors prefer a

certain range that is anything below 3000 meters. Anything above 3,000 meters would not

be suitable habitat. The second reason is that I can derive a Topographic Wetness Index

from this DTM. A topographic wetness index (TWI) is an estimate of predicted water

accumulation in a defined area. It describes the shape of the land at any given point on the

landscape as the ratio of uphill area from which water would flow into that point to the

local slope at that point. The higher the index, the more we would expect water would

accumulate at that point (Figure 5). This is useful because it allows us to map where

standing water would be, as well as stream networks. In order to calculate the TWI, I first

had to calculate flow direction, and flow accumulation. Then I calculated the flow area and

imputed it along with a slope layer to get the TWI.

2.5 Population Data

The population data that was used in this study came from the 1981 Town Directory

Census Tables. The list of towns included in these tables was geocoded using an online

service called “Indian Place Finder”. The variables included in the town directory included

population, literacy rates, working categories, and scheduled classes. The decision to focus

only on the selected states in northeastern India was based on only having the geocoded

towns from the Town Directory. The variables of interest are the population figures of each

town. Areas with higher population densities will have higher risk levels.

Part 3 Methods

Malaria risk is determined in this study by a weighted index model. In addition to

ranking values within each contributing layer, a weighted index model assigns weights to

the layers themselves relating to their importance. It is the optimal method of determining

high risk areas because certain parameters are more preferred by mosquito vectors. For

the first risk map, I am using temperature, vegetation, land use, elevation, and TWI as my

parameters. For the second risk map of the state of Uttar Pradesh, I will also use population

density as a parameter. In both cases, I will isolate the highest risk areas and designate

them as suitable sites for malaria clinics.

3.1 Within Layer Ranks

Each layer’s ranks were in increments of two that corresponded with

increasing importance with the max value being 10. I used the “reclassify” tool to divide

each layer’s values into equal intervals, then ranked each interval according to their

importance.

Table 1 shows the ranking system for the temperature layer. The ranks of zero were

chosen because at that low of temperature the parasites do not develop. The assumption

under this ranking system is that as the temperature gets warmer, the faster the breeding

cycle for malaria vectors, and parasite development. The assumption for vegetation (Table

2) is that more forest cover lowers surface water temperatures which impedes on the

development of malaria vectors. Table 3 shows the ranking system for elevation. Since,

elevations above 3000 meters is not conducive to malaria vector breeding cycles, I assigned

it a zero. As the elevation decreases, malaria vector prevalence is likely to increase. The

Topographic Wetness Index (Table 4) is assumed to be positively correlated with malaria

vector prevalence since mosquitos breed in water bodies. The TWI is where we would

expect water to be so after a monsoon season, water will accumulate in areas with higher

TWI values, and provide suitable habitats for malaria vectors. The land use ranking (Table

5) was categorical. I assigned higher ranks for croplands, specifically rice villages. Rice

croplands consist of standing water and are very suitable habitats. Irrigation channels are

also known to be suitable habitat for malaria vectors, but also rain fed villages that rely on

wells where mosquitos can breed as well.

3.2 Weighted Index Model

The next step after reclassifying the layers was to assign weights to each individual

layer that corresponded to their relative importance to malaria vector habitats. Once those

weights were established within Raster Calculator, I then divided by the max total value,

which then gave me the weighted index value of malaria risk.

I assigned land use and TWI the heaviest weights since malaria vectors need aquatic

breeding grounds. Since land-use and TWI were parameters related to water bodies, and

general wetness of the land, they were assigned the highest ranks. Mosquitos and parasite

lifecycles are also dependent on climate, so temperature was assigned a medium weight.

Vegetation and elevation were assigned a small weight since their correlations are assumed

to be rather small.

3.3 Uttar Pradesh

A large part of this study was looking at the state of Uttar Pradesh which is the state

taken from the Indian Census Town Directory. The data includes different variables

pertaining to the population including literacy rates, working categories, and

unemployment. Since this data was only available for this state, a separate analysis was

conducted to see which areas are most at risk. Since this data set had actual population

numbers, I was able to conduct a kernel density function to calculate the population density

within a 25 kilometer kernel. When it comes to malaria risk, high populations will be more

conducive to malaria vector survival and rate of infection, so malaria clinics should be

located within these dense areas. I then created a series of scatter plots looking for any

kind of correlation between the malaria risk index that I calculated and population

characteristics. Specifically I plotted risk against illiteracy rates, unskilled workers, and

unemployment to see if malaria risk was at all correlated with poverty.

Part 4 Results

The results of this study showed that the maximum risk level was 0.92, and the

minimum was 0.079 (Figure 1). The total area of high risk areas, risk index greater than or

equal to 78, was 5,161 results of this study showed 1,763 square kilometers. When the

values were extracted to geocoded towns, we see that the town of Ramnagar in the

Varanasi sub-district is the most at risk, with an index value of 0.88. Malaria clinics should

ideally be set up in high risk towns with risk values over a certain threshold, in this case

greater than or equal to index values of 0.78. I have identified 50 towns within the sampled

states that are located in these high risk areas. Malaria clinics should therefore be allocated

to these locations in order to maximize benefits of ITN distribution and emergency care.

For the state of Uttar Pradesh the highest index value was 0.86, and the lowest was

0.24. Again, this is including population density into the model. The town of Allahabad is

most at risk with an index value of 0.759. Since the risk within Uttar Pradesh was relatively

lower I lowered the threshold when designating high risk areas. The total area that are at

high risk, in this case greater than or equal to 55, is 1367 square kilometers. I found that

there were 71 towns within these risky areas. Malaria clinics should therefore be placed in

these towns in order to maximize coverage. When the risk values were plotted against a

poverty index, which is calculated from the sum of the illiteracy rate, non-employment rate,

and unskilled labor rate over 3, we see a slightly downwards trend (Figure 10). This is

counterintuitive, but difficult to explain without more information

Part 5 Discussion

A large part of the limitations to this study was in fact the lack of information.

Biological and environmental parameters for mosquito habitats come with a great deal of

uncertainty. For example, precipitation was not included in this study because of the

variable results from different studies- so much so that it was not worth including. The

parameters with the most correlation from the literature were temperature and TWI; but

even then, accurate intervals were still mostly subjective based on correlated assumptions.

The within ranking system for elevation and temperature were modelled from the range

that I found from the literature, but the rest of the values were mostly just equal intervals

without concrete evidence to specify.

Another limitation was the scale of the study area, and the resolution of the raster

sets. The validity of the study would improve with smaller, finer scales. For example,if the

scale was at the household, or village level, then I could use proximity analysis to find the

distance to the nearest max TWI point, and judge that as an important parameter. Malaria

vector travel distance is approximately 1 kilometer from the breeding source. Since the

resolution size of all of my layers was 1 square kilometer, this made it impossible to do

proximity analysis that would have been helpful. The 1 km resolution was useful in some

regards, such as for the land-use data, but it was too coarse for the TWI, which was a

significant variable. TWI would have been hugely impactful if the scale of this study was

finer.

Another limitation was that my geocoded towns did not encompass their associated

areas on the map, but were only represented by points. Therefore, regardless of the size of

the towns, the risk value was only the value at that exact location of the point. This is a huge

limitation, because if a town is surrounded by cells with high risk values, but was placed on

a low risk cell, valuable information would have been lost. A more efficient way of doing

this would have been to set up a buffer around each point corresponding to that town area,

and extracting the highest risk value within it. Unfortunately due to time constraints I could

not determine this, and valuable information was left out.

This type of research design can be very valuable for NGO’s or government

programs that aim to eradicate or alleviate malaria. If future studies had more detailed

information on household, village, or even sub-district scale, a weighted index model could

be a powerful way to combat this disease.

Tables and Figures

Table 1

Intervals New Ranks

(-22.2)- (14.9) 0

14.9-16.0 2

16.0-17.0 4

17.0-18.2 6

18.2-19.7 8

19.7-23.4 10

Table 2

Intervals New Ranks

0-50 10

50-100 8

100-150 6

150-200 4

200-250 2

Table 3

Intervals New Ranks

18 10

18-500 10

500-1000 8

1000-1500 6

1500-2000 4

2000-2500 2

2500-3000 2

3000-4926 0

4926-6768 0

Table 4Intervals New Ranks(-0.2408)-(1.960) 21.96-4.16 24.16-6.36 46.36-8.56 48.56-10.76 610.76-12.96 612.96-15.16 815.16-17.36 817.36-19.56 1019.56-21.77 10

Table 5Land Use New Ranks

Remote Rangelands/ Remote Forests 0Residential Rangelands/ Populated Forests 2Cropped Pastoral Villages/Residential Rainfed Mosaic 4Irrigated villages/ Residential Irrigated Croplands/ Populated Irrigated Croplands

6

Urban/ Dense Settlement/ Rainfed Villages/ rainfed mosaic villages/Populated rainfed Cropland

8

Rice Villages 10

Figure 1

Figure 2

Figure 3 Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Figure 10

0.15 0.35 0.55 0.75 0.950

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Poverty and Malaria Risk

Poverty and Malaria RiskLinear (Poverty and Malaria Risk)

Poverty Index

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References:

Alemu, A., Abebe, G., Tsegaye, W., Golassa, L. (2011). Climatic variables and malaria transmission dynamics in jimma town, south west Ethiopia. Parasite and Vectors. Retrieved fromhttp://www.parasitesandvectors.com/content/4/1/30#B1

Dev, V., Phookan, S., Sharma, V., Anand, S. (2004). Physiographic and entomologic risk factors of malaria in assam, india. The American Journal of Tropical Medicine and Hygiene. Retrieved fromhttp://www.ajtmh.org/content/71/4/451.full

Kumar, A., Valecha, N., Jain, T., Dash, A., (2007). Burden of malaria in india: retrospective and prospective view. American Journal of Tropical Medicine and Hygeine. Retrieved from

World Health Organization (2014). World Malaria Report. Retrieved from

www.who.int/malaria/...malaria_report_2014/.

Stresman, G., (2010). Beyond temperature and precipitation ecological risk factors

that modify malaria transmission. Johns Hopkins Cloomberg School of Public Health.

Retrieved from

http://www.researchgate.net/publication/45797812_Beyond_temperature_and_pr

ecipitation_ecological_risk_factors_that_modify_malaria_transmission