a computational system for support to actions on dengue fever control and management

8/13/2019 A computational system for support to actions on dengue fever control and management

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International J ournal of Advanced Computer Science, Vol. 3, No. 7, Pp. 363-367, Jul., 2013.

ManuscriptReceived:26,Mar.,2013Revised:14,Apr.,2013Accepted:21,May,2013Published:

15,Jun.,2013Keywords

Dengue; InformationSystem;

Agent-Based Modeling; Dengue´sSimulation

Abstract This paper describes theconstruction of a computing system whosegoal is articulate specific actions aiming toprovide tools and strategies for the combatand control of dengue fever. The system iscomposed by three independent moduleswhich could work together in a multi-level

structured system. The first module is aninformation system to collect and record dataabout dengue fever. The georeferencingmodule provides tools to build maps,statistical and environmental analysis ofdengue events and their respectivegeoreferenced exhibition. Finally, a modulefor simulations of plausible scenarios aboutdengue spreading in a given area. When fullycompleted the system is expected to provideuseful tools for the design control strategiesand political policies for public health relatedto dengue fever. The built system has as acase of study the city of Cascavel, Parana,Brazil.

1. IntroductionThe epidemiology deals with the frequency, distribution

and determinant aspects of the events related to health statesin specific populations as well as the application ofrespective gathered knowledge in the control of health

problems. Among other additional goals the epidemiologyaims to identify and provide a better comprehension of thecausal agent and the factors related to health disorders,identify and clarify the patterns of geographical distributionof diseases, establish goals, control strategies and

preventive measures against the diseases and providesupport for planning, management and evaluation of publichealth policies [1]. In this context, the relevance of thistheme induces and justifies all the studies, research and

This work was supported by the Programa de Apoio a Núcleos deExcelência (Grant N05500 30/2010−7, CNPq, PRONEX -Dengue - Brazil),Fundação Araucária, Universidade Estadual do Oeste do Paraná(UNIOESTE).

Dr. Rogério Luís Rizzi, UNIOESTE, CCET (rogerío.rízzí@uníoeste.br) Dr. Claudia Brandelero Rizzi, UNIOESTE, CCET(claudía.rízzí@uníoeste.br)

Dr. Reginaldo A. Zara, UNIOESTE, CCET (regínaldo.zara@uníoeste.br)

André Luiz de Barros Luchesi, UNIOESTE, CCET(andre e _luchesí@msn.com)

Pétterson Vinícius Pramiu UNIOESTE, CCET (ppramí[email protected])

activities related to epidemiology such as the present work.Dengue fever is a vector borne infectious disease caused

by a virus and could be benign or severe and, depending onthe form that it expresses itself could be classified as silentor unapparent infection, dengue fever, dengue hemorrhagicfever, dengue shock syndrome.

In Brazil the dengue vector transmitting is the femalemosquito Aedes aegypti . The mean lifetime of an adultfemale mosquito is about 45 days and once infected by thevirus, she will remain infected until the end of her life. Thecycle of transmission occurs from an infected person to thesusceptible mosquito which becomes infected and frominfected mosquitoes to human susceptible individuals(considering an initial scenario in which all the individualsare susceptible).

The transmission and spreading of the dengue fever aresupposed to be directly affected by the contact patterns ofindividuals (such as small-world phenomenon) [2], the

places that those individuals attend, the environmentalcharacteristic of these places, the local climatic conditions,etc. In this way a myriad of elements should be consideredwhen one aims to perform modeling and simulations ofrealistic models of dengue spreading.

Compartmental modeling divides the populations ofhumans and mosquitoes into categories or compartmentsaccording to their relative state of health and defines flowrates between a pair of compartments taking into accountthe characteristics of the disease, the habitude of vector´stransmission among other features. The host (human)

population is divided into classes being the most commonthe susceptible (S), exposed (latent) (E), infected (I) andremoved (R) classes. In this case, for closed populations, itis considered that the number of individuals of the

population, N, is such that N = S + E + I + R, where S is thenumber of susceptible individuals, E is the number ofexposed individuals, I is the number of infected individualsand R is the number of recovered individuals. Since thevectors do not recover themselves from the disease only thestates susceptible and infected may be considered for adultvectors in a such way that the number of vectors V is given

by V = S V + IV, where S V and I V are the number ofsusceptible and infected vectors, respectively [3].

This paper aims to present the current stage ofdevelopment of a computer system to monitoring dengueevents. This system is composed of three main modules

which, when fully completed, will interact with each other:a module that is an Information System for Acquisition, Handling and Processing of Data on Dengue events

A Computational System for Support to Actions on

Dengue Fever Control and Management Rogério Luis Rizzi, Claudia Brandelero Rizzi, Reginaldo A. Zara, André Luiz de Barros Luchesi

& Pétterson Vinícius Pramiu

mailto:[email protected]












International J ournal of Advanced C omputer Science, Vol. 3, No. 7, Pp. 363-367, Jul., 2013.

International J ournal Publishers G roup ( IJPG ) ©

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(SIGDENGUE) and other two being a module containingGeoreferencing mechanisms that allow the actions includingvisualize data on dengue in maps, select regions forapplication of mosquitoes control strategies, select specific

points of interest in a given area and a Simulation module,

which includes epidemiological models that makes possibleto simulate the dynamics of spreading dengue in given area.This article presents an overview of the modules composingthe computational environment. Moreover, general aspectsof an epidemiological model based on computational agentsto be included in the simulation module are discussed.

This work is included as an action of the projectPRONEX-Dengue (Support Program for Centers ofExcellence), whose purpose is to develop mathematicalmodels for application in Dengue control and has beensupported by Brazilian Research agencies, mainly the

National Council for Scientific and TechnologicalDevelopment (CNPq). The development of this computersystem (Modules I, II and III) is one action which aims tocontribute to decision making about the control of dengueand covers, as a case of study, the city of Cascavel, Parana,Brazil. It is necessary to say that this work fulfills all therequirements of ethics laws currently in effect in Brazil.

2. The SIGDENGUE InformationSystem

The main goals of the SIGDENGUE as a georeferencedinformation system are twofold. In first place it aims tointegrate data and information on dengue events that are

currently available in a scattered and disordered way, aswell as other important data related to the diseaseoccurrence such as meteorological information, density ofhost population and their geographic distribution over themunicipality. The second goal is to enable a quickrecovering of data stored in different kinds of reports and

build statistical analysis, as well as the visualization map ofthe cases (suspected, reported and confirmed) of dengue.

The SIGDENGUE allows the filling of informationabout different cases of dengue including the record ofsuspected cases of the disease. Among other data to beinserted in the register of cases are the addresses that the

person frequently attends such as the home address,working place, study and leisure, as shown in the Figure 1and Figure 2.

Once a particular suspicious case of dengue is notifiedto the system, the SIGDENGUE points to a controloperation performed by the Division of Disease of theCascavel City Hall around the registered address. Thisoperation named Raio aims to visit all places in an area

bounded by a radius of 300 meters from the registeredaddress. In this the operation the inspection, treatment ofmosquitoes breeding points, collecting larvae and pupae arecarried out. Information about each breeding vessels areregistered in the system including the results of thelaboratory examination of these samples as seen in theFigure 3.

Additional functionalities of the system should also be

mentioned since the system provides other applications suchas the registry of health field agents, record of insecticideapplication, indication and management of places with highconcentration of mosquitoes breeding (classified as strategic

points).

At the current stage of development the SIGDENGUEhas been already fed with available registered data. Thesedata include the recorded information collected by the staffof health agents during field visits carried out in the cityover the period 2007 to 2010 and the reported cases ofdisease in the same period. The data collected in the period2011 and 2012 are being manually recovered from the

physical forms and gradually included in the SIGDENGUEsystem. Fortunately, the data about the year 2013 are beingregistered directly into the system. It is expected thathistorical series about the occurrence of dengue cases inCascavel for the period 2007/2012 could be constructedsoon.

Fig. 1 Data entry for identification of a suspicious case of dengue fever.

Fig. 2 Registration form gathering information about the workplace of asuspicious case individual.



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Fig. 3 Register form of samples of recovered mosquitoes at the locus of adengue suspicious event.

3. Georeferencing Module

The purposes of the georeferencing module within theSIGDENGUE are developing and providing tools to makeeasier perform simulations of realistic dengue spreadingmodels based on the georeferenced the real cases extractedfrom the historical series as well as the exhibition of theregistered cases over the space.

To accomplish the goals, the maps have been handled inan ESRI Shape file format [4], containing three files: a .shpfile which is the drawing of the map, an index file used asreference for the file. shp, aiming to speed up the reading ofthe shape file, and a .dbf file, that stores features, attributesand information related to the shape file. For subsequentchanges on the map, such as insertion of points indicatingsome new information, the system transfers the map to adatabase (in PostGre Gis), in order to make the handleeasier. In order to perform such kind of data processing ageoreferenced system for information view (GeoVisi) have

been designed and implemented. The GeoVisi classifies theinformation into categories according to their nature andallows to custom the final results in a visual map. The finalmaps could be customized through insertion, edition orremoval of information about a particular geo-referenced

place such as squares, schools, marketplaces, etc. Sincethese places could be classified by the GeoVisi according totheir nature and filtering by their activities, for example,leisure, educational or commercial places, the map presentsthe relevant information corresponding to the customizationchoices. The GeoVisi also helps the simulation module in

both the preprocessing and post processing. In the preprocessing the GeoVisi allows the insertion, edition,removal and selection of points in the map as well as theselection of maps used in the simulation, indicating theselected points in the map through customized iconscontaining all the required information. In the posprocessingthe GeoVisi provides reports extracted from the resulting

data.Some representative views provided by the GeoVisi may

be seen in the Figure 4. The functionalities of the softwareare sorted in taps at the left side of the interface. In each tapare collected information about specific subjects to be used

in simulation and which could be combined in the preprocessing phase. In the right side of the interface a partial view of Cascavel downtown together to the iconsrepresenting workplaces like markets, stores, groceries(green icon) and educational places such as schools, Collegeor Universities (red icons).

Fig. 4 GeoVisi interface exhibiting the functional taps and a partial view ofCascavel downtown in a map.

The GeoVisi interface allows an overview of a givenarea together the focal points indicated by the icons. The setof information gathered by each icon for the specific locusmay be accessed by the user who may get knowledge aboutdisease at focal point and its surrounding area. In this waythe environmental situation and the influence on the lifecondition of infected individuals may be analyzed andcontrol strategies to prevent the disease spreading could bedesigned.

4. Simulation Module

The simulation module deals with the data obtainedfrom module I (SIGDENGUE) and module II(Georeferencing) to perform the computational modeling ofdengue spreading dynamics. Different approaches are usedfor modeling, including compartmental representation ofepidemiological models, complex networks representationfor pattern of contacts between individuals, dynamiccellular automata and computational agents. The main goalof the simulation module is the analysis of plausible

scenarios about dengue spreading in a given area providinga better comprehension of the causal agent and the factors



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related to health disorders which could be useful forestablish control strategies and preventive measures againstthe disease. Although different simulation approachescompose this module, in this section only the model basedcomputational agent technique is discussed as a

representative approach.In general, an unified definition of computational agents

widely accepted by the community has not been founded.However, one can use the proposition of the Foundation ofIntelligent Physical Agents (FIPA) which defines acomputational agent as an entity encapsulating a state, a

behavior, control of processing, interaction skills andcommunication capability with other entities [8]. In thisway, the computational agents constitute a useful techniquewhen the focus is the individual behavior.

Computer modeling based on individuals has beenemployed to investigation of the transmission of diseases

because this kind of approach is supposed to handlecomplex emergent phenomena resulting from theinteractions between the elements of the system and theenvironment. Moreover when computer simulations are

performed one could include geographical variations besides temporal, social and ecologic aspects of theenvironment in order to provide a better comprehensionabout the different scenarios that represent differentecological or epidemiological situations.

The simulations employ the approach of thecompartmental model (S + E + I + R) considering that thehosts are organized in a complex network topology andtakes into account the spatial-temporal dynamics of theinteractions between individuals.

In order to get insights about the behavior of the spreaddisease in realistic scenarios the simulation allowsintroduction of spatial and environmental aspects such ascenters of breeding, feeding and motion of hosts. The hostindividualization is based on Agent Systems that, for thecase of dengue spread, has shown versatility on theaccuracy of the parameters, on monitoring individualizedagent and in the global implications of their relationships.

The computational agents are composed basically bysensors, actuators and a mental architecture. Sensors can beviewed as an entity consisting of all incoming messages tothe agent and that are understood by it. The architecture ofmental state is a simplification strategy BDI (Beliefs, Desire,Intention). The actuators are being modeled as messagesfrom the overall stock of intention. For the development is

being used the framework JADE (Java Agent DevelopmentFramework) [5].

JADE is a framework for multi-agent systemimplementation, working independently of the applicationin a simplified way. It has been designed following the FIPAspecifications and using Java defined classes and offeringsupport for life cycles and the logic of the agent’s core.Moreover, the communication occurs in a distributed way

by means of messages exchange. It also provides graphicaltools which help the simulation designed [9].

In an abstract way, the proposed agent model is may bedescribed as a three interacting layers: the environmentallayer, the mosquito agent layer and the human agent layer.

The environment layer uses a map generated by the GeoVisitool associating meteorological information, populationdensity, geographical distribution of points of interest andother available data to compose and characterize the localenvironmental conditions of the area to be analyzed. The

Aedes aegypty mosquito layer contains, described as afunction of time, all the interactions of the agents in theirgeo-referenced environment. This layer is shall be useful tocollect information about dynamic behavior of infestation

by mosquitoes as well as to estimate the critical areas overthe map, as have been done by Almeida et al [6]. Theagent’s interaction reflects the relationship among the set ofthe agent sensors and the mental state’s architecture in agiven time step. The simulation module aims to maximizethe interaction among a mosquito agent and other agents,

being or not a mosquito, under a set of suitable constraints.The human agent layer describes the human behavior as afunction of time, simulating the dengue spreading as have

been done by Tao et al [7]. The possible interaction amongthe agents is determined mainly by their connectivity to theneighborhood which defines their contact pattern. In thiscase graphs of small world connectivity pattern has beenused to represent the social connections and the humanmotion through the available area, for example, thedislocations from workplace to home or to educational sitesand so on. Due to the scope of the problem, the agents arecharacterized by low cognition level but the communicationskills and social relationships are essential to the modelingand deserve continuous improvement. In this way, thespecific features related to the social relations due the work,education and leisure of human agents and their impact onthe dynamics of dengue fever spreading are being includedin the agent based model.

In order to perform the simulation it is necessary specifya set of parameters establishing the initial conditions of theenvironment and the internal agent based model. These

parameters constitute by: Topological data: identify the graph nodes, edges

and connectivity pattern as well as the mobilityinformation needed for determine the localcharacteristics and establish the connect-mobilityrules to the transport of agents over thegeographical (and logic) area.

Age structure data: specify the agent’s behavioraccording to their respective age, since it has beentake into account that the social relationships areage-dependent.

Demographic data: distribute the agents over themap taking into account realistic mean density

population according to the local characteristicsand time period.

Epidemiological data: specify the effectivecontact, recovering and loss of immunity rates usedto determine the change of health state.

Initial population: inform about the initial densityof agents in the states Susceptible, Infecting and

Removed.



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5. Comments and Discussion

This paper discusses the present stage of development ofa computational environment for recording, managementand simulation of dengue fever events that could be used todesign control strategies and political policies for publichealth. The computational system is composed by threemodules: an information system, a georeferencing and asimulation module.

The information system module named as InformationSystem for Acquisition, Handling and Processing of Data on

Dengue (SIGDENGUE ) has two main goals: to integratedata and information on dengue events that are currentlyavailable in a scattered and disordered way and to enable aquick recovering of data stored in different kinds of reports

building statistical analysis and the visualization map of theof dengue events. The georeferencing module provides toolsto build maps, statistical and environmental analysis ofdengue events and their respective georeferenced exhibition.The simulation module joins the data obtained from the firstmodule (SIGDENGUE) and from the Georeferencingmodule to perform the computational modeling of denguespreading dynamics using different approaches. The maingoal of the simulations is the analysis of plausible scenariosabout dengue spreading in a given area providing tools andknowledge for establishment of control strategies and

preventive measures against the disease.The first release of the SIGDENGUE system is now in

use by the Endemic Office under the Cascavel City Hallstructure. Furthermore, the first release of GeoVisi is nowready to be used soon to build georeferenced mapsreflecting the recorded dengue events.

Statistical analysis of the available data gathered by theSIGDENGUE have being conducted aiming to find anddescribe eventual correlations among the variablescontaining information about environmental, meteorologicaland spatial-temporal distribution of dengue events.

As explained before the simulation module have beendesigned to perform the computational modeling of denguespreading dynamics using different approaches and usinginformation loaded from the other modules. The interactionamong the three modules occurs in a multi-levelenvironment in which the geographic domain is representedin a discrete form of a lattice together the informationcoming from the georeferenced module.

It is expected that when fully completed, each module ofthe system will interact with each other allowing that theactions including visualize data on dengue in maps, selectregions for application of mosquitoes control strategies,selection of specific interest points in a given area andsimulation of realistic epidemiological models providinguseful tools for the design control strategies and political

policies for public health related to dengue fever.

Acknowledgments

Support Program for Centers of Excellence (Grant N0550030/2010−7, CNPq, PRONEX -Dengue - Brazil),Fundação Araucária, Western Paraná Sate University(UNIOESTE), SIMEPAR – Meteorological Agency fromParaná, Cascavel City Hall.

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[8] FIPA. Foundation of Intelligent Physical Agents.Available on line in: <http://www.fipa.org/>. Lastacess in February 27, 2013.

[9] F. V. Teixeira. JADE: Java Agent DevelopmentFramework. Available on line in:<http://www.dca.fee.unicamp.br/~gudwin/courses/IA009/artigos/IA009_2010_12.pdf>. Last acess in:February 7, 2013.

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