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Disease susceptibility mapping using spatial modeling techniques

Author: Mohammadreza Rajabi

Supervisors: Ali Mansourian , Petter Pilesjo

GIS Center ,Department of Physical Geography and Ecosystem Science

Lund University

September, 2015


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Table of Contents

1- Introduction ......................................................................................................................................... 1

2- Literature Review ............................................................................................................................... 2

2-1- Disease mapping ............................................................................................................................... 2

2-2- Geographic correlation studies .......................................................................................................... 3

2-2-1- Spatial statistics ............................................................................................................................. 3

2-2-2- Ecological Analysis ...................................................................................................................... 4

2-3- Disease Surveillance ......................................................................................................................... 5

2-3-1. Risk mapping and modeling ......................................................................................................... 6

2-3-2. Simulation of disease spread ......................................................................................................... 8

2-3-3. Artificial intelligence for disease surveillance .............................................................................. 9

2-3-3.1. Neural networks for risk mapping and modeling ...................................................................... 9

2-3-3.2. Agent based modeling (ABM) for spatial epidemiology ........................................................ 10

2-4- Summary ......................................................................................................................................... 11

3- Research ............................................................................................................................................. 12

3-1- Research Problem ........................................................................................................................... 12

3-2- Research Objective ......................................................................................................................... 12

3-3- Research Questions ......................................................................................................................... 13

3-4- Research Summary ......................................................................................................................... 13

4- Material and methods ....................................................................................................................... 14

4-1- Case studies ..................................................................................................................................... 14

4-1-1. Visceral Leishmaniasis ............................................................................................................... 14

4-1-1.1. Study area for Visceral Leishmaniasis .................................................................................... 15

4-1-2. Cutaneous Leishmaniasis ............................................................................................................ 16

4-1-2.1. Study area for Cutaneous Leishmaniasis ................................................................................ 16

4-2- Methods........................................................................................................................................... 17

4-2-1. Artificial neural networks ........................................................................................................... 18


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4-2-2. Agent based modeling ................................................................................................................. 19

4-2-3. Fuzzy Logic ................................................................................................................................ 19

5- Progress .............................................................................................................................................. 19

5-1- Artificial Neural Networks for Visceral Leishmaniasis .................................................................. 19

5-2- Agent based modeling for Cutaneous Leishmaniasis ..................................................................... 20

5-3- Knowledge- and data-driven spatial modelling methods for VL susceptibility mapping ............... 21


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1- Introduction Infectious diseases have long been one of the major challenges to human progress and survival.

They are considered as the main causes of death and disability worldwide. Infectious diseases

can be expected to remain a significant challenge for the foreseeable future (Morens et al., 2004).

About 25% (>15 million) of annual deaths worldwide are directly related to infectious diseases

(Morens et al., 2004). The main victims are people in developing countries and particularly

infants and children.

It has long been recognized that location can influence the health. In the book ‘Airs, Waters and

Places’, Hippocrates (430 to 377BC) mentions that certain diseases tend to occur in some specific

places (Rinaldi et al., 2006). Generating the world map of disease by the German physician

Finke in 1792 (Barrett, 2000) , geographical distribution of yellow fever cases in the harbor of

New York in 1798 (Stevenson, 1965) and the famous map of the addresses of cholera victims

and its association with location of water supplies in London by John Snow in 1855 (Koch and

Denike, 2009) are other historical examples that relate location with health.

The 1792 mapping of yellow fever and the 1854 cholera endemic triggered the idea of disease

susceptibility mapping. Afterwards , disease prediction using the environmental characteristics of

geographical areas have been frequently applied in epidemiological studies (Bergquist and

Rinaldi, 2010). In most of these studies environmental data have been integrated with

information of the causative agents and (intermediate) hosts in a geographical information

system (GIS) which includes modeling and exploratory analysis (Bergquist and Rinaldi, 2010).

The use of GIS together with spatiotemporal analysis for modeling disease dynamics refers as

“spatial epidemiology” and has been developed and diversified increasingly recently, adding new

technologies and techniques. However, despite the current well-developed understanding of

infectious diseases epidemiology (i.e. humans , vectors , intermediate hosts and environment), it

should come as surprise that advanced artificial intelligence (AI) techniques have been briefly

explored in spatial epidemiology. AI has several advantages over other modeling techniques

including: (i) accurate modeling of emergent phenomena, (ii) providing natural description of a

complex system , (iii) flexibility and (iv) fast performance and time saving (Bazghandi, 2012).

Accordingly, we utilize advanced artificial methods such as neural networks, agent based


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modeling, fuzzy logic and etc., to examine the inter-relationships of infectious disease

components for disease surveillance and predictive mapping. Ecological and socio-

environmental data are considered as the main inputs for the model.

2- Literature Review The term ‘Spatial epidemiology’ in GIS refers to the analysis of the spatial/geographical

distribution of the incidence of disease(Lawson, 2013). This is done in consideration of

“demographic, environmental, behavioral, socioeconomic, genetic, and infections risk factors ”.

(Elliott and Wartenberg, 2004). Spatial epidemiology can be divided into three main areas: (1)

disease mapping, (2) geographic correlation studies (3) disease surveillance (Elliott and

Wartenberg, 2004). Each of the above types of spatial epidemiology is reviewed in the following

sections.

2-1- Disease mapping Basic mapping is the most popular usage of GIS in epidemiology(Rinaldi et al., 2006).

Representation of epidemiological data in the form of a map simplifies interpretation, synthesis

and recognition of frequency and clusters of disease(Rinaldi et al., 2006).

The oldest examples can be referred to the world disease map of Finke in 1792 (Barrett, 2000) ,

geographical distribution of yellow fever cases in New York in 1798 (Stevenson, 1965) , and the

mapping of cholera victims addresses in London by Snow (1855). Smith and Stiles in 1903

displayed the prevalence of hookworm infection in Texas in two independent papers and

indicated the association of this parasitic disease with soil type(Brooker and Michael, 2000).

Disease maps can be based on demography or geography information (Rinaldi et al., 2006).

Disease maps representation can also be qualitative or quantitative. Qualitative maps illustrate

the location of disease without specifying the amount of disease infection. Cringoli et al. (2002),

generated a point distribution map in which Dicrocoelium dendriticum in sheeps was shown from

an area of the southern Italian. Quantitative maps display number of disease cases , population at

risk , infection prevalence or intensity or incidence (Rinaldi et al., 2006). Cringoli et al. (2001),

generated a distribution map with proportional peaks to show the Dipetalonema reconditum in

dogs from an area of southern Italy.


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2-2- Geographic correlation studies In geographic correlation studies, the main aim is to examine geographic and demographic

dynamics in exposure to environmental variables and socioeconomic measures, or lifestyle

factors in relation to health outcomes measured on a geographic (ecologic) scale (Elliott and

Wartenberg, 2004). We have explored this area in two divisions: spatial statistics and ecological

analysis.

2-2-1- Spatial statistics Spatial statistics are considered as statistical methods in which location data are used in the

analysis(Rinaldi et al., 2006). The framework of spatial epidemiology using statistics include

visualization , spatial analysis and modeling (Pfeiffer, 2004). Statistical methods are used to

interpret the differences in disease occurrence between areas. Their main advantage is their

ability to filter the signal from the noise(Elliot et al., 2000). Some of these methods have been

utilized in spatial epidemiology regarding veterinary diseases, e.g. fasciolosis (Durr et al., 2005) ,

paramphistomosis (Biggeri et al., 2005) , echinococcosis (Berke, 2001, Budke et al., 2005),

bovine spongiform encephalopathy (Abrial et al., 2005) , mastitis (Green et al., 2004), foot and

mouth disease (Lawson and Zhou, 2005) and Dicrocoelium dendriticum (Biggeri et al., 2006).

Spatial analysis and statistical methods are combined to identify patterns in distribution of

disease incidences and discriminate between systematic and random fluctuations(Rinaldi et al.,

2006). For example auto correlation indices such as Moran’s I or Sman’s D have been used to

detect the presence of structured variability on aggregate disease data(Mollalo et al., 2014).

Moreover , case-control sampling designs have been used on point data in the form of the

Cuzick-and-Edwards’ test to detect general fluctuations (Cuzick and Edwards, 1990). GIS has

also been combined with statistical analysis methods to identify new cutaneous leishmaniasis

epidemiological patterns (Salah et al., 2007, Rodríguez et al., 2013, Mollalo et al., 2015,

Adegboye and Kotze, 2012).


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2-2-2- Ecological Analysis The term “ecological analysis” in spatial epidemiology refers to the description of relationships

between spatial distribution of diseases and environmental preconditioning factors and their

analysis. There are significant number of literatures in which the relationship between disease

indicators (e.g. positivity, incidence and prevalence) and the environmental and climatic

variables have been explored. There are studies conducted by entomological researchers in

which GIS is used to study the ecology of disease vectors (Karimi et al., 2014, Gálvez et al.,

2010, Abdel-Dayem et al., 2012, Kassem et al., 2012).

A number of studies around the world have demonstrated that environmental, demographic and

statistical data about the ecology of vector-borne diseases can provide the basis for the

development of spatial, predictive risk models (Peterson et al., 2003, Castillo-Riquelme et al.,

2008, Salahi-Moghaddam et al., 2010). There are also studies in which disease epidemiology has

been modeled by considering the potential ecological factors. For example, Barhoumi et al.

(2015) highlighted the impact of irrigation of arid regions in Tunisia on the population of CL

sand fly vectors. Mollalo et al. (2014) developed a model to discuss the relation between

vegetation cover and the incidence of CL. Garni et al. (2014) explored the influence of land

cover change on the occurrence of CL using GIS and remote sensing analysis.

Spatial epidemiology can be used together with remote sensing data to predict disease

seasonality. Accordingly, climatic, environmental and habitat analysis of disease host and

vectors is performed in a GIS framework. Climate-based forecast systems , utilizing the growing

degree days (GDD), have been developed for different diseases such as fasciolosis,

schistosomosis and malaria (Malone, 2005), along with dirofilariosis (Genchi et al., 2009)

(Figure 3).


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Figure 1- Yearly average predicted number of Dirofilaria generations in Europe by Genchi et al. (2009)

2-3- Disease Surveillance A surveillance system is defined as an integrated set of planned epidemiological activities whose

aim is to identify and prevent new cases of disease(Rinaldi et al., 2006). A spatial surveillance

system looks for the presence of non-natural clusters of diseases in the areas. The system could

be regarded as an expert system in which decision making process is based on geographical

information associated with the disease prevalence identified from geographical correlation

methods.

Several infectious diseases have (re)-emerged during recent years. According to the geographical

correlation studies (see section 2.2) the most important factors causing the (re)-emergences are

climate change , habitat changes, alterations in water storage and irrigation habits, pollution ,

development of resistance to insecticides and drugs , globalization and the increase in

international trade , tourism and travel. Examples of re-(emerging) vector-borne diseases caused

by the mentioned factors include Babesiosis, bluetongue, chikungunya, dengue, encephalitis,

ehrlichiosis, leishmaniasis, Lyme disease, malaria, plague, trypanosomiasis, West Nile disease

and yellow fever(Takken and Knols, 2007). The surveillance systems could be a solution to

suppress the losses of the (re)-emergences (Hendrickx et al., 2004). The main advantage of these


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systems is their ability for exploratory analysis. There is a wide variety of modeling techniques

which could be applied for disease surveillance and predictive mapping. These methods could be

explored in two different perspectives:

a) Risk mapping and modeling

b) Simulation of disease spread

2-3-1. Risk mapping and modeling The systematic routine collection and analysis of health outcome data for disease prevention and

control purposes is performed using risk mapping and modeling techniques. In this regard,

disease clusters are explored through the use of space , time , and space-time pattern detection

methods(Elliott and Wartenberg, 2004). The term disease cluster indicates an excess of cases

above background rate in relation to certain time and space (Elliott and Wartenberg, 2004).

Risk mapping and spatial data modeling in epidemiology aims to predict the occurrence of

disease. There is a direct association between the epidemics spread and the characteristics of the

environment which could be identified using geographical correlation methods (Bergquist and

Rinaldi, 2010) (see section 2.2). In this regard, spatiotemporal data such as temperature ,

humidity , vegetation , hydrology , meteorology , etc. could be used together with clinical data to

construct predictive models. These datasets will be processed using spatial analysis methods to

generate risk maps (Seid et al., 2014, Garni et al., 2014, Ali-Akbarpour et al., 2012). Bhunia et

al. (2011) studied the influence of the distribution of inland water bodies on transmission of

leishmaniasis and presence of its dominant vector in a GIS model. In a knowledge-driven

approach, Rajabi et al. (2012) used multi-criteria decision-making methods together with GIS to

identify high-risk areas for leishmaniasis outbreak in north-western Iran. Pezeshki et al. (2012),

have used fuzzy clustering means to describe the relation of spatial and climate variables to

cholera incidence in Chabahar, Iran. Salahi-Moghaddam et al. (2010), have used GIS to generate

visceral leishmaniasis risk maps using ecological and environmental data.

Risk mapping and modeling has become one of the most important research areas in

epidemiology recently (Bergquist and Rinaldi, 2010). The relevant literature is growing fast

regarding the development of the theoretical work, methodological development and field

application(Angulo and Ruiz-Medina, 2008). Specifically, for the vector-borne diseases the


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spatial and temporal characteristics of environment play an important in epidemiology(Bergquist

and Rinaldi, 2010). Significant number of literatures indicated that vector-borne diseases are

highly sensitive to the variations in temperature and rainfall(Liang et al., 2007). It took a while

till researches considered GIS as an analytic tool for epidemiology rather than a visualization

tool. Accordingly, spatiotemporal modeling is increasingly applied for a wide variety of vector-

borne diseases. Mott et al. (1995) introduced the applicability of geographical analysis for the

epidemiology and predictive modeling of Cutaneous Leishmaniasis. Thereafter, spatial analysis

has been frequently used to identify and evaluate the underlying environmental precondition

factors which influence the Cutaneous Leishmaniasis (Seid et al., 2014, Garni et al., 2014, Ali-

Akbarpour et al., 2012). Similarly, other vector-borne diseases have been explored using spatial

modeling techniques, e.g. dengue (Pongsumpun et al., 2008) , leishmaniasis (Ready, 2008) ,

malaria (Mabaso et al., 2006), Rift Valley fever (Vignolles et al., 2009), bluetongue (Racloz et

al., 2008) and Shistosomiasis (Wu et al., 2007). There are also some studies in which the

dynamics of historical epidemics has been explored. For example, Yu and Christakos (2006),

studied the spatiotemporal evolution of bubonic plague in India 1896-1906 using GIS

techniques.

The techniques applied in risk mapping and modeling can be divided into two groups: data-

driven and knowledge-driven methods. In the knowledge-driven approach, the analyst uses

expert knowledge to assign weights to a series of factors. The main problem here is that

insufficient knowledge could mislead the work. Another limitation is the subjectivity of the

weighting of the factors.

On the other hand, data-driven methods could be discussed in two different classes: (1)

deterministic and (2) statistical data-driven methods. Deterministic data-driven methods can

only be applied in areas where there are simple relations between the predetermined factors and

the desired phenomena. Moreover, the environmental properties should not be inhomogeneous

and intricate (Turner and Schuster, 1996). In addition, these methods are only applicable for

small areas (Yilmaz, 2009). One of the main drawbacks of the deterministic, data-driven

methods is malfunctioning when the data are incomplete (Gomez and Kavzoglu, 2005).

Statistical, data-driven methods, on the other hand, require the collection of large amounts of


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data to produce reliable results (Turner and Schuster, 1996; Gomez and Kavzoglu, 2005; Yilmaz,

2009).

2-3-2. Simulation of disease spread The interactions of environment, hosts and vectors in specific locations have an important role in

development of an outbreak to an epidemic. Disease spread modeling can be performed by

mathematical modeling approaches. Chaves and Hernandez (2004), presented a mathematical

model for dynamics of transmission of American Cutaneous Leishmaniasis (ACL), that includes

a population of incidental hosts , along with species that are reservoir hosts. Their model

obtained expressions that allow computing the threshold conditions for the persistence of the

infection. RABINOVICH and FELICIANGELI (2004) , developed a mathematical model of

cutaneous leishmaniasis (CL) transmission predicting CL based on field data of number of

positive sand flies , new CL cases , and number of susceptible people. Time series analysis also

has been applying for simulation of disease dynamics. Lewnard et al. (2014), fit time series

models using meteorological covariates to predict CL cases in a rural region of Bahia , Brazil

from 1994 to 2004. They used the model to forecast CL cases for the period 2005 to 2008.

Chaves et al. (2014), studied association patterns between monthly time series of: CL cases,

rainfall and temperature from Panama. They employed autoregressive models to quantify the

seasonal and inter-annual impacts of climate and El Nino on CL dynamics. Chaves and Pascual

(2006) , studied monthly data from 1991 to 2001 , of CL incidence in Costa Rica using several

approaches for non-stationary time series analysis in order to ensure robustness in the description

of CL’s cycles.

While such models have proved useful, they have also been criticized by scientists(Crooks and

Hailegiorgis, 2014). The most important issue of these studies is that they don’t consider socio-

ecological complex relations and individual behavioral factors for an epidemic progression

modeling (Epstein, 2009). Moreover, the interactions between the key epidemiological factors

couldn’t be simulated realistically using these conventional methods (e.g. differential equations)

(Bonabeau, 2002). Lots of classical models neglect the population heterogeneity (Bonabeau,

2002), and consider uniform mixing assumptions for the disease spread (Eubank et al.,

2004).They also tend to smooth out fluctuations which make them susceptible to large

perturbations (Bonabeau, 2002). Accordingly, treatment of all components of an epidemic as


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largely homogeneous entities is one of the other criticisms for these models (Crooks and

Heppenstall, 2012). Subsequently, the modeling of disease dynamics in the mentioned models is

performed by just focusing on the local interactions and ignores the complex situations of the

environment (Birkin and Wu, 2012).

2-3-3. Artificial intelligence for disease surveillance Spatial epidemiology aims to explain or predict the occurrence of disease(Rinaldi et al., 2006).

Advanced modeling methods could be used to extract various static or dynamic relationships

from a set of input maps. While developments in spatial statistics within the health science have

advanced considerably, there have been fewer efforts in utilizing new advanced techniques.

Predictive maps can be obtained using heuristic algorithms (e.g. neural networks), and agent

based modeling techniques could be used for disease dynamics simulation. The linkage between

the mentioned modeling methods and spatial epidemiology has been poorly developed.

2-3-3.1. Neural networks for risk mapping and modeling To overcome the mentioned limitations of conventional risk mapping and modeling, new

techniques, such as artificial neural networks (ANNs) could be used for modeling complex

problems. A Neural network is an artificial system which is designed based on the way the

human brain works. These advanced, data-driven approaches are being used more frequently in

spatial studies to identify and classify areas as well as to predict the distribution of real-world

phenomena having to do with flooding, landslides, mines, etc. (Biswajeet & Saro, 2007;

Nykanen, 2008; Yilmaz, 2009; Choi et al. 2010; Pradhan, 2013).

Although most of epidemics are restricted to specific localities with special environmental,

topographical, demographical, and socio-economic factors, there has been only a few studies on

adapting neural networks in GIS to explain their focal distribution. Neural networks are useful

when the primary goal of model is prediction and complex non-linarites exist in the dataset (Tu,

1996). According to ,Tu (1996) , the most important advantages of neural networks are as

follows : (1) require less formal statistical training to develop (2) detect complex non-linear

relationships (3) detect all possible inter-relationships (4) utilizing multiple different training

algorithms.

Neural networks are able to learn by themselves, an ability which makes them remarkably

distinctive to identify complex relationships between the epidemic and the preconditioning

http://simple.wikipedia.org/wiki/Brain


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factors. Little attention however, has been focused on using ANN in spatial epidemiological

studies, with some exceptions. For example, Kiang et al. (2006) used ANN methods to model the

dependency of malaria transmission on precipitation, temperature, relative humidity and

vegetation index variables in Thailand. Capinha et al. (2009) also applied ANN to combine

Anopheles atroparvus species records with a set of five environmental predictors to present

habitat suitability for malaria vectors in mainland Portugal. Moustris et al. (2012) provided an

ANN forecasting model to evaluate the possible impact of meteorological parameters and air

pollution on the number of childhood asthma admissions.

2-3-3.2. Agent based modeling (ABM) for spatial epidemiology In an ABM, “agents” represent entities in a real world system to be modeled. An ABM also

represents the environment in which these entities “live.” Each of these entities has a state and

exhibits an explicit behavior. An agent can interact with its environment and with other

entities(Barnes and Chu, 2010).

ABMs offer an alternative to classical simulation methods as they provide the ability to focus on

the dynamic interactions between entities who are involved in a disease spread (Kelly et al.,

2013). Kelly et al. (2013) , note that ABMs are proper methods when the purpose of the model is

for developing an understanding of a desired system. Especially when it comes to assumptions

about processes and interactions, ABMs are strong methods and various complex systems can be

explored through ABM simulations (Kelly et al., 2013, Crooks and Hailegiorgis, 2014).

ABMs have been increasingly used in spatial epidemiology applications for different diseases,

e.g. Cholera (Crooks and Hailegiorgis, 2014) , dengue fever (Lourenço and Recker, 2013), foot-

and-mouth (Dion et al., 2011), hepatitis (Ajelli and Merler, 2009), influenza (Rao et al., 2009) ,

malaria (Linard et al., 2009), measles (Perez and Dragicevic, 2009), mumps(Simoes, 2012),

swine flu (Epstein, 2009), smallpox(Epstein et al., 2002), tuberculosis(Patlolla et al., 2006) etc.

However, with respect to vector-borne diseases there has been little work carried out using

ABMs considering habitat analysis of vectors and reservoir hosts in GIS. For example, Linard et

al. (2009) explored the potential biting rate for malaria vectors in a land use/cover map using a

multi-agent simulation. Arifin et al. (2013), developed a model to simulate the resource-seeking

process of malaria vectors.


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2-4- Summary The main objective of spatial epidemiology is to prepare a framework in which different aspects

of a disease (epidemic) has been explored and could be used for disease surveillance and making

appropriate preventive decisions. Map visualization and geographical correlation methods

prepare the input for modeling and risk mapping of the desired diseases. In this regard, statistical

methods and ecological analysis are used in geographical correlation methods. Different methods

could be applied for surveillance of epidemics. These methods could be classified according to

two broader objectives.

a) Risk mapping and modeling according to different environmental, demographical,

socioeconomic, etc., factors.

b) Simulation of disease spread using interactions between host , vector , intermediate host

and the environment.

According to complexity in relationships of different components of a disease, applied models

should be strong enough to extract the desired patterns. Neural networks offer a number of

advantages including requiring less formal statistical training, ability to detect complex nonlinear

relationships and the ability of multiple training algorithms(Tu, 1996). Accordingly, artificial

neural networks could be used to explore the existing associations of diseases dynamics.

The interactions of environment, hosts and vectors in specific locations have an important role in

development of an outbreak to an epidemic. Having these interactions simulated, one would be

able to prepare a surveillance system for the certain disease. Conventional modeling methods

have several drawbacks when it comes to a complex system such as an epidemic. Agent based

modeling methods are proved to be a reliable alternative for the conventional simulation

methods, since they consider randomness and heterogeneity during simulation.


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3- Research

3-1- Research Problem

There have been various studies in relation to disease surveillance. The main problem in risk

mapping and modelling part of disease surveillance studies is the weakness of applied techniques

in extracting non-linear relationships between factors and the disease. There are two important

reasons for their weakness. First, conventional techniques do not have the ability to identify the

complex relationships. Second, conventional techniques don’t have the ability to consider all the

spatial and non-spatial preconditioning factors in the modelling.

Disease spread modelling is another important part of disease surveillance studies. Various

studies have worked on disease spread using conventional mathematical and statistical methods.

These methods have been criticized by researchers because they neglect randomness,

heterogeneity and interactions in the disease spread. Most of recent studies are focusing on

applying agent based modeling (ABM) techniques for disease spread. But there are several

challenges that should be addressed while modeling by an ABM: (1) agent’s behavior should be

designed and parameterized (2) sensitivity analysis should be performed for the verification and

validation of the model (3) coupling socio-demographic , ecological and biophysical models, and

(4) capture spatial heterogeneity in both inputs and outputs across multiple spatial scales

(Filatova et al., 2013).

3-2- Research Objective

The main problem of this study is to address challenges associated with the disease surveillance

techniques (risk mapping, disease spread) adapting artificial intelligence techniques in spatially

explicit environments. The main objective will be achieved following these auxiliary objectives:

• Adopt artificial intelligence techniques to risk mapping and modelling of infectious

diseases.

• Adopt agent based modelling techniques to modelling of disease spread in a spatially

explicit environment.


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3-3- Research Questions

The main questions which should be answered during the research include:

• How to apply artificial intelligence techniques such as Neural Networks , Fuzzy Logic ,

etc. as spatial models which approximate the relationship between a set of recognition

criteria (or predictor maps) and a target disease?

• How data-driven and knowledge-driven spatial modelling techniques will perform in

relation to susceptibility mapping of a disease?

• How to develop a spatial agent-based model for a vector-borne disease in which common

challenges of the ABMs are addressed?

• How to apply ABMs to capture the characteristics of socio-ecological systems associated

with an epidemic and explore the effects of environmental and human-made changes to

disease progress.

3-4- Research Summary

The main focus of this study is to adopt new solutions for disease surveillance. As discussed in

previous sections, risk mapping and modelling of infectious diseases is associated with a wide

variety of geographical and non-geographical factors. Identification and modelling of linear and

non-linear relationships between these factors and the incidence probability of an epidemic is the

main effort in the first part of research. In this regard, artificial intelligence data-driven

techniques will be utilized for risk mapping and modelling of infectious diseases.

According to the literature, the interactions between host, vector, intermediate hosts and

environment are the main source of a disease spread. The main effort in the second part of the

study is to include these interactions in a spatially explicit environment and simulate their

behaviour in a real-world-like agent based model.


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4- Material and methods

4-1- Case studies

Leishmaniasis caused by protozoan parasites Leishmania, is strongly associated with victims’

living conditions (Kumar, 2013). Leishmaniasis has been considered as one of the most

significant tropical diseases in World Health Organization’s (WHO) reports and is considered as

the second most prevalent parasitic disease after malaria. The parasite could be transmitted by

Phlebotomus sandflies (Swaminath et al., 2006). The number of annual Leishmania infections is

increasing due to man-made environmental changes that causes humans to be more exposed to

the sand fly vector (Desjeux, 2004). Leishmaniasis has three major forms which have different

clinical manifestations (Handman, 2001) including Visceral Leishmaniasis , Cutaneous

Leishmaniasis (CL) and Mucocutaneous leishmaniasis.

4-1-1. Visceral Leishmaniasis

Visceral leishmaniasis (VL), also known as kala-azar, is a zoonotic, vector-borne disease,

endemic in 76 countries. This disease is the second-largest parasitic killer in the world (after

malaria), responsible for an estimated 500,000 infections each year (Desjeux 2001, Palit et al.

2005). Between 20 000 to 40 000 of people die from VL annually (Alvar, et al., 2012).

According to World Health Organization (WHO) reports, VL is a neglected tropical disease

(WHO report, 2012) that might spread throughout developing countries, where the conditions for

the disease exist. Without any control strategies, the untreated VL can have a fatality rate as high

as 100% within 2 years (WHO, 2013).

Leishmania infantum is the principal agent of human and canine VL in Iran (Mohebali et al.,

2002; 2004). The disease has been reported sporadically in Iran with the north-western and

southern part being the primary, endemic foci, where it is most frequent seen among the rural

population and nomads. From 1996 to 2010, more than 3,000 cases of symptomatic VL were

detected in 31 of Iran's provinces. The majority of the cases (92.8%) were found among children

up to 12 years old (Mohebali, 2012).


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4-1-1.1. Study area for Visceral Leishmaniasis

The social and physical environment of north-western Iran is characterized mainly by the

presence of several factors strongly associated with VL, including nomadic lifestyle, suitable

climatic conditions and large dog populations (i.e. sheepdogs, guard dogs and stray dogs)

(Mirsamadi et al., 2002; Salahi-Moghaddam et al., 2010; Edrissian et al., 1988; Moshfe et al.,

2008; Rajabi et al., 2012). The Meshkin-Shahr district in the Ardabil Province is one of the most

important endemic zones for VL in north-western Iran (Salahi-Moghaddam et al., 2010) and the

Iranian Ministry of Health (MOH) records indicate that the number of VL infections has

noticeably increased in this and neighbouring districts during the last decade (Soleimanzadeh

1993, Tamook et al., 2006). Newer reports shows that VL occurs also in other provinces in

north-western Iran, including East Azerbaijan (Mirsamadi et al., 2002, MOH, 2006; 2008).

Further investigations have shown that VL has become common in two districts of East

Azerbaijan, i.e. Kalaybar and Ahar, which have the largest VL-infected population in the

province (MOH, 2006; 2008, Fallah, 2009; Khanmohammadi, et al., 2010). This part of our

study focused on 800 villages in these two districts: Kalaybar in the north-eastern part of East

Azerbaijan and Ahar, located immediately south of Kalaybar (Figure 1).The above-mentioned

Iranian endemic areas border three other countries: Azerbaijan, Armenia, and Turkey, raising

substantial national and international concern over the probability of international spread.


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Figure 2- study area for Visceral Leishmaniasis, Northwest Iran

4-1-2. Cutaneous Leishmaniasis

Cutaneous Leishmaniasis (CL) is the most common kind of Leishmaniasis. This form is not fatal

but it leaves severe scars on the victim’s skin exactly where it has been bitten by the sand fly. It

has a relative long incubation period in comparison to other vector borne diseases and it can last

from few days to months.

WHO’s reports indicate that there are 1.5-2.0 million new cases of cutaneous leishmaniasis

(CL)(Alvar et al., 2012). Almost 90% of all cases of CL now happen in Iran , Syria, Saudi

Arabia, Afghanistan, Peru, and Brazil (Kumar, 2013). CL represents the most frequent vector-

borne disease in Iran with an average of more than 22,000 cases in the last decade (Oshaghi et

al., 2010).

4-1-2.1. Study area for Cutaneous Leishmaniasis Isfahan province, at the centre of Iran, has long been known as one of the most important

endemic areas of CL (Arjmand et al., 2014, Nadim and Faghih, 1968) and still has a large

number of annual infections with approx. 2200 in year 2013. CL due to L.major and L.tropica

protozoa has epidemic status in Isfahan. Rhombomys opimus, a domestic rodent, is the main


17

reservoir host and Phlebotomus papatasi , is the most common sand fly specie in Isfahan

(Emami et al., 2009).

Surprisingly, considering the huge effort of preventive measures from health authorities and

large number of academic efforts in Isfahan province through the past 30 years, the number of

infections is still prominent and recently CL has started spreading to the non-endemic parts of

the province (Emami et al., 2009, Arjmand et al., 2014).

Figure 3- Study area for Cutaneous Leishmaniasis, Isfahan province, Iran.

4-2- Methods Artificial intelligence (AI) refers to any computer system that uses a logical process to learn and

develop based on the surrounding environment and prior mistakes. Accordingly, AI is the

intelligence revealed by machines or software. Several types of AI technology are available. In

this study three types of AI techniques will be explored including artificial neural networks

(ANN), Agent based Modeling (ABM) and fuzzy logic.


18

4-2-1. Artificial neural networks Artificial neural networks (ANNs) mimic the nervous systems of the human brain. They can be

defined as simplified mathematical models trained to learn (Tsoukalas and Uhrig, 1997; Beucher

et al., 2013). The fundamental elements are neurons, which receive multiple signals, combine

and modify them to transmit the result to other neurons. In an ANN, the artificial neurons are

usually organized in layers (Beucher et al., 2013). In this study, we use a neural network model

called Radial Basis Functional Link Nets (RBFLN) described by Looney (1997; 2002). This

specific model is chosen since, unlike other ANNs, it requires a smaller volume of training data

(Looney, 1997; 2002). In spatial epidemiology studies, especially in poorly explored areas, the

number of known areas susceptible to a specific endemicity like VL is low (i.e. the amount of

training data that can be obtained is low). One of the advantages of RBFLN, desirable for disease

predictive mapping, is that a smaller number of hidden nodes can be used and hence good results

can still be achieved even with a small amount of training data (Looney, 1997; Looney and Yu,

2001; Looney, 2002).

An RBFLN is composed of three layers (Figure 6): (1) an input layer of N nodes, where each

node has one input (e.g. an area in a map layer) representing a feature vector of N elements; (2) a

hidden layer of M artificial neurons, where each neuron signifies a radial basis function (RBF);

(3) an output layer of J artificial neurons (Looney, 1997; Looney and Yu, 2001, Beucher et al.,

2013).

Figure 4- General structure of RBFLN


19

4-2-2. Agent based modeling Agent based modeling has been increasingly applied to the simulation of geographical dynamics

of wide variety of systems. ABM allows the disaggregation of systems into separate components

(the “agents”) that can have their own features and rule sets(Crooks and Heppenstall, 2012). An

agent is anything that can be viewed as perceiving its environment and acting through upon that

environment through predefined rules (ref). Agent-based models (ABM) are type of the

computational models in which simulating the actions and interactions of autonomous “agents”

are performed with a view to assessing their effects on the system as a whole (ref). They can

simulate the simultaneous processes and interactions of multiple agents in an attempt to

reconstruct and predict the appearance of complex phenomena (ref). Most agent-based models

are composed of: (1) agents; (2) decision-making processes; (3) adaptive processes; (4)

interactions; and (5) environment. Agent-based models comprise dynamically interacting rule-

based agents. The systems within which they interact can create real-world-like complexity (ref).

Usually agents are placed in space and time and reside in networks. The location of the agents

and their responsive behavior are encoded in algorithmic form in computer programs (ref).

4-2-3. Fuzzy Logic Real-world models of complex phenomena such as epidemics are associated with two kinds of

uncertainties (i) stochastic and (ii) systematic uncertainties(Porwal et al., 2003). Systematic

uncertainties arise from vagueness (or ‘fuzziness’) in the definition of the phenomena and their

parameters and could be best treated using the concepts of fuzzy sets and fuzzy logic(Zadeh,

1965). The term "fuzzy logic" was presented with the 1965 proposal of fuzzy set theory by Lotfi

A. Zadeh. Fuzzy logic has been applied to many fields in artificial intelligence. Fuzzy logic is a

class of many-valued logic that discusses approximate, rather than fixed and exact reasoning.

Compared to traditional binary logic (where variables may take on true or false values), fuzzy

logic variables may have a truth value that ranges in degree between 0 and 1.

5- Progress

5-1- Artificial Neural Networks for Visceral Leishmaniasis The main aim of this part of the study is to develop a model based on ANN to map the potential

risk-prone areas in relation to VL outbreak in the study area. Since VL is a vector-borne disease,

which spreads mostly by means of reservoir hosts (e.g. dogs), it is not possible to collect


20

complete input data for the model. ANN represents a powerful data-driven approach that models

the behavior of VL vectors and reservoir hosts based on a sample data collection investigating

the complex association between the environmental properties of the study area, the socio-

economic factors and the spread of VL. With this in mind, we aim to develop an environmental

model for predictive mapping of the most susceptible areas for VL, taking into account

meteorological, topographical, demographic and socio-economic factors. The map would

provide new insight, which would help to develop strategies for preventing further spread of VL

in the study area.

The implementation and modeling of this part of the study has been finished and the results are

published in a journal paper (Figure 7).

Figure 5-published paper according to the results of first part of research

5-2- Agent based modeling for Cutaneous Leishmaniasis Agent-based modeling approaches have been recently used to overcome the heterogeneity

problem and other challenges of conventional models (Crooks and Heppenstall, 2012). Utilizing

diverse interactions between individual agents, or an individual agent and the environment, gives

the ABM methods the ability to simulate the processes and their impacts realistically (Crooks

and Heppenstall, 2012, Crooks and Hailegiorgis, 2014). They simulate complex situations where

agents make rational decisions based on their knowledge of the environment (Birkin and Wu,

2012). Agent-based simulations can exhibit different time scale processes into a single model

which gives them the ability to accurately simulate a particular phenomenon such as epidemics

(O'Sullivan, 2001). Therefore they are more akin to the reality, and are capable of representing

the social theory and map more naturally to the structure of problem than other modeling


21

approaches (Van Dyke Parunak et al., 1998, Crooks and Heppenstall, 2012). Moreover, ABMs

are scale independent and their mobility will help spatial simulations. Likewise, ABMs

flexibility will be highlighted in relation to geospatial modeling particularly in terms of potential

variables and parameters that can be specified(Crooks and Heppenstall, 2012). In this regard, the

integration of ABM and GIS would make a powerful approach to evaluate and analyze a disease

spread (Perez and Dragicevic, 2009). As a result, ABMs have been increasingly used in spatial

epidemiology applications for different diseases.

An agent based modeling approach has been developed for CL spread modeling in which the

mentioned drawbacks of the current studies are covered. First, dynamic interactions between

environments, vectors and hosts have been simulated. Secondly, the simulation includes a wide

variety of socio-ecological factors which makes it more realistic. Third, different types of agents

have been defined to model the behavior of vectors, hosts and environment in relation to CL

spread.

With this in mind, in this part research, a comprehensive epidemiological approach has been

conducted to analyze and evaluate the spread of CL using an agent-based modeling

methodology. The study area is mainly focused on Isfahan province in central part of Iran .

The modeling and implementation of this part of the study is being performed and we are

preparing the first manuscript for the results acquired from ABM explorations. `

5-3- Knowledge- and data-driven spatial modelling methods for VL susceptibility mapping

This part of work aims to assess the disease susceptibility of an endemic area , using three

methods: the weight of evidence statistical method (WOE) that is based on bivariate statistical

analysis , logistic regression approach and fuzzy logic.

To document the efficiency of these methods, a risky area in relation to VL outbreak, in

northwestern Iran is chosen.

The disease susceptibility map derived from WOE method will be compared with those

produced from the logistic regression and fuzzy logic and the results will be analyzed. As a


22

general aspect, it should be concluded which of the methods is recommended as the most

suitable for disease mapping regarding the study area and input-data.

This part of the study has also finished and the draft of manuscript is prepared.

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