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Decision Support Conceptual Framework for Zoonosis Emerging System Article · March 2010 DOI: 10.1109/ICCEA.2010.240
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2010 Second International Conference on Computer Engineering and Applications
Decision Support Conceptual Framework for Zoonosis Emerging System Adhistya Erna Permanasari1,2 , Dayang Rohaya Awang Rambli1 , P. Dhanapal Durai Dominic 1 1
Computer and Information Science Dept., Universit i Teknonologi PETRONA S Bandar Seri Iskandar, 31750 Tronoh, Perak, Malaysia 2 Electrical Engineering Dept., Gadjah Mada Un iversity Jl. Grafika no. 2, Yogyakarta 55281, Indonesia
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[email protected] Abstract— Emerging zoonosis represents an increasing threat to animal and human health. Zoonosis is any infectious disease that is able to be transmitted from other animals, both wild and domestic, to humans. The increasing number of zoonotic diseases coupled with the frequency of occurrences, especially lately, has made the need to study and understand theses diseases a major concern among researchers from various disciplines. It is important to have a single system that is able to analyze the relationship between different diseases in emergence zoonosis. However, the development of such system presents a daunting and yet to be explored task. Many factors have been attributed but inadequate information has been identified as a major challenge. In this paper, a review on zoonosis aspects and previous research on zoonosis are presented. Based on this review, a framework for developing an emerging zoonosis system is proposed. Therefore, the initial findings of framework were also presented. The system is considered as a zoonosis risk-emergence based on similar pattern recognition of zoonosis domain across different zoonotic diseases. Potential challenges in developing an emerging zoonosis model based on quantitative approach to unify zoonosis knowledge from different diseases into a single system were highlighted.
to human through many ways, i.e. d irect contact with infected animal, infected food, raw milk, bite/scratch, stitch, and airborne. Zoonosis evolution from the orig inal form could cause the newly emerging zoonotic disease. It was reported by Dr Cathy Roth [4] that: “Epidemic threats continue to be characterized by unexpected events with unstable or poorly understood patterns of transmission and pathogenesis, and bring with them the potential for large public health and economic impact”. Indeed this is evidenced in a report presented by WHO [4] associating microbiological factors with the agent, the animal hosts/reservoirs and the human victims which could result in a new variant of a pathogen that is capable of jumping the species barrier. For examp le, Influenza A virus mechanism have jumped fro m wild waterfo wl species into domestic farm, farm animal, and humans . To date, several new emerg ing zoonoses documented (avian influenza, Ebola, Marburg, Nipah, and SARS v iruses) and re-emerg ing zoonosis (cholera, dengue, measles, meningit is, shigellosis, and yellow fever). The number of zoonotic disease incidence and frequency has increased in the past 30 years [8]. Some of these zoonotic diseases recently have major outbreaks worldwide which resulted in many losses of lives both to humans and animals. The losses to farmer because of the livestock death are an example of zoonosis impact to economy. While the examp le of zoonosis impact to environment is the side effect of pesticide that use to kill the animal wh ich caused zoonosis. Considering this problem, a number of researches on emerg ing zoonosis have been conducted [2, 7, 9-23]; some of which have resulted in the development of zoonosis systems. However, some existing emerging zoonosis systems have been very specific, focusing on one specific disease only [9, 12, 13, 15, 19, 21, 24-32]. Due to the growing number of emerging zoonotic diseases, it is important to have a single system that is able to analyze the relationship between different diseases in emergence zoonosis. Currently only a few of such systems exists. These systems are further limited in terms of the number of domain included [33, 34]. Inadequate information of zoonosis was assumed as the main
Keywords - Salmonellosis; zoonosis; forecasting; Artificial Neural Network (ANN)
I.
INT RODUCTION
The monitoring and evaluating the effect of animal disease to the environment, especially on human being is a very important problem arising. It was estimated that 75% of emerg ing disease infections to humans come fro m animal origin [1-5]. Th is is mainly due to many animal diseases that influence human health is acquired because we live in the same environment with some animal [6]. Zoonosis is any infectious disease that is able to be transmitted from other animals, both wild and domestic, to humans. Contact between human and animal can be seen anywhere, from home, surrounding residence, working place, and public area, such as animal displays, petting zoos, animal swap meets, pet stores, zoologic institutions, nature parks, circuses, carnivals, farm tours, livestock-birthing exhibits, county or state fairs, schools, and wildlife photo opportunities [7]. Thus, zoonotic disease can be transmitted
978-0-7695-3982-9/10 $26.00 © 2010 IEEE DOI 10.1109/ICCEA.2010.240
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important factor to develop a more co mprehensive system [1, 3, 6, 35]. Comp lex and unpredictable nature of zoonosis evolution further justify the pressing need for the creation of a pool of knowledge on zoonosis into one central system. The aim of the research project reported in this paper is to propose a framework for developing an emerging zoonosis system that could analyze similar patterns on zoonosis factor and its relationship to zoonosis emergence. The paper is organized as follows. Section 2 introduces emerg ing zoonosis. Section 3 presents DSS concepts. Section 4 described a proposed conceptual framework. Finally, Section 6 contains the conclusion and future work.
Example: the Anthra x attack in USA and Japan. III.
DECISION SUPPORT SYST EM (DSS)
The concept of Decision Support System (DSS) is very broad because of many diverse approaches and a wide range of domains in wh ich decisions are made. DSS terminology refers to a class of computer-based information systems including knowledge based systems that support decision making activities. In general, it can say that a DSS is a computerized system for helping make decisions. Turban defines DSS as an approach (or methodology) for supporting decision making [36]. Besides, a DSS [37] also can be defined as a system under the control of one or more decision makers that assists in the activity of decision making by providing an organized set of tools intended to impart structure to portions of the decision-making situation and to improve the ultimate effectiveness of the decision of the outcome. A DSS application can be co mposed of the subsystems [36]. Despite of the variety of DSS application, basically DSS consists of three main co mponents : database management system, model base management system, and user interface system.
II. EMERGING ZOONOSIS This section gives a briefly description about emerging zoonosis. As an introduction of emerging zoonosis, the general explanation of emerging infectious diseases (EID) in human is given. EID is any disease that has recently increased in incident, geographic location, or host range (i.e. tuberculosis, West Nile Fever, and yellow fever), diseases caused by new variants of known pathogen (i.e. Avian Influenza virus, SARS, Nipah Virus, and Ebola virus), and bacteria newly resistant to antibiotics [6]. Among EID are zoonoses because according to estimation 75% of EID co me fro m animal. A new defin ition for emerging zoonosis was made at the WHO/FAO/OIE joint consultation in Geneva 3-5 May 2004 [18]: “An emerging zoonosis is a zoonosis that is newly recognized or newly evolved, or that has already occurred previously but shows an increase in incidence or expansion in geographical, host or vector range. Some of these diseases possibly develop and become transmissible between human beings.”
IV.
CONCEPTUAL FRAMEWORK FOR DEVELOPING DSS OF ZOONOSIS EMERGING SYST EM
The zoonosis epidemics arise and have the potential threat for public health and economic impact. Several methods have been used in order to overcome zoonosis emergence and it effect to environment. Different data modeling could be selected to support such zoonosis emerging system. W ithin zoonosis data modeling phase, a qualitative approach has been chosen [1, 30, 34] rather than quantitative method [33]. Contrary to quantitative method, developing qualitative method requires much more t ime in collecting data and less able to be generalized. Quantitative data approach is more efficient than qualitative approach when there is only a few supported data. The overall aim of this research is to develop a zoonosis emerging system that able to analyze pattern of zoonosis factors fro m several zoonosis and predict emerging of zoonosis based on domain input. Due to the problem of gathering zoonosis information, this research focus on developing a quantitative model approach for mapping pattern of zoonosis data domain using a few in itial data. In order to achieve the goal, three components of DSS are proposed (Figure 1):
EID could be categorized into these following classifications [23]: 1) Newly emerging infections: This group consists of any infectious diseases that have not been previously recognized in human. Example: Arenavirus hemorrhagic fevers, Hantavirus, Variant Creut zfeldt-Jakob disease (vCJD), Eschericia coli. 2) Re-emerging or resurging infections: Many factors, such as microbial evolutionary vigor, zoonotic encounters and environmental encroachment, could cause re-emerg ing disease. Example: Malaria, Influenza A v irus, SA RS, West Nile Virus (WNV), Cholera. 3) Deliberately emerging infections: It consists of any microbes that deliberately developed by human for the crime.
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add data 2007
1 Zoonosis data (1993-2006)
Zoonosis data (1993-2007)
Forecasting Technique 1
Forecasting Technique 2
Forecasting Technique 3
Forecasting Technique 4
Forecast number of 2008 and 2009
Forecast number of 2008 and 2009
Forecast number of 2008 and 2009
Forecast number of 2008 and 2009
2
ANOVA Blocked Design
Duncan Multiple Test
3 What If (sensitivity) analysis
What If (sensitivity) analysis
What If (sensitivity) analysis
What If (sensitivity) analysis
Figure 1. Framework of of DSS for Emerging Zoonosis
on the previous range (1993-2006). Finally, the result was analyzed to observe the fluctuation between them.
1) Database management subsystem In this co mponent, zoonosis database was established by collecting monthly data fro m 1993 – 2006 for two kind seasonal model, additive model (Salmonellosis time series) and mu ltip licative model (Tuberculosisfor). 2) Model base management subsystem The main role o f this component is to transform data fro m database management system into informat ion that can be used in decision making. For this research, the core of model base management system was developed by applying different forecasting methods. 3) User interface subsystem The aim of this co mponent is to provide such interaction med ia between user and the model. Through this system, user can fill in d ifferent input and get a result corresponding to the input value. At this research, what-if analysis (sensitivity analysis) was chosen to model the user interface system. The latest data (2007) were input into the database. Furthermore, the forecast results of the recent range (1993-2007) were co mpared with the forecast based
V.
INIT IAL FINDINGS
The proposed zoonosis prediction framewo rk provided a systematic process for aiding in the decision as to forecasting the future number of seasonal zoonosis. The DSS framework covered a co mprehensive decision support components with its own feature. As a DSS, it took into consideration of seasonal model, both of mu lt iplicative or additive models. Multiplicative models is a model when time series that having a constant trend, while additive models is a time series model that exh ibits a relat ively upward o r downward trend [38]. Salmonellosis was selected as a case study of additive model and Tuberculosis was used for mu ltip licat ive models. Data for case studies were obtained from the s ummary of notifiable diseases in United States. It was being collected fro m the Morb idity and Mortality Weekly Report (MMWR) that published by Centers for Disease Control and
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order to create a DSS that could predict zoonosis incidence not only in seasonal diseases but also in non-seasonal diseases.
Prevention (CDC) fro m 1993 to 2007. Therefore, some initial works have been done for both diseases. Nu mber of Salmonellosis incidence in US was projected up to a year ahead [39]. Monthly data set from January 1993 to December 2006 was collected for model development by using Seasonal ARIMA (SARIMA) method. Several model was applied and selected based on BIC and AIC. The most appropriate model was SA RIMA(9,0,14)(12,1,24)12 . The forecast result was evaluated by using different stand alone measure error and relative measure. Artificial neural network (ANN) was applied to predict number of incidence of Salmonellosis [41]. Time series data used was similar with [39]. A total of 168 monthly data were collected. The first 12 data were used for the inputs. It left for 156 data that divide into three parts: 84 data used fo r training (54%), 36 data used for selection, and 36 data used for testing (23%). Selection of the best network was done fro m the calculat ion of error measures. The best model was judged by the least MAPE. It resulted the three layer best network in iteration index 508 with input layer, hidden layer, and output layer consists of 12 nodes, 5 nodes, and 1 node respectively. The error measures yielded MAPE was 10.761 and Theil’s U was 0.209. A comparat ive study of Tuberculosis forecasting was conducted by applying three methods, namely regression analysis, decomposition, and Holt Winter’s [40]. Tuberculosis time series in US fro m 1993-2006 was collected fro m CDC US report. The decreasing variation of time series plot was observed to determine the suitable model. Based on the results, mu ltiplicative decomposition method and multip licat ive Holt-W inter’s was chosen. Regression method was derived by using dummy variab les. Because of a seasonal pattern in time series data, seasonal factors were included into the calculat ion. Predicted values up to 12 months ahead were obtained. Hence, the comparative method based on error measures yielded Holt Winter as the fittest method with MAD was 81.922, MSE was 12551.116, MAPE was 6.301, and Theil’s U value was 0.037. VI.
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CONCLUSION
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