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Spatial susceptibility analysis of vector-borne diseases in KMC using geospatial technique and MCDM approach

September 2019
Modeling Earth Systems and Environment 5(4)

September 2019
5(4)

DOI:10.1007/s40808-019-00586-y

Authors:

Sk Ajim Ali

The University of Manchester, Manchester, UK

Ateeque Ahmad

Aligarh Muslim University

The prevalence of vector-borne diseases (VBDs) like malaria and dengue claims many parts of the capital city Kolkata. Although the frequency of affects has been declining, still several cases are still reported from different parts of Kolkata Municipal Corporation. The present study aimed to apply multi-criteria decision making (MCDM) approach along with geospatial technique to map susceptible areas of vector-borne diseases. For growing vectors and transmitting diseases, there are always many factors responsible instead of a single factor. Hence, the present work was carried out in multiple stages. Initially different susceptible factors to vector-borne diseases like environment, demography, epidemic and related to suitable breeding sites were selected. Analytic hierarchy process as a technique of MCDM was considered and pair-wise comparison matrix (PCM) was established for each selected factor. Synergistically, weight-based single layer of susceptible zonation was developed and finally, GIS integration was performed for susceptible map of VBDs. The decision-making process was judged by consistency measurement and result shows that the consistency ratio of each selected factor ranged between 0.02 and 0.07, i.e. < 0.1 which is acceptable. Geospatial technique offers space to apply statistical method and analytical technique to acquire information. With the help of remote sensing data and spatial information, GIS tool was utilised to analyse spatial susceptibility of vector-borne diseases. The study revealed that spatial location of water bodies is the most responsible factor with highest weight among all selected factors and concomitantly, moisture content, surface temperature, proximity to waste storage bins, and reported dengue and malaria cases also share influential contributions in prevalence of vector-borne diseases. The present result shows the high applicability of geospatial technique in epidemic diseases’ zonation which may considered helpful for applying in different fields of research.

Chart showing the methodology used for analysing VBD-susceptible zonation in KMC

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Selected factors relating with suitable breeding sites responsible for vectors' growth and diseases' transmission; a proximity to waste bins, b proximity to open vats, c proximity to compactor stations and d proximity to water bodies

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Susceptibility analysis of VBDs based on selected environmental factors

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Sources of data collection and their use in mapping of vector-borne diseases

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Ranking scale of analytic hierarchy process adopted from Saaty to establish PCM

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Figures - uploaded by Sk Ajim Ali

Content may be subject to copyright.

Content uploaded by Sk Ajim Ali

Content may be subject to copyright.

Vol.:(0123456789)

1 3

Modeling Earth Systems and Environment

https://doi.org/10.1007/s40808-019-00586-y

ORIGINAL ARTICLE

Spatial susceptibility analysis ofvector-borne diseases inKMC using

geospatial technique andMCDM approach

SkAjimAli1 · AteequeAhmad1

Received: 4 November 2018 / Accepted: 21 March 2019

Abstract

The prevalence of vector-borne diseases (VBDs) like malaria and dengue claims many parts of the capital city Kolkata.

Although the frequency of aﬀects has been declining, still several cases are still reported from diﬀerent parts of Kolkata

Municipal Corporation. The present study aimed to apply multi-criteria decision making (MCDM) approach along with

geospatial technique to map susceptible areas of vector-borne diseases. For growing vectors and transmitting diseases, there

are always many factors responsible instead of a single factor. Hence, the present work was carried out in multiple stages.

Initially diﬀerent susceptible factors to vector-borne diseases like environment, demography, epidemic and related to suitable

breeding sites were selected. Analytic hierarchy process as a technique of MCDM was considered and pair-wise comparison

matrix (PCM) was established for each selected factor. Synergistically, weight-based single layer of susceptible zonation

was developed and ﬁnally, GIS integration was performed for susceptible map of VBDs. The decision-making process was

judged by consistency measurement and result shows that the consistency ratio of each selected factor ranged between 0.02

and 0.07, i.e. <0.1 which is acceptable. Geospatial technique oﬀers space to apply statistical method and analytical technique

to acquire information. With the help of remote sensing data and spatial information, GIS tool was utilised to analyse spatial

susceptibility of vector-borne diseases. The study revealed that spatial location of water bodies is the most responsible fac-

tor with highest weight among all selected factors and concomitantly, moisture content, surface temperature, proximity to

waste storage bins, and reported dengue and malaria cases also share inﬂuential contributions in prevalence of vector-borne

diseases. The present result shows the high applicability of geospatial technique in epidemic diseases’ zonation which may

considered helpful for applying in diﬀerent ﬁelds of research.

Keywords Vector-borne diseases· Kolkata Municipal Corporation· Multi-criteria decision-making approach· Geospatial

analysis· Susceptibility analysis

Introduction

Growth and development of vectors are the main causes of

transmission of vector-borne diseases. It is always consid-

ered that suitable breeding sites for vector carrying insects

is the main factor in transmission of such type of diseases.

Hence, it is essential to know what is vector and how dis-

eases are transmitted. Vectors are living organisms that can

spread infectious diseases between individuals or from ani-

mals to humans, i.e. transfer virus and pathogens from one

infected person or animal to another, causing serious health

problems in human populations. These vectors may be

bloodsucking insects like mosquitoes, which swig disease-

generating microbes and virus during a blood meal from

an infected host, i.e. animal or human and later inject this

infected blood into a new host during their subsequent blood

meal (WHO 2017). Therefore, vector-borne diseases are not

possible till it is transmitted by any vector from an infected

host to another. Excluding mosquitoes, others vectors are

ticks, sand-ﬂies, ﬂies, ﬂeas, triatomine bugs and some fresh-

water aquatic insects. Chikungunya, dengue fever and den-

gue haemorrhagic fever, Japanese encephalitis, Kala-azar,

ﬁlariasis, and malaria are considered as the main vector-

borne diseases.

The spatial distribution of vector-borne diseases is

determined by diﬀerent dynamic factors like demography,

environment and social factors. Unexpected urbanization,

* Sk Ajim Ali

skajimali.saa@gmail.com; skajimali@myamu.ac.in

1 Department ofGeography, Faculty ofScience, Aligarh

Muslim University, Aligarh, UttarPradesh202002, India

Modeling Earth Systems and Environment

1 3

urban growth and climatic factors including climate change,

variation in temperature and rainfall, variation in moisture

contents can inﬂuence vector breeding and growth (Morin

etal. 2013; Hii etal. 2016). Moreover, various environmen-

tal factors such as changes in local land use, local sanitation

conditions, drainage facility and improper management of

generated waste in and around households can also aﬀect the

transmission of vector-borne diseases due to availability of

suitable sites for breeding and can render large populations

in towns and cities at risk of viral disease spread by mosqui-

toes (Rattanarithikul etal. 1995; Mushinzimana etal. 2006;

Ayele etal. 2012).

As far as Kolkata Municipal Corporation is considered,

malaria and dengue are found as major vector-borne dis-

eases. The proportion of Chikungunya and ﬁlariasis is lim-

ited and found in a little frequency. Japanese encephalitis

and Kala-azar are rarely found here (Chatterjee 2017). The

prevalence of malaria is much more in comparison to dengue

(Sharma etal. 2014). Although the degree of malaria cases

has been reducing from 96,909 in 2010 to 4769 in 2017 with

no more deaths from 2011, the cases of dengue have also

fallen down from 3546 in 2005 to 662 in 2017 although the

deaths in dengue cases reoccur year after year. Malaria and

dengue are an ever-seen and common public health prob-

lem in Kolkata. Anopheles stephensi is the chief source of

malaria transmission in Kolkata and dengue is transmitted

by Aedes aegypti (Aedes albopictus).

Recently, KMC has launched different initiatives to

reduce vector-borne diseases from the capital city includ-

ing establishment of mosquito research laboratory, estab-

lishment of dengue detection centre, dissemination of

dengue report through SMS Alert, disease surveillance

system strengthening, plying of speedboats along canals

for destruction of mosquito larvae, larvicidal spray along

canals using rowing-boats as transport, formation of 21

rapid-action teams for vector control, eﬃcient non-medi-

cal staﬀ assigned with vector control responsibilities etc.

Along with such type of initiatives some micro-level ini-

tiatives are also taken including localised vector detection

centres, multilingual leaﬂets, multi-coloured ﬂex-banners,

multi-coloured hoardings, awareness meeting, comprehen-

sive booklet, documentary ﬁlm, auto-miking and house visit

etc. for awakening the residents to lessen the extent of risk

of vector-borne diseases.

But the best prevention method of vector-borne disease

is to detect and destruct considerable suitable breeding site

of vector-carrying parasites. Recently, remote sensing (RS)

and geographical information system (GIS) prove their eﬃ-

ciency in detecting and recognising the suitable breeding

sites of vector-carrying parasites, susceptible risk zones

and area under prone to risk using geospatial technique and

spatial analyst tool (Nazri etal. 2012; Ahmad etal. 2017;

Ali and Ahmad 2018, 2019). The use of spatial tools in GIS

for public health is a signiﬁcant technique to ﬁnd spatial

association and integration (Richardson etal. 2013). Remote

sensing and geographical information system provides up-

to-date information on environmental parameters of a par-

ticular region or location that inﬂuence the vector-borne

disease transmissions (Nazri etal. 2009). Instead of con-

sidering one single factor, the result becomes weighted on

considering multiple factors (Wondim etal. 2017; Ali and

Ahmad 2018). Hence, the present study punctuates multi-

criteria decision making (MCDM) tool for considering the

parameters and making decision towards susceptibility of

vector-borne diseases and mapping of risk zonation. The

fundamental functioning principle of any MCDM process

is same: selection of weighted criteria, selection of alterna-

tives or sub-criteria, selection of aggregation, methods, and

ultimately rank of alternatives based on weights or outrank-

ing (Majumder 2015). Decision making is based on various

data relating to the study problem. It has been suggested that

80% of data used by decision makers are geographical, i.e.

spatial in nature (Rikalovic etal. 2014). Decision problems

that involve geographical data are denoted as geographical

or spatial decision problems (Malczewski 2004).

Several research works have been carried out using RS

and GIS in malaria susceptibility, dengue risk zonation and

analysis of other vector-borne diseases over time (Nazri et at.

2012; Sarfraz etal. 2014; Tu etal. 2014; Ahmad etal. 2017;

Wondim etal. 2017; Ali and Ahmad 2018). Digital elevation

model, satellite data and location of GPS superior spatial

analysis were utilised for current environment strategy in

urban areas (Dongus etal. 2009). Geospatial technology

was applied to identify and demarcate malaria-susceptible

areas (Delgado etal. 2011). Satellite images with various

resolutions, i.e. IRS LISS I, LISS II and WiFS were utilised

to recognise suitable vector habitats and susceptible areas

(Sharma etal. 1996; Palaniyandi 2004; Palaniyandi and

Mariappan 2012). It is always required to rely on authentic

and accurate data sets for vector-borne diseases’ control and

management to predict susceptible areas and project risk

mapping (Robert etal. 2003; Palaniyandi 2013). Mosquito-

producing ﬁelds were identiﬁed in California through cor-

relating Anopheles larva density with reﬂectance of canopy

growth in early season using Landsat TM imagery and

GIS technique (Wood etal. 1992). Detection of many vec-

tor diseases is directly not possible through ﬁeld visit and

observations, thus Multi-criteria analysis (MCA) along with

GIS and AHP technique was applied by integrating various

thematic layers for malaria risk mapping West Singhbhum

district of the Jharkhand, India (Ahmad etal. 2017). Vari-

ous socio-economic, geographic and epidemiological factors

were integrated to familiar with malarial hotspots (Qayum

etal. 2015). Moreover, there are diﬀerent studies in diﬀer-

ent geographical locations of earth which have shown the

capability of RS and GIS technique in detecting vector-borne

Modeling Earth Systems and Environment

1 3

diseases (Nakhapakorn and Tripathi 2005; Rochon etal.

2010; Hongoh etal. 2011; Khormi and Kumar 2011; Yadav

etal. 2012; Walker etal. 2013; Nazri etal. 2016; Ali and

Ahmad 2018, 2019).

The present study also highlights the application of RS

data and GIS technique along with analytic hierarchy pro-

cess as a multi-criteria decision-making tool for identifying

susceptible areas and hotspot mapping of vector-borne dis-

ease. Thus, present study aimed to use GIS tools and other

primary and secondary data for measuring the weight of

various environmental, demographic and epidemic factors

to make interrelationships towards vector-borne disease

and to map susceptible areas. Such mapping of VBDs can

provide assistance to health authorities to understand spa-

tial distribution under risk as well as temporal outbreak of

such diseases. Risk maps have prepared for public health

and decision making because this will help in preventing

measure and also provide guideline for implementing control

programs and preparing health facilities based on spatial

information of area under risk.

Study area

Kolkata is the capital of the Indian state of West Bengal

which is located on the east bank of River Hooghly. As a

typical riverine city, Kolkata in earlier days was surrounded

by marshes, tidal creeks, mangroves, swamps, and wetlands.

But now all these have changed with time. Kolkata Munici-

pal corporation (KMC) is the largest urban agglomeration

of West Bengal which is located in UTM Zone 45°N with

geographical extension of 22°28ʹ 00ʹʹN–22°37ʹ30ʹʹN and

88°14ʹ30ʹʹE–88°25ʹ30ʹʹE (Fig.1). The KMC has an area of

205.07Km2 which is divided into 16 administrative Bor-

oughs and, respectively, 144 wards. The mean elevation of

the city is 1.5m–16m above MSL. Many parts of the city

were originally wetlands which were reclaimed over the time

to house the bourgeoning population. The annual mean tem-

perature over Kolkata Municipal Corporation is 26.8°C or

80.24°F. Monthly mean temperature ranges between 19 and

32°C. Summers are hot and humid with temperature 34°C,

during May and June the temperatures often exceed 40°C.

Fig. 1 Location of the study area

Modeling Earth Systems and Environment

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Winter or mild winter lasts for highly 2 and half month with

lowest temperature between 9 and 11°C in December and

January. The annual average rainfall is near about 1600mm

and highest rainfall occurs during the month of August.

According to latest population data (DSH 2014-15) the city

has a population of 4,496,694 and 1,024,928 households.

The population density of KMC is about 24,306 persons

per km2 where, household density ranges between 755 and

23,237 households per km2 by making KMC as India’s third

largest metropolitan city as well as the world’s eighth largest

urban agglomeration.

Materials andmethod

Data sources

Different types of satellite data were collected looking

towards requirement of the study. Landsat 8 OLI and TIRS

data were used for deriving Land Surface Temperature

(LST) which is an important factor for growing disease-

carrying mosquitoes. The Landsat OLI and TIRS, 30m

resolution data with UTM projection by default (Path:

Row 138/44) were collected from United States Geo-

logical Survey (USGS) earth explorer portal and thermal

bands were obtained looking towards purpose of use. The

Sentinel-L2A data were utilised for preparing Land Use

Land Cover (LULC), Normalized Diﬀerence Vegetation

Index (NDVI) and Normalized Diﬀerence Moisture Index

(NDMI). Sentinel-L2A data (45QXE & 45QXF) with 10m

and 20m spatial resolution along with WGS 1984 projection

by default were collected from Sentinel Hub EO browser

(Table1).

Excluding satellite data, GPS ﬁeld location, information

from Google Earth, demographic data and epidemic data

were also used in this study. The demographic data includ-

ing population, household and literacy were collected from

District Statistical Handbook, Kolkata 2015. The densities

were calculated by dividing with respective area. To con-

sider suitable breeding sites for vector-borne transmitting

agents, GPS locations were collected which were further

processed and accuracy measured in Google Earth. These

locations include waste bins, open vats of waste, compactor

stations and water bodies. These locations were used for

proximate analysis. Furthermore, spatial epidemic data were

collected from KMC and interpolation was run to determine

area under high and low risk. Finally, all these input layers

were processed in GIS environment with equal projections

and cell size for further processing and analysis.

Study plan

Multi-criteria decision making (MCDM) is considered in

present study to analyse susceptibility of vector-borne dis-

eases. The multi-criteria decision making or multi-criteria

decision analysis is a decision-making method that helps

in structuring, measuring, and judging a decision prob-

lem. As a part of MCDA, it is always required to select

multi-criteria and prefer more weightage criteria through

diﬀerent techniques. Analytical hierarchy process (AHP)

is a widely used MCDA method. Hence, AHP was used

Table 1 Sources of data collection and their use in mapping of vector-borne diseases

Data set Date Source Uses in VBDs suscep-

tibility

Data type Method used

Landsat-8 (OLI and

TIRS)

14/04/2017 USGS-earth explorer

portal

LST Raster Raster calculator: convert

into TOA, reﬂectance

value and brightness

temperature

Sentinel-L2A 11/03/2018;

17/10/2017

Sentinel Hub EO

browser

LULC, NDMI and

NDVI

Raster Supervised classiﬁcation;

standard equation for

moisture and vegetation

index

Demographic data 2015 District Statistical

Handbook, Kolkata

Population density,

household density,

and literate population

Vector; converted to

Raster

Data-based classiﬁcation

using Choropleth

Suitable breeding sites 2018 Field visit and GPS Proximate analysis Vector; converted to

Raster

Multiple ring buﬀers

Epidemic data 2013–2017 KMC, Govt. of West

Bengal

Mapping of malaria and

dengue prevalence

Vector; converted to

Raster

IDW interpolation

Base map 2011 The Kolkata Gazette,

Govt. of W.B

Georeference; delinea-

tion of outer bound-

ary, ward and borough

map

Vector Simple digitization

Modeling Earth Systems and Environment

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in present study. It is a multi-step plan including selection

of relevant criteria/factors, data inputs, data processing,

analysis, and making accurate decision. Kolkata Municipal

Corporation was chosen to carry out such type of study,

because the prevalence of vector-borne diseases like den-

gue and malaria is seen every year from early August to

late October and claims many lives year after year. Thus,

proper susceptibility zonation of VBDs can enhance better

surveillance to reduce the impacts.

The methodology to carry out the study is presented

in the following ﬁgure (Fig.2). The important factors

(considered as decision criteria) that associated to VBDs

occurrence were chosen looking towards the aim of the

study. First of all, 13 associated factors were chosen and

included them into three categories, i.e. environmental

factors, demographic, and epidemic factors and factors

related to suitable breeding site. To cheek the decision

accuracy, Consistency Ratio (CR) of each selected deci-

sion factor was calculated using Saaty’s method. For spa-

tial analysis, the spatial database of each factor was cre-

ated in GIS Environment. GIS integration was run using

weighted linear combination (WLC) to prepare spatial

susceptibility map of VBDs.

Determination ofdecision criteria: data

selection andpreparation

Environmental factors

Land use land cover (LULC)

Diﬀerent type of land cover has signiﬁcant role in provid-

ing sites for suitable breeding environment. The incidence

of vector-borne disease like chikungunya and malaria is

related with water bodies and land under low area (Sheela

etal. 2017). Alterations of land-use type including impound-

ments, dams, irrigation and draining systems that create

shelters for the carrying agents have potential effect in

transmitting vector-borne diseases like malaria, dengue, and

ﬁlariasis (Norris 2004). Thus, preparation of LULC map is

crucial to classify major area under diﬀerent land cover and

their uses to understand those areas susceptible to mosqui-

toes’ breeding site.

The LULC map was prepared using Sentinel-2 dataset. Pri-

marily band combination was performed with Near Infrared

(Band 8), Red (Band 4), and Green (Band 3) to make false

color composite (FCC) infrared. Secondly, object-based

Fig. 2 Chart showing the

methodology used for analys-

ing VBD-susceptible zonation

in KMC

Modeling Earth Systems and Environment

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signature ﬁle was created to run supervised classiﬁcation and

ﬁnally, the entire area was classiﬁed into ﬁve major land-use

types and susceptible risk was considered based on classiﬁed

category (Fig.3a).

Normalized dierence moisture index (NDMI)

The Moisture Index was calculated using NIR and SWIR

Bands of Sentinel-2. Many variables are interrelated and cor-

related with each other. Like the wet areas have high moisture

content and concomitantly high vegetation. NIR is useful for

classifying the vegetation and SWIR is good for measuring the

moisture content of soil and vegetation, i.e. the reﬂectance in

the NIR band is inﬂuenced by the leaf’s internal structure and

the SWIR reﬂects the changes in vegetation water content. In

SWIR, a darker area highlights higher water content. The fol-

lowing equation was used to estimate moisture content;

NDMI =(NIR −SWIR)∕(NIR +SWIR),

where NIR is for the Near infrared (Band 8A) and SWIR

is the Short-Wave Infrared (Band 11). For Sentinel-2 data,

these bands are with 20m spatial resolution. NDMI have

a values ranging from − 1 to + 1, where − 1 indicates very

bad moisture level and + 1 indicates very high moisture level

(Fig.3b).

Normalized dierence vegetation index (NDVI)

Mosquito-borne diseases were found in highest proportion

in areas with low land and lowest forest cover (Sheela etal.

2017). Many researchers emphasized that NDVI is a signiﬁ-

cant factor for analysing mosquito-borne diseases. Moreo-

ver, in present day deforestation seems to be associated with

the growth of mosquito and rise in mosquito-borne disease

transmission (Norris 2004). Vegetation indices, specially

based on the NDVI, the habitat suitability for diﬀerent spe-

cies of mosquitoes can be described (Kleinschmidt etal.

2000; Brown etal. 2008; Lourenço etal. 2011). The mos-

quito population is positively correlated with Normalized

Fig. 3 Maps of four selected environmental factors which were taken as decision criteria in susceptibility analysis of VBDs; a land use land

cover, b normalized diﬀerence moisture index, c normalized diﬀerence vegetation index and d land surface temperature

Modeling Earth Systems and Environment

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Diﬀerence Vegetation Index (Gemperli etal. 2006). The

amount of precipitation can also be linked with vegetation

index. The green vegetation is related with higher amounts

of precipitation and is considered as suitable environment

for mosquito habitats (Brown etal. 2008).

To know the vegetation index (VI) of KMC, NDVI was

calculated (Fig.3c). It was used for quantifying the green

vegetation. NDVI normalizes the green leaf scattering in

the near-infrared wavelength and chlorophyll absorption in

the red wavelength. Hence, near-infrared and Red bands are

required. Band 8 and Band 4 of Sentinel-2 dataset were used

for this purpose and NDVI was calculated using the follow-

ing equation;

where NIR is Band 8 and RED is the Band 4. As per Senti-

nel-2 EO products guideline, the value of NDVI ranges from

− 1 to 1. Negative values (values approaching − 1) repre-

sent water bodies. Values close to zero (i.e. − 0.1 to + 0.1)

mostly represent barren areas of rock, sand, or snow. Low

but positive values represent shrub and grassland (mostly

it ranges from 0.2 to 0.4) and high positive values indicate

dense forest.

Land surface temperature (LST)

Land surface and air temperature aﬀect the feeding behav-

iour of the mosquito and play an important role in the growth

of mosquito larva (Sandru 2014). The survival and the life

cycle of vectors are also eﬀected by land surface tempera-

ture. Sandru (2014) found that mosquito survival is low at

extreme temperatures, i.e. very high or very low temperature

is harmful for mosquito’s growth. The optimum temperature

in which mosquito can grow and survive is found between

28°C and 32°C and temperatures < 5°C and or > 40°C are

deadly for the mosquitoes.

The land surface temperature of KMC was derived from

TIRS 1 (Band 10) of Landsat 8 dataset (Fig.3d). To calcu-

late LST using thermal infrared sensor data, ﬁrst, the conver-

sion of digital number to TOA (Top of Atmosphere) radi-

ance is required. OLI and TIRS datasets can be converted

to TOA spectral radiance using the radiance rescaling factor

which is provided in metadata ﬁle of Landsat dataset. Thus

following equation was used:

where Lλ=TOA spectral radiance (Watts/(m2*srad*µm)),

ML= band-speciﬁc multiplicative rescaling factor taken

from metadata (RADIANCE_MULT_BAND_x, x is the

thermal band), Qcal=quantized and calibrated standard

product pixel value and AL=band-speciﬁc additive rescaling

factor taken from metadata (RADIANCE_ADD_BAND_x,

x is the thermal band).

NDVI =(NIR −RED)∕(NIR +RED),

L𝜆=ML×Qcal +AL

Now, the top of atmospheric brightness temperature has

to calculate from the reﬂectance value using the equation:

where BT = atmospheric brightness temperature in Kel-

vin, which is further subtracted by 273.15 to calculate the

degree Celsius, K2 = band-speciﬁc thermal conversion con-

stant (K2_CONSTANT_BAND_x, x is the band number),

K1 = band-speciﬁc thermal conversion constant (K1_CON-

STANT_BAND_x, x is the band number), Lλ=TOA spec-

tral radiance.

Finally, brightness temperature was converted to land sur-

face temperature using the following equation:

where BT = brightness temperature, λ = wavelength of

emitted radiance, c2 = h × c/s = 1.4388 × 102mK = 14,388

µm (h, c, and s are constant), e = emissivity which was cal-

culated from near-infrared and Red band of the same dataset.

Demographic andepidemic factors

A densely populated area creates a higher chance of experi-

encing an epidemiological outbreak even if the vector house

index is low in that area. The respective vector does not

have to travel far to search for its victims (Sandru 2014).

High human population density and poor water supply are

regarded as major contributors to dengue epidemics (Gubler

2004; Barreto and Teixeira 2008). An unexpected range of

human population densities between 3000 and 7000 per-

son/km2 in rural villages is prone to high dengue outbreaks

(Schmidt etal. 2011). The risk of contracting West Nile

disease is increased by living in low-density housing in the

presence of vegetation (Sandru 2014). Household density,

housing types and surrounding built-up environment have

oﬀered vital insights into the epidemiology of dengue fever

and potential risk (Braga etal. 2010).

Literacy is highly related with human awareness about

diseases transmission. High literate population may be well

aware about the precautions and consequences of transmis-

sible diseases and hence have low risk in contrast with low

literate population. Research showed that people well con-

scious about the cause of malaria and found that awareness

increased with the increasing literacy status and knowledge

about mosquito-borne diseases were also signiﬁcantly bet-

ter among literate than illiterate population (Rasania etal.

2002).

Based on literature survey, the above three demographic

factors (population density, household density and literacy

(

L𝜆+1

)

−

273.15,

LST

(1+(

𝜆

×BT∕c2)

×Ln(e

)

Modeling Earth Systems and Environment

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Fig. 4 Selected factors related

with demographic and epidemic

factors; a population density,

b household density, c literate

population, d reported dengue

cases and e reported malaria

cases

Modeling Earth Systems and Environment

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Fig. 4 (continued)

Modeling Earth Systems and Environment

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rate) were chosen in susceptibility analysis of VBDs. Data

on population and literacy were collected from Bureau of

Applied Economics & Statistics, Department of Statistics

& Programme Implementation, Govt. of West Bengal. First

of all, area of each respective wards of KMC was calculated

using spatial statistical tool in GIS environment. The total

number of population and household of each ward were then

divided with respective area and density of population and

household was calculated. The following formula was used

to calculate population and household density;

where Dx is the density of population or Household, Nx is

the total residing population or households and Ax is the

area of respective ward in Km2. Choropleth is technically

a thematic areal map in which areas are shaded in color or

patterned in proportion. Choropleth technique was used to

prepare the literacy, population and household density map

in ﬁve categories from very low to very high using natural

break in GIS environment (Fig.4a–c).

Epidemiology is the analysis of the distribution of dis-

ease conditions and public health. Dengue and malaria are

considered as an endemic disease instead of pandemic as

these are conﬁned in certain places of the earth, i.e. tropical

region due to availability of temperature and moisture which

oﬀers best breeding sites for the disease-carrying host. For

the epidemic parameters, cases of dengue and malaria for

the year of 2012–2015 were collected for spatial analysis.

Dengue and malaria are main vector-borne diseases in KMC,

other VBDs including West Nile Virus, Lyme disease, Japa-

nese Encephalitis, Chikungunya etc. are not seen here. Only

2 cases of Japanese Encephalitis (JE) were found during

2014. Thus, only malaria and dengue cases were consid-

ered for present work. For spatial distribution of dengue and

malaria cases, spatial interpolation was used based on given

inputs on recorded dengue and malaria cases (Fig.4d, e).

Factors related tosuitable breeding sites

Places with garbage and waste accumulation, stagnant

sources of water, drains, stagnant canal with garbage accu-

mulation, in and around community waste bins and open

waste dumping vats are considered as the most appropri-

ate breeding sites for mosquitoes. Improper handling and

management of waste and their unscientiﬁc disposal cause

adverse impact on all components of the environment and

human health. Unscientiﬁc disposal of wastes here and there

and constructing built-up areas can be the reason for creating

suitable sites for breeding. Disposal of garbage and dump-

ing these into drains can also be causes of breeding grounds

for mosquitoes. Hence much attention is to be paid to pro-

vide appropriate treatment and disposal of waste generated

in the built-up area. In an evolving urban policy, priority is

Fig. 4 (continued)

Modeling Earth Systems and Environment

1 3

to be given to the installation of safe treatment and disposal

facility of waste especially, sewage, sullage, and solid waste

(Sheela etal. 2017).

Wastes tend to accumulate near settlements, oﬀers breed-

ing sites to vectors like rodents, insects and animals (Coin-

treau 2006). Dumping of wastes and poorly managed land-

ﬁlls aﬀect the health, quality of life and impact on local

environment and livelihood. The storage of waste inside the

house, kitchen, room and nearby waste bins and delay of

refuse trucks to collect such waste can create disease-car-

rying pathogens such as vector, rodent and vermin having

high risk of disease spread like malaria, dengue and other

viral diseases (Abdellah and Balla 2013). The vector-borne

disease transmission is strongly connected with location of

breeding site (Carter etal. 2000). Transmissible diseases

Fig. 5 Selected factors relating with suitable breeding sites responsible for vectors’ growth and diseases’ transmission; a proximity to waste bins,

b proximity to open vats, c proximity to compactor stations and d proximity to water bodies

Table 2 Ranking scale of analytic hierarchy process adopted from

Saaty to establish PCM

AHP scale of importance for PCM Numeric

rank

Reciprocal

rank (deci-

mal)

Extremely importance 9 0.11

Very strong to extremely importance 8 0.12

Very strongly importance 7 0.14

Strongly to very strongly impotence 6 0.17

Strongly importance 5 0.20

Moderately to strongly importance 4 0.25

Moderately importance 3 0.33

Equally to moderately importance 2 0.50

Equally importance 1 1.00

Table 3 Standard value of random index (RI) given by Saaty to measure consistency in judgement

N2345678910

RI 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.49

Modeling Earth Systems and Environment

1 3

like malaria and dengue are known to be highly prevalent

around speciﬁc mosquito breeding sites and they can nor-

mally be transmitted only within certain distances from the

breeding sites. The range of dispersal is typically between

a few hundred meters and a kilometre, but rarely exceeds

2–3km (Kobbe etal. 2006). Land disposal facilities add

to global warming and subsequently vector-borne disease

abundance and pathogen survival. Proximity with landﬁlls

blurs the lines between occupational impacts on scavengers

and environmental health impacts on surrounding communi-

ties (Sabesan etal. 2001; Cointreau 2006).

Keeping in mind these circumstances, four factors were

chosen which are associated for providing suitable breed-

ing sites. These factors including distance from waste bins,

distance from open vats, distance from compactor stations

and distance from water bodies were selected for proximity

analysis. This operation was performed in two steps, i.e.

GPS-based record of such locations throughout the study

area was gathered during a 45days ﬁeld survey and these

points were plotted in Google earth to export and create

point shape ﬁles. Finally, multiple ring buﬀers of waste bins,

distance from open vats, distance from compactor stations

and distance from water bodies were created with a common

interval of either 200m or 300m based on their extension

for proximity analysis (Fig.5a–d).

Multi‑criteria decision approach

Decision-making approaches are gaining much importance

as a potential tool for assessment of multifaceted real prob-

lems due to their innate ability to judge diﬀerent options.

Decisions about susceptibility analysis of VBDs typically

involved the assessment of multiple criteria according to

study objectives (Carver 1991; Eldin and Sui 2003; Mal-

czewski 2004). Analytic Hierarchy Process (AHP) is one

of the more commonly applied MCDM approach (Lootsma

and Schuijt 1997). AHP allows decision makers to struc-

ture their decision criteria into a hierarchy of sub-criteria or

alternatives which can then be analysed individually. Thus,

in ﬁrst step, the aim was deﬁned to identify susceptible

areas of vector-borne diseases. In second step, the hierarchy

was constructed by the main criteria to reach the deﬁned

aim. In the present study, 13 criteria were selected looking

towards their importance in VBDs susceptibility. Then in

third step, selected criteria were divided into alternatives

for further analysis. Then, pair-comparison matrix (PCM)

was established and weights were calculated subsequently

in next steps.

AHP is essentially based on pair-wise comparisons

matrix of the deﬁned criteria which are used to establish the

weight to calculate the importance or performance scores

for selected criteria and alternatives (Syamsuddin and

Hwang 2009). PCM is a relative ranking-based matrix table

used to calculate the weight value of all sub-criteria under

a selected criterion in decision making process. As all the

selected criteria will not have the same signiﬁcance for a

particular instance, hence, the signiﬁcance rank of all alter-

natives is complimentary and divides the same alternatives

in the matrix (Saaty 1990; Saaty and Vargees 2001). The

AHP employs an underlying scale with values from 1 to 9 to

rank the relative importance for two criteria or alternatives

(Table2). Here, extreme signiﬁcance is indicated by nine

and equal signiﬁcance by one between criteria of the matrix

(Saaty 1990; Malczewski 1999). The pair-wise comparison

matrix mainly has the criteria of reciprocity which is arith-

metically denoted as 1/RC, R rank of C criteria in pairwise

comparison matrix (Saaty 2012). PCM gives the weights

of each criterion with comparison to all others. Once the

comparison rank is ﬁtted, the weightage has to be calculated

for each respective alternative to judge the consistency for

considering into decision. After constructing the pairwise

matrix, relative weights/eigenvectors are calculated using

following equation;

Ax =𝜆maxX

Table 4 Intensity of importance and respective risk value of selected

environmental factors

Factors Class Impor-

tance

given

Susceptibil-

ity category

Risk value

LULC Water bodies 1 Very high 5

Fallow land 0.33 Very low 1

Open space 0.25 Medium 3

Vegetation cover 1 Low 2

Built-up area 0.5 High 4

NDMI − 0.3439 to 0.0117 1 Very low 1

0.0118–0.1044 2 Low 2

0.1045–0.2044 3 Medium 3

0.2045–0.3267 5 High 4

0.3268–0.6009 7 Very high 5

NDVI − 0.2355 to 0.0959 1 Very low 1

0.0960–0.1943 2 Low 2

0.1944–0.3072 3 Medium 3

0.3073–0.4311 4 High 4

0.4312–0.6934 5 Very high 5

LST 25.9021–28.9500 1 Medium 3

28.9600–30.7402 3 High 4

30.7502–32.0464 6 Very high 5

32.0564–33.3901 3 Low 2

33.4001–37.0101 1 Very low 1

Modeling Earth Systems and Environment

1 3

where A is the comparison matrix of n criteria (i.e. priority

matrix), X is the Eigenvector of n criteria (i.e. priority vec-

tor) and λmax is the Eigenvalue.

Calculated through,

where w1… wn are the weights of alternatives, Ci1… Cin

are the ranks of alternatives and λmax is the Eigenvalue.

(

×Ci

(

×Ci

+…

(

×Ci

𝜆max =Ax1∕Wx1, Ax2∕Wx2…Axn∕Wxn

Once the weights are calculated it is essential to check the

consistency in result. As the numeric ranks are derived from

the subjective preferences of individuals, it is highly possible

to have some personal bias in making the ﬁnal matrix of judg-

ments. For this purpose, AHP always oﬀers a measure of the

consistency of PCM by calculating the consistency ratio (CR).

Saaty (1980) provides the calculated RI value for matrices of

diﬀerent sizes of alternatives as given here (Table3). The ratio

is designed in such a way that if the value of the ratio exceeds

0.10, it will be considered as inconsistent for judgments and

value 0 as perfectly consistent, while values 0 or close to 0 (i.e.

Table 5 Pairwise comparison

matrix along with consistency

ratio of four environmental

factors

a CI = 0.0664, CR = 0.0503

b CI = 0.0351, CR = 0.0313

c CI = 0.0170, CR = 0.0152

d CI = 0.0420, CR = 0.0375

12345wAx λmax

LULCa

Water bodies 1 3 4 1 2 0.3176 1.6924 5.3283

Fallow land 0.33 1 0.5 0.25 0.2 0.0643 0.3320 5.1672

Open space 0.25 2 1 0.33 0.33 0.0913 0.4748 5.2001

Vegetation cover 1 4 3 1 0.5 0.2388 1.2314 5.1573

Built-up area 0.5 5 3 2 1 0.2880 1.5196 5.2758

NDMI 1 2 3 4 5 w Ax λmax

− 0.3439 to 0.0117 1 0.5 0.33 0.2 0.14 0.0499 0.2539 5.0923

0.0118–0.1044 2 1 0.5 0.25 0.2 0.0808 0.4059 5.0259

0.1045–0.2044 3 2 1 0.33 0.25 0.1279 0.6463 5.0546

0.2045–0.3267 5 4 3 1 0.33 0.2639 1.3790 5.2255

0.3268–0.6009 7 5 4 3 1 0.4776 2.5336 5.3047

NDVIc

− 0.2355 to 0.0959 1 0.5 0.33 0.25 0.2 0.0624 0.3140 5.0345

0.0960–0.1943 2 1 0.5 0.33 0.25 0.0986 0.4952 5.0234

0.1944–0.3072 3 2 1 0.5 0.33 0.1611 0.8150 5.0603

0.3073–0.4311 4 3 2 1 0.5 0.2618 1.3372 5.1080

0.4312–0.6934 5 4 3 2 1 0.4162 2.1291 5.1154

LSTd

25.9021–28.9500 1 0.33 0.16 0.33 1 0.0723 0.3715 5.1412

28.9600–30.7402 3 1 0.33 0.33 2 0.1503 0.7700 5.1235

30.7502–32.0464 6 3 1 2 3 0.4052 2.1230 5.2391

32.0564–33.3901 3 3 0.5 1 4 0.2860 1.5013 5.2500

33.4001–37.0101 1 0.5 0.33 0.25 1 0.0863 0.4389 5.0870

Table 6 Calculation of weight

value of environmental factors

for overlay analysis

CI = 0.0147, CR = 0.0164

Environmental

parameters

1 2 3 4 Weight Ax Weight (%)

LULC 1 0.2 0.33 0.2 0.0687 0.2753 6.8674

NDMI 5 1 3 1 0.3897 1.5854 38.9665

NDVI 3 0.33 1 0.33 0.1530 0.6172 15.3025

LST 5 1 3 1 0.3886 1.5807 38.8636

Modeling Earth Systems and Environment

1 3

0.01 or 0.03) are highly acceptable. Consistency ratio (CR)

was calculated through the help of CI and RI. Mathematically,

CR is expressed as;

CI was calculated by putting the value from above estima-

tion by applying the following simple equation;

CI = consistency index, λmax = average of ΣW1…Wn. RI

depends on the number of elements being compared (i.e.

number of alternatives in PCM) and if the value ranges from

0 to 0.09, the matrix will be considered as reasonably con-

sistent and may continue the process of decision making

using AHP.

Many researchers successfully used AHP as a multi-cri-

teria decision making approach in diﬀerent ﬁelds of study.

Analytic hierarchy process was utilised to assess the vari-

ous non-monetary criteria (Velasquez and Hester 2013).

The combination of multi-criteria decision analysis and

cost-beneﬁt analysis was used for risk analysis in trans-

port infrastructure appraisals (Ambrasaite etal. 2011). The

Fuzzy multi-criteria decision-making approach and GIS

were used for siting landﬁll in south Texas (Chang etal.

2008). The MCDM approach was used in combination

CR =CI∕RI

𝜆

max

−n

n−1

with GIS to deﬁne risk zones of malaria epidemic in the

central highlands of Madagascar (Rakotomanana etal.

2007). MCDM based analytic hierarchy process was

applied for analysing criteria weight sensitivity to the

study of spatial sensitivity in suitability evaluation (Chen

etal. 2010). Several more studies applied analytic hier-

archy process for analysing susceptibility, susceptibility,

sensitivity and suitability (Syamsuddin and Hwang 2009;

Tavares etal. 2011; Gorsevski etal. 2012; Rikalovic etal.

2014; Tu etal. 2014; Qayum etal. 2015; Eskandari etal.

2016; Nazri etal. 2016; Guler and Omralıoglu 2017; Ali

and Ahmad 2018, 2019).

Susceptibility analysis ofVBDs using weighted

overlay analysis

Susceptibility analysis for vector-borne diseases was esti-

mated using weighted overlay analysis (WOA). It is consid-

ered as an eﬀective technique to resolve spatial complex-

ity for susceptibility analysis (Kuria etal. 2011). AHP was

applied to decide the more signiﬁcant factors in the selected

hierarchy of diﬀerent inputs (Parimala and Lopez 2012).

Thus, the selected thematic layers were integrated in GIS

environment using linear weighted combination (Girvan

etal. 2003). Susceptibility analysis of VBDs was determined

Fig. 6 Susceptibility analysis

of VBDs based on selected

environmental factors

Modeling Earth Systems and Environment

1 3

based on weight calculated through AHP. All selected causa-

tive raster layers were deﬁned with same cell size and put

respective weight to combine into a single susceptibility

index. The following equation was considered;

where Wj is the weight value of decision factors j, Xj is the

selected raster input and n is the number of selected deci-

sion criteria.

Results anddiscussion

The prevalence of vector-borne diseases in Kolkata

Municipal Corporation is seen year after year, although

the frequencies of occurrence and numbers of death have

reduced. Thus, effort was made to carry out a spatial

∑

j=1

(WjXj

)

analysis on susceptibility zonation of such vectors and

vector-borne diseases. In this regard, a complex decision-

making process was carried out through multi-criteria

selecting, rating, weighting, and analysing. A total 13

factors were selected which were pre-categorised into

three decision parameters. These factors are environmen-

tal parameter, demographic and epidemic parameter and

parameter related to suitable breeding site. Thus, the hier-

archy was constructed with three decision parameters, 13

factors and 65 alternatives or sub-factors. Primarily, after

calculating the weight value of each factors and sub-fac-

tors by AHP, single layer of susceptibility map was created

for each of three parameters and ﬁnally VBDs susceptible

map of KMC was created using overlay of each of this

single layer in GIS environment.

Table 7 Intensity of importance

and respective risk value of

demographic and epidemic

factors

Factors Class Importance

given

Susceptibility

category

Risk value

Proximity to waste bins ≤ 200 1 Very high 5

200–400 0.5 High 4

400–600 0.25 Medium 3

600–800 0.2 Low 2

≥ 800 0.17 Very low 1

Proximity to open vats ≤ 300 1 Very high 5

300–600 0.5 High 4

600–900 0.2 Medium 3

900–1200 0.17 Low 2

≥ 1200 0.14 Very low 1

Proximity to compactor stations ≤ 300 1 Very high 5

300–600 0.33 High 4

600–900 0.25 Medium 3

900–1200 0.17 Low 2

≥ 1200 0.14 Very low 1

Proximity to Water bodies ≤ 200 1 Very high 5

200–400 0.5 High 4

400–600 0.33 Medium 3

600–800 0.2 Low 2

≥ 800 0.14 Very low 1

Modeling Earth Systems and Environment

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spatial susceptibility to VBDs, the following equation was

used (Qayum etal. 2015):

where VBDsH = vector-borne diseases hotspot; Henvp=hot-

spot identiﬁed based on environmental factors; Hdemp=hot-

spot identiﬁed based on demographic and epidemic factors;

Hsbs=hotspot identiﬁed based on factors related to suitable

breeding sites.

VBDsH =Henvp ×Hdemp ×Hsbs

The ﬁnal VBDs raster layer was created and reclassi-

ﬁed according to the susceptible risk zone into 5 class, i.e.

Very high (VH), high (H), moderate (M), low (L) and very

low (VL) areas (Fig.10). The areal proportion of suscep-

tible zones to VBDs was estimated that 0.75%, 18.107%,

48.21%, 32.36% and 0.55% area were found under VH,

H, M, L and VL susceptible zones to VBDs, respectively.

The result of the study shows that about 81% area comes

under moderate to low susceptible zones, whereas about

19% area comes under high to very high susceptible zones.

Table 11 Pairwise comparison

matrix along with consistency

ratio of four parameters related

to suitable breeding sites

a CI = 0.0573, CR = 0.0511

b CI = 0.0415, CR = 0.0371

c CI = 0.0569, CR = 0.0508

d CI 0.0348, CR 0.0311

12345WAx λmax

Proximity to waste binsa

≤ 200 1 2 4 5 6 0.4358 2.3438 5.3784

200–400 0.5 1 2 4 5 0.2610 1.3961 5.3485

400–600 0.25 0.5 1 3 5 0.1710 0.9025 5.2764

600–800 0.2 0.25 0.33 1 3 0.0857 0.4353 5.0800

≥ 800 0.17 0.2 0.2 0.33 1 0.0465 0.2352 5.0632

Proximity to open vatsb

≤ 300 1 2 5 6 7 0.4671 2.5119 5.3778

300–600 0.5 1 2 4 6 0.2581 1.3493 5.2266

600–900 0.2 0.5 1 3 5 0.1618 0.8335 5.1515

900–1200 0.17 0.25 0.3 1 2 0.0693 0.3491 5.0337

≥ 1200 0.14 0.17 0.2 0.5 1 0.0436 0.2199 5.0422

Proximity to compactor stationsc

≤ 300 1 3 4 6 7 0.4838 2.6063 5.3868

300–600 0.33 1 2 4 5 0.2316 1.2460 5.3802

600–900 0.25 0.5 1 3 5 0.1629 0.8516 5.2286

900–1200 0.17 0.25 0.33 1 3 0.0795 0.4014 5.0510

≥ 1200 0.14 0.2 0.2 0.33 1 0.0422 0.2151 5.0923

Proximity to water bodiesd

≤ 200 1 2 3 5 7 0.4262 2.2217 5.2128

200–400 0.5 1 2 4 6 0.2713 1.4186 5.2290

400–600 0.33 0.5 1 3 5 0.1771 0.9155 5.1687

600–800 0.2 0.25 0.33 1 3 0.0837 0.4215 5.0362

≥ 800 0.14 0.17 0.2 0.33 1 0.0417 0.2105 5.0505

Table 12 Calculation of weight

value of parameters related

to suitable breeding sites for

overlay analysis

CI = 0.0034, CR = 0.0038

Parameters related to suitable breeding sites 1 2 3 4 Weight Ax Weight (%)

Proximity to waste bins 1 0.5 1 0.33 0.1411 0.5653 14.11

Proximity to open vats 2 1 2 0.5 0.2630 1.0549 26.30

Proximity to compactor stations 1 0.5 1 0.33 0.1411 0.5653 14.11

Proximity to water bodies 3 2 3 1 0.4546 1.8276 45.46

Modeling Earth Systems and Environment

1 3

Fig. 9 Susceptibility analysis

of VBDs based on parameters

related to suitable breeding sites

Fig. 10 Mapping of vector-

borne diseases hotspot

Modeling Earth Systems and Environment

1 3

Fig. 11 Map showing assess-

ment of accuracy (a) places

with reported VBDs during last

year (b) multi-criteria-based

susceptible map of VBDs using

geospatial technique

Modeling Earth Systems and Environment

1 3

For the out breaking of diseases like water-borne, vector-

borne or mosquito-borne are not due to any single factor.

Many related factors are responsible for such. The present

study tried to use such associated multi-criteria for spatial

susceptibility analysis of VBDs. The result revealed that

based on environmental factor, areas with water bodies,

moisture content index in air > 0.33, vegetation index > 0.43

and surface temperature ranges between 30 and 32°C has

greater susceptibility to VBDs. Based on demographic and

epidemic factor, areas with population density > 81,686,

household density > 16,070, literate population < 5888, high

reported cases of dengue and malaria has more prone to

VBDs. Among suitable breeding sites, areas ≤ 200m from

waste bins, ≤ 300m from open vats, ≤ 300m from compac-

tor stations and ≤ 200m from water bodies has high sus-

ceptibility to VBDs. Out of total 13 selected criteria in this

study, it was found that moisture index, land surface tem-

perature, water bodies and nearness to open waste dumping

vats have greater role in outbreak of vector-borne diseases.

Accuracy measurement

It is a very crucial task in spatial analysis, to evaluate the result

with actual ground. As per KMC report in last year from Janu-

ary to October more than 600 dengue and more than 2000

malaria cases were reported in KMC out of which 6 were

death in dengue. So, a ﬁeld survey was carried out during

June and July of 2018 in diﬀerent wards of each 16 Borough

of KMC. Looking towards surrounding settings and respond-

ent’s response on structured questions, diﬀerent GPS locations

were recorded. These locations were further veriﬁed in Google

Earth open software and export as point locations. After sam-

pled categorization, result found that in many areas of south-

ern, south-eastern and western part has very low prevalence

of MBDs, some areas found with no cases of such diseases,

while some areas of northern and central parts has high to

moderate prevalence of MBDs. Thus, some areas with very

high prevalence of such diseases were plotted to merge with

susceptibility analysis of MBDs (Fig.11). The result shows

that these reported areas are much uniform with ﬁnal layer

and constantly merged with very high to high zones as derived

from spatial susceptibility analysis using geospatial technique

and MCDM approach.

Conclusion

Disease-susceptibility analysis and assessment are

required for better surveillance and strategy making to

prevent such issues from public health. Susceptibility

zonation and mapping are gaining great signiﬁcant for

this requirement. Materials and methods are considered

as the base for logical and accepted result for this type of

study. Thus looking towards research problem and main

objective, geospatial technique and multi-criteria decision-

making approach were used for analysing spatial suscepti-

bility of vector-borne diseases in Kolkata Municipal Cor-

poration. Analytic hierarchy process, as a decision-making

tool of MCDM approach was selected for ranking and

weighting the spatial information. AHP in combination

with geographic information system oﬀers useful result for

the identiﬁcation of vulnerable zones. The great advantage

of AHP is the pairwise comparison matrix which helps in

developing rank-based decision to judge decision crite-

ria. Hence, wrong selection of rank or judgement of any

chosen factor will oﬀer consistency ratio of > 0.1, which

is not acceptable and make decision process inconsistent.

The consistency measurement of present study showed

that it was always between the range of 0.01–0.07, it sig-

niﬁes acceptance for decision making based on intensity

of importance. So, ﬁnal overlay was carried out using

derived weight of selected factors. The result revealed

that maximum portion of study area belongs to moderate

susceptible zones followed by low and high, i.e. covering

about 48.21% (99.10km2) area under moderate, 32.36%

(66.3km2) under low and 18.10% (37.6km2) under high

susceptible zones to vector-borne diseases. While very lit-

tle portion was found under very high and very low with

areal extension of 0.75% (1.50km2) and 0.55% (1.13km2),

respectively. The study result also revealed that out of all

selected criteria in this study, moisture index, land sur-

face temperature, water bodies and nearness to open waste

dumping vats have greater importance in outbreak of vec-

tor-borne diseases.

Finally, the study result was evaluated through loca-

tions of previously vector-borne disease-aﬀected areas. The

assessment found that highest malaria and dengue recorded

areas of previous year are closely associating with high and

very high susceptible zones derived from spatial suscepti-

bility analysis. To seek more accurate and precise result,

the present research also recommends to highlight on more

ground-level information, long and extensive GPS-based

ﬁeld study and comprehensive studies of breeding sites of

mosquitoes and vectors. The present study also suggests to

use very high resolution satellite data including IKONOS

(1m), QuickBird (60cm), GeoEye-1 (50cm), WorldView-1

(50cm) and WorldView-4 (30cm) which will provide

more accurate result in identiﬁcation of susceptible areas.

Acknowledgements We thankfully acknowledge the anonymous

reviewers and the editor for their valuable time, productive comments

and suggestions for improving the overall quality of the manuscript.

Modeling Earth Systems and Environment

1 3

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Waste bins location problem: A review of recent advances in the storage stage of the Municipal Solid Waste reverse logistic chain

Preprint

Full-text available

Feb 2022

Municipal Solid Waste systems have important economic, social, and environmental impacts for society. Within the diverse stages of the Municipal Solid Waste reverse logistic chain, the waste bin location problem consists in properly locating bins in the corresponding urban area to store waste produced by the citizens. This stage has a large impact in the overall efficiency of the whole system. Thus, several researchers have addressed the location problem considering different optimization criteria and approaches. This article presents a comprehensive review of recent advances on the Waste Bins Location Problem, with the main goal of serving as a reference point for decision-makers in this area. The main findings indicate that several optimization criteria and resolution approaches have been applied, but few proposals have simultaneously optimized bins location and waste collection, or considered uncertainty of the model parameters and integrated approaches.

Waste bins location problem: A review of recent advances in the storage stage of the Municipal Solid Waste reverse logistic chain

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Feb 2022
J CLEAN PROD

Municipal Solid Waste systems have important economic, social, and environmental impacts for society. Within the diverse stages of the Municipal Solid Waste reverse logistic chain, the waste bin location problem consists in properly locating bins in the corresponding urban area to store waste produced by the citizens. This stage has a large impact in the overall efficiency of the whole system. Thus, several researchers have addressed the location problem considering different optimization criteria and approaches. This article presents a comprehensive review of recent advances on the Waste Bins Location Problem, with the main goal of serving as a reference point for decision-makers in this area. The main findings indicate that several optimization criteria and resolution approachas have been applied, but few proposals have simultaneously optimized bins location and waste collection, or considered uncertainty of the model parameters and integrated approaches.

Hydro-geochemical assessment of ground water for drinking and agricultural purposes and potential human health risk in Aligarh city, India

Article

Full-text available

Aug 2023

Health effects are related globally to water quality. The residents of Aligarh city are solely rely on groundwater for drinking and domestic purposes. The aim of the study was to find out association between ground water quality and water borne diseases. For this purpose, the present study evaluated physicochemical quality of drinking water. The water quality was evaluated for both pre-monsoon (PRM) and post-monsoon (POM) seasons based on samples collected from 19 stations. To find the potential health risk and waterborne diseases vulnerability, questionnaires-based survey was carried out. Although most of the samples range slightly above the Bureau of Indian Standard's (BIS) permissible limit, the results revealed the sign of contamination from the surface of the earth. According to result of water quality index, the city is classified as having well to moderate water quality throughout the year. The correlation, HCA, and Trilinary plot demonstrate that natural pollution sources, human activities, and geographical factors affecting water quality in the study area. During PRM, 84.21% area falls under slight to moderate sodium hazard category where as the result gets better during POM where 68.42% area falls under the same category. Overall, the water was fit to be used for irrigation purposes. The accuracy of WBD's susceptibility maps was assessed by RMSE. During PRM, chemical parameters in groundwater, WQI and WBD's RMSE were found to be 0.1483, 0.3157 and 0.212 whereas during POM the results were 0.1822, 0.3172 and 0.1966, respectively. The results obtained from susceptibility assessment shows that drinking water will not have any major waterborne diseases. However, the result revealed that POM season is more vulnerable to some waterborne diseases than the PRM season.

A GIS-based novel approach for suitable sanitary landfill site selection using integrated fuzzy analytic hierarchy process and machine learning algorithms

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Full-text available

Jan 2022
ENVIRON SCI POLLUT R

Disposal of waste without treatment is the least preferable way of sustainable solid waste management (SWM). But most cities in developing nations still use open dumps, causing negative impacts on the environment and human health. This study offered a novel approach for selecting landfill sites and sustainable SWM in Aligarh city, India. This was done through data collection, selecting models for criterion weighting, and validation. In order to prepare a landfill site suitability map, a geographic information system (GIS)–based ensemble fuzzy analytic hierarchy process-support vector machine (FAHP-SVM) and fuzzy analytic hierarchy process-random forest (FAHP-RF) models were implemented. Considering the previous studies and the study area characteristics, eighteen thematic layers were selected. The result revealed that land value; distance from residential roads, hospitals and clinics, and waste bins; and normalized difference built-up index (NDBI) have a fuzzy weight greater than 0.10, indicating significant factors. In contrast, land elevation, land slope, surface temperature, soil moisture index, normalized difference vegetation index (NDVI), and urban classification have a zero fuzzy weight, indicating these criteria have no importance. The result further revealed that FAHP-RF with an area under curve (AUC) value of 0.91 is the more accurate model than FAHP-SVM. According to the final weight-based overlay result, seven potential landfill sites were identified, out of which three were determined as most suitable by considering current land cover, public opinions, and environmental and economic concerns. This research proposed a zonal division model based on landfill sites location for sustainable SWM in Aligarh city. However, the findings may provide a guideline to the decision-makers and planners for optimal landfill site selection in other cities of developing countries.

Identification and prioritization of strategies to tackle COVID-19 outbreak: A group-BWM based MCDM approach

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Jul 2021
APPL SOFT COMPUT

The world is reeling in the midst of the novel coronavirus pandemic with fear of rising toll due to the deadly virus. Decision making during a pandemic outbreak has numerous challenges. Covid19 has become a challenging problem for organizations, countries and the world at large. It is even more complicated when governments and medical care communities are changing their priorities based on the growing challenges and level of effectiveness of measures taken in other countries. In this study, a potential application of a well-known MCDM method called the Group Best Worst Method is presented to overcome such challenges and draw the strategies to handle COVID19 outbreak. The methodology is applied to rank the 10 identified strategies based on their relative importance provided by multiple groups of stakeholder. These strategies focus on social distancing, medical care, essential commodities, financial support to poor people, public awareness, overall impact of COVID19, digital surveillance of infected or doubtful people, maintaining the economy of the country, and an effect on industries. Furthermore, the local and global weights along with ranking order of strategies are obtained. A sensitivity analysis also done to show the change in global weights and order of strategies.

Application of spatial multicriteria decision analysis in healthcare: Identifying drivers and triggers of infectious disease outbreaks using ensemble learning

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Jan 2021

Modelling infectious diseases is a complex and multi‐disciplinary problem that necessitates the combined use of multicriteria decision analysis (MCDA) and machine learning (ML) in a spatial framework. This research attempts to demonstrate the extensive applications of MCDA in the field of public health and to illustrate its utility with the combined use of spatial models and machine learning. The study investigates the risk factors for communicable diseases with a focus on vector‐borne infectious diseases, such as West Nile Virus (WNV), malaria, dengue, etc. It aims to quantify vector‐borne disease risk by examining the geographic contextual effects of socio‐economic, climatic, and environmental factors using the objective‐weighting technique adopted from MCDA and machine learning in a geographic information systems (GIS) framework. The authors attempted to minimize subjective bias from the decision space by utilizing an objective‐weighted technique to quantify the risk. The study adopted Shannon's entropy to derive weights for each factor and its classes. The derived weighted layers are fed to an artificial neural network to obtain a final map of risk susceptibility. This final risk map allows policymakers to examine vulnerable areas and identify the factors pivotal to the contribution of risk. Findings show the traffic volume as the most influential variable, and terrain slope as the least one in the disease spread for the study area. The risk appears to be concentrated and distributed along vegetation, wetlands, and around water bodies. The results produced by ensemble learning show great promise with more than 94% accuracy. The accuracy of the results was determined by the confusion matrix and the kappa index of agreement (KIA). The vector control programmes need to adapt to better manage the dynamic changes in patterns involving vector‐borne infectious diseases.

Comparing the Performance of Machine Learning Algorithms for Groundwater Mapping in Delhi

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Dec 2023

The problem of groundwater depletion has arisen as havoc in countries like India due to expanding intensive agriculture, growing population, and burgeoning urban centres. Delhi is one of the greatest urban agglomerations in the country facing severe groundwater depletion, but the robust methods for modelling the groundwater have not yet been adopted for examining the conditions of the groundwater. In such scenarios, accurate modelling of groundwater resources using appropriate techniques and tools is essential. The present study aimed to investigate groundwater level using GIS tools and machine learning algorithms and find the best models for application. The previous studies conducted are purely based on GIS methods without the possibility of accuracy determination of the results. Thus, in this study, boosted regression tree, generalized linear model (GLM), and neural net multi-layer perceptron (NNET-MLP) were applied for modelling the groundwater table in the capital city of India (i.e. Delhi). Anthropogenic, physiographic, meteorological, and hydrological factors like LULC, geology, elevation, slope, aspect, curvature, soil permeability, LST, precipitation, stream power index, and topographic wetness index are supplied as conditioning factors. The performances of the models were compared using area under curve (AUC) plot and correlation (COR). The AUC plot appears well above the diagonal line, showing acceptable results for all the models. The COR is maximum for the NNET-MLP, i.e. 0.93, while minimum value is for GLM, i.e. 0.60. The modelled rasters represented variable groundwater depths, and the mean of each district of Delhi is calculated. This is one of the first studies where GIS and machine learning are integrated to model the groundwater level of Delhi and hence open new prospects for research focussing on the capital of the country.

Spatial accessibility analysis of public healthcare resources in Muktsar district of Punjab, India using geospatial technology

Article

Full-text available

Jun 2023
GeoJournal

The public health policy depends on the management, quality, and coverage aspects. However, the assessment analysis of spatial patterns and distribution of public health services is still a challenge. This paper attempts to analyze the availability and accessibility of public health facilities in the Muktsar district of Punjab at the village level. Spatial and attribute data for public health facilities have been used within the GIS platform to produce accurate measures of accessibility. The average nearest neighbor tool has been used for spatial patterns of services in the district. Near analysis tool has been used to calculate distances from demand points (populations) to providers (facilities). Apart from these, the ratios of public health facilities to population were calculated for the identification of underserved and over-served areas. The study advocates that the spatial pattern of public health facilities is significantly clustered (p value 0.000) with a Z-score of − 5.18. It has been found that urban areas of the Muktsar district were identified as having a higher density of health facilities, whereas villages located in the marginal parts have the lowest density of health facilities. When looking at the average distance from village centroids to health facilities, it varies considerably from village to village. Considerably less than 60 percent of the population living in 108 villages has access to public health facilities. Apart from these, 40 percent of the population living in 126 villages has the greatest increase in the distance while accessing these facilities.

A GIS-based Novel Approach for Sustainable Sanitary Landfill Site Selection Using Integrated Fuzzy AHP and Machine Learning Algorithms

Preprint

Full-text available

Jun 2021

Disposal of collected waste is the least preferable way of sustainable solid waste management. But most of the cities in developing nations prefer to use open dumping in an inappropriate and non-scientific way, causing negative impacts on the environment as well as human health. This study offers a novel approach for scientific landfill site selection and sustainable waste management in Aligarh city, India. This could be possible through relevant data collection, selection of suitable models for criterion weighting, and model validation. In order to prepare a suitable landfill site selection map, a GIS-based ensemble FAHP-SVM and FAHP-RF model was implemented. Considering the previous studies and the characteristics and the study area, a total of eighteen thematic layers (decision criteria) were selected. The result reveals that land value, nearness to residential roads, nearness to hospitals and clinics, distance from waste bins, and NDBI having a fuzzy weight of > 0.10, indicates significant factors; whereas land elevation, land slope, surface temperature, soil moisture index, NDVI and urban classification having a fuzzy weight of 0, indicates these criteria have no importance for the present study. The result further reveals that FAHP-RF with an AUC value of 0.9182 is the more accurate model in comparison to FAHP-SVM. According to the final result of weight-based overlay, a total of seven potential landfill sites were identified, out of which three sites were determined as most suitable by considering current land cover, environmental and economic concerns, and public opinions. This study proposed a zonal division model based on the location of suitable landfill sites for sustainable waste management in the study area. The findings of this study may provide a guideline to the decision-makers and planners for optimal landfill site selection in other cities of developing countries.

A Selection of Guide Pin for an Automobile Part Manufacturing Process by Using Analytic Hierarchy Process

Article

Jul 2021

A guide pin is important for a production system because it can force the workpiece to be in the correct position in a manufacturing process but the selection of guide pin is based on a multiple criteria decision-making problem due to involving a large number of criteria and alternatives. Therefore, the objective of this study was to select the most appropriate guide pin for an automotive manufacturing process. The research was started by collecting decision criteria from various studies and interviewing experts with more than 10 years of working experience and responsibility in the manufacturing process. Next, a questionnaire based-item-objective congruence (IOC) was designed to identify the decision criteria for selecting the guide pin. It found that decision criteria include quantitative and qualitative decision criteria: cost, durability, quality, and shape. Then, the analytic hierarchy process (AHP) was employed to select the guide pin. The result showed that the most appropriate guide pin is a ceramic guide pin with the weight at 0.564, followed by Polyurethane and KCF guide pins with the weights at 0.259 and 0.177, respectively. The ceramic guide pin has been used for six months, it was found that such a guide pin can decrease total production costs and increase productivity simultaneously since the number of guide pins used in the manufacturing processes, the costs of guide pins, and the number of defects are reduced at 89.19, 50.00 and 97.25 percent.

Malaria Hazard and Risk Mapping Using GIS Based Spatial Multicriteria Evaluation Technique (SMCET) in Tekeze Basin Development Corridor, Amhara Region, Ethiopia

Article

Full-text available

Jan 2017

Tekeze Bain Development Corridor (TBDC) is a major river basin that has malaria problems in Ethiopia. Given that malaria is an environmental disease and spatial phenomenon, the application of GIS based Spatial Multicriteria Evaluation Technique (SMCET) are essential to the malaria hazard/risk management process. Malaria hazard and risk map are effective tools to reduce the spread of malaria. The purpose of this study was to assess malaria hazard and risk of TBDC using GIS based SMECT. Malaria causative factors such as distance from water bodies, temperature, drainage, slope and elevation were developed in the GIS environment. The computed Eigen vector was used as a coefficient for the respective factor maps to be combined in weighted overlay in the Arc GIS environment. A model builder in Arc GIS was used to facilitate the overall malaria hazard assessment by combining all impact factors. Malaria risk assessment was done using the malaria hazard layer and the elements at risk/socio-economic factors that determine the level of risk, namely population density, land use, health services and road access. The major finding of the malaria hazard map of TBDC indicated that 397,333.1ha (14%), 1,689,179.68ha (58%), 743,988.96 ha (26%) and 59, 849.41ha (2%) of the area considered in TBDC were subjected respectively to low, moderate, high and very high malaria hazards.

Mapping of mosquito-borne diseases in Kolkata Municipal Corporation using GIS and AHP based decision making approach

Article

Full-text available

Jan 2019

Mosquito-borne diseases are those which transmitted through the bite of an infected mosquito. Stagnant water bodies are often preferable for breeding sites of mosquitos. But from breeding eggs to final stage, there are many factors responsible for its incubation, maturation and growth enough to bite and transmit diseases. The main aim of present study is to focus on associated environmental factors that provide suitable breeding sites and susceptibility mapping of mosquito-borne-diseases through geospatial technique and decision making approach. Analytic hierarchy process as a decision making approach was integrated with geographic information system to map of mosquito-borne diseases in Kolkata Municipal Corporation. Choice based various ranking was used to decide the weights of selected factors through establishing pairwise comparison matrix. Initially, 10 relevant environmental factors were chosen to determine their weight through pairwise comparison matrix. Concomitantly, weight of each causative factor was used as geo-database to support overlay analysis. Consistency ratio was calculated to check the decision process and significance measurement. The consistency ratio of decision factors was calculated as 0.0470, which is < 0.1 and considered as consistent and acceptable. The study analysis shows that proximity to water bodies is a major responsible factor and subsequently moisture content, water index, availability of shadow area and presence of vegetation are also influential factors in prevalence of mosquito-borne diseases. The present result shows the high applicability of satellite data and spatial technique in epidemic diseases zonation.

Using analytic hierarchy process with GIS for Dengue risk mapping in Kolkata Municipal Corporation, West Bengal, India

Article

Full-text available

Apr 2018

Detecting and mapping of Dengue risk areas is a complex, tiring, multifaceted and protracted task requiring evaluation of many criteria. It is not sure that always one single factor is liable for Dengue Fever transmission in all areas, but it differs with changing geographical location. This paper presents the application of analytic hierarchy process alongside with geospatial analysis for detecting Dengue risk areas in Kolkata Municipal Corporation by integrating environmental parameters. It employs two stage analyses synergistically to form a Spatial Decision Support System. The first stage analysis makes use of the thematic layers in Geographical Information System in combination with environmental factors leading to support the second stage analysis using the analytic hierarchy process as a tool. Moreover, weighted overlay analysis was used for detecting potential risk areas. The research result shows that the calculated weights of criteria are within the range of Consistency Ratio being > 0.1. The chosen decision criteria are consistent because the calculated Consistency Ratio is 0.0551 which is < 0.1 and considered as acceptable for decision making. The most influential factors are found the Household Density, Water Logged Areas, Land Surface Temperature, Population Density, Land Elevation and Land Use Land Cover. The present study shows that the spatial relationship can help in understanding the pattern and distribution of dengue outbreak and zonation of potential risk areas.

Alternative suitable landfill site selection using analytic hierarchy process and geographic information systems: a case study in Istanbul

Article

Full-text available

Oct 2017
ENVIRON EARTH SCI

Solid waste management is an important environmental event for developed and developing countries. One of the most sensitive issues in waste management is the selection of a suitable location for the landfill site. This paper presents geographic information systems and analytic hierarchy process approach for selecting the alternative for landfill site selection in Istanbul, Turkey. Totally, eleven factors were used, and two main classification groups were set up in the study which is environmental and economic. Environmental factors are land use, geology, settlement areas, surface waters, population density, airports, and protected areas. Economic factors are slope, solid waste transfer stations, land values, and highways. The identified factors are separated by sub-criteria according to the appropriateness of solid waste landfill. One of the studies that has been made is the discussion of the creation of a dynamic model for the location selection of the solid waste dumping site. In light of legal restrictions, 80% of the study area is classified as unauthorized area. As a result of the study, 1% of the region is unsuitable, 4% is less suitable, 13% is suitable and 2% is very suitable and the digital map bases leading the decision makers were created.

Studying Malaria Epidemic for Vulnerability Zones: Multi-Criteria Approach of Geospatial Tools

Article

Full-text available

May 2017

Introduction: Despite serious interventions worldwide, malaria remains a significant cause of global morbidity and mortality. Malaria endemic zones are predominant in the poorest tropical regions of the world, especially in continental Africa and South-Asia. Major Indian population reside in malaria endemic zones which are tribal dominated and inaccessible. Lack of suitable data, reporting and medical facilities in malaria vulnerable regions handicaps the decision makers in taking adequate steps. Natural resources were mapped to establish their possible linkage with malaria incidence and to delineate malaria hotspots using geo-spatial tools. Methods: Remote sensing data along with various ancillary data such as socio-economic (population in general, child population, tribal population, literacy), epidemiology (Malaria API and Pf cases) and environmental parameters (wetness, forest cover, rainfall, aspect, elevation, slope, drainage buffer, and breeding sites) were integrated on GIS platform using a designed weight matrix. Multi criteria evaluation was done to generate hotspot for effective monitoring of malaria incidences. Results: Various thematic layers were utilized for integrated mapping, and the final map depicted 59.1% of the study area is vulnerable to high to very high risk of malaria occurrence. Manoharpur Administrative Block consisted of 89% of its area under high to very high probability of malaria incidence and it needs to be prioritized first for preventing epidemic outbreak. Various village pockets were revealed for prioritizing it for focused intervention of malaria control measures. Conclusions: Geospatial technology can be potentially used to map in the field of vector-borne diseases including malaria. The maps produced enable easy update of information both spatially and temporally provide effortless accessibility of geo-referenced data to the policy makers to produce cost-effective measures for malaria control in the endemic regions.

PROMOTING SPATIAL DATA SYNTHESIS FOR VECTOR-BORNE DISEASE ASSESSMENT IN ROMANIA

Article

Full-text available

Jan 2014

Maria Ioana Vlad Sandru

Given the continually human interaction with the environment, the present paper reveals the spatial data integration of environmental factors such as topography, vegetation and surface temperature with epidemiological statistical data for assessing vector-borne diseases in Romania, monitored between 2009 and 2011. This study is a small piece of a big puzzle, part of an extended research that evaluates the contribution of geospatial and ground observation data to public health assessment. It is intended to implement a useful and innovative system for Romania's public health, which provides information on various aspects such as the prevalence of diseases, facilities that are available in order to take decisions on, either for creating infrastructure facilities or for taking immediate actions to handle situations.

Application of GIS-based Analytical Hierarchy Process as a tool for Dengue Risk Assessment

Article

Full-text available

Oct 2016

Objective: To highlight the use of analytical hierarchy process (AHP) in geographical information system that incorporates environmental indices to generate dengue risk zonation area. Methods: The medical database considered for the study was referenced to the environmental data layers. Factors related to the risk of dengue fever (DF) were selected throughout previous research and were arranged in a hierarchical structure. The relative weights of factors were calculated, which were within acceptable range with the consistency ratio being less than 0.1. The outcomes from AHP based DF risk zonation area produced useful information on different levels of risks. Results: As a result, factor weights used in AHP were evaluated and found to be acceptable as the consistency ratio of 0.05, which was < 0.1. The most influential factors were found to be housing types, population density, land-use and elevation. Findings from this study provided valuable insights that could potentially enhance public health initiatives. The geographical information system and spatial analytical method could be applied to augment surveillance strategies of DF and other communicable diseases in an effort to promote actions of prevention and control. The disease surveillance data obtained could be integrated with environmental database in a synergistic way, which will in turn provide additional input towards the development of epidemic forecasting models. Conclusions: This attempt, if successful, will have significant implications that could strengthen public health interventions and offers priorities in designing the most optimum and sustainable control program to combat dengue in Malaysia.

International Series in Operations Research & Management Science

Book

Jan 2001

Assessment of relation of land use characteristics with vector-borne diseases in tropical areas

Article

Apr 2017
LAND USE POLICY

Land use and land cover changes play an important role in the occurrence of vector-borne diseases. It is highly essential to identify the prominent changes responsible for its occurrence so that suitable measures can be adopted. An attempt was made to identify the prominent land use and land cover changes responsible for the spread of chikungunya, malaria, and dengue fever in the State of Kerala, India, using hierarchical cluster analysis and multiple regression analysis. Large extent of water bodies, low land and agricultural land played a significant role on the incidence of chikungunya and malaria. High population density, built-up area and agricultural area favoured dengue fever. Vector-borne diseases were found to be the lowest in places where there is no low land and with higher forest area. Inappropriate disposal of wastes generated in the built-up area might be the reason for the spread of dengue fever. Freshwater in drains of these areas is polluted and form breeding grounds for mosquitoes. Hence much attention is to be paid to provide appropriate treatment and disposal of wastes generated in the built-up area of the State In an evolving urban policy, priority is to be given to the installation of safe treatment and disposal facility of wastes especially, sewage, sullage, and solid waste. The protection of forest land also plays an important role. Economic policy instruments such as Payment for Environment Services (PES) schemes, may constitute a useful tool to encourage an improved land use management through appropriate price signals, such as, for instance, for the preservation of forested areas especially in proximity of highly populated urban environments.

The Analytic Hierarchy Process: Decision Making in Complex Environments

Chapter

Jan 1984

Thomas L. Saaty

The Analytic Hierarchy Process (AHP) is a problem solving framework. It is a systematic procedure for representing the elements of any problem. It organizes the basic rationality by breaking down a problem into its smaller constituent parts and then calls for only simple pairwise comparison judgments, to develop priorities in each hierarchy.

Spatial susceptibility analysis of vector-borne diseases in KMC using geospatial technique and MCDM approach

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