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
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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
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.