APPLICATION OF DYNAMIC TRANSMISSION VECTOR MODELS AND KNOWLEDGE SHARING TO DETERMINE THE SPREAD AND PREDICTION OF DENGUE FEVER EPIDEMIC An Online System to Predict the Infected Population and Spread of Aedes agepty Mosquito Retno Widyaningrum and Arief Rahman, S.T, M.Sc. Industrial Engineering, Industrial Technology Faculty, Institut Teknologi Sepuluh Nopember (ITS) Jl. Arief Rahman Hakim, Surabaya 60111 E-mail: retno09@mhs.ie.its.ac.id ; rahman.arief@gmail.com. 1 Abstract - Increasing number of dengue cases in Surabaya shows that Surabaya is a city with the potential spread of dengue fever epidemic. Some policy which was designed by the Health Department such as fogging, abateseae, and Pemberantasan Sarang Nyamuk still out of target because of inaccurate predictions. Ineffectiveness eradication of dengue fever epidemic caused of lack of information and knowledge on environmental conditions in Surabaya. Developing spread and prediction system to minimize spread of dengue fever is necessary conducted. Spread and prediction system can improve eradication, prevention, and design spread map of dengue fever epidemic. The online spread and prediction system of dengue fever epidemics will design with knowledge management system (KMS) website based which can be a media of a sharing knowledge. The Transmission Dynamics Vector simulation will use as an approach that used to drawn a complex system and mosquito life cycle which involved a lot of factor that can predict spread of dengue fever epidemics at a time period. The output of this research is website of spread and prediction system of dengue fever epidemics to predict growth rate of Aedes agepthy mosquito, infected and death population of dengue fever ephidemics. Dynamics transmission model used to build model in mosquito model (oviposition rate and pre adult mosquito), infected and death cases in dengue fever. The model of mosquito and infected population can represent system. The deviation of infected population is 0,519. The model of death cases in dengue fever is less prcision with the deviation 1,229. Death cases model need improvement by adding some variables that influence to dengue fever death cases. Spread of dengue fever prediction will help the government, health department to decide the best policies in minimizing the spread of dengue fever epidemics. Keywords: Dengue Fever Epidemic, Knowledge Sharing, Transmission Dynamics Vector I. INTRODUCTION This section will explain about the background of the problem which became base of the research, research question, research objectives, scope of research which contains limitations and assumptions used in the research, also benefits that will be achieved in this research. A. Research Background Dengue fever is a disease caused by the dengue virus which is transmitted through the bite of Aedes aegypti and Aedes albopictus previously been infected by dengue virus from other dengue patients. The mosquito population will rapidly increase during the rainy season. (Ginanjar,2004). Surabaya was the city had the largest number of dengue cases in East Java with the amount up to 4,187 cases. Figure 1. Dengue Fever Cases in Surabaya. (Health Department in Surabaya, 2011) Surabaya Health Department has done some efforts in order to minimize the spread of dengue fever. The efforts are fogging (fumigation to kill dengue mosquitoes), abate
(larvicides that aims to kill mosquito larvae), and Pemberantasan Sarang Nyamuk (PSN). The efforts and policies that created by the government are still not working efficiently. The current policies are less accordance in the real situation. If these problems happen every year then the spread of the Aedes aegypti mosquito cannot be controlled optimally. The spread and prediction system can be enhanced with an integrated knowledge sharing. Knowledge sharing is a method of sharing knowledge among experts in the health sector. This knowledge will give information about the factors that influence the spread of the Aedes aegypti mosquito, symptoms of dengue fever, dengue fever mosquito spread map on some areas in Surabaya, and effective efforts to minimize the number of dengue fever epidemics. Two researches has been conducted in 2010 and 2011, these research is about integrated sharing knowledge in preventing epidemic. In 2010, Satwika has developed a communication media (website) that aims to control the spread of tropical diseases., The website only displays information without give prediction of epidemics distribution in future time. When the prediction of disease distribution can be seen in future time or next period, the government or Health Department can easy make policies to minimize the spread of the epidemic. In 2011, Hudaningsih conducted research with the title Designing Spread and prediction System and Handling Spread of Dengue Hemorrhagic Fever (DHF) using System Dynamics Approach and Knowledge Sharing which the result was the pattern of spread of dengue fever by using a dynamic system.. The lack of dynamics system model that conducted by Hudaningsih are: 1. There was no loop in model to balance steady state condition in system. The sub-models in continuous variable did not have a balance loop, so the model can be invalid. 2. There are many variables used to build system dynamics model and some variables that used have less precise functions such as random function. Based on some lacks of the research, it can become an opportunity for the development in spread and prediction system in dengue fever virus. It is necessary to do a research that able to predict the spread of dengue fever by using a method which is able to model the interaction between mosquito larvae, female mosquitoes, and people in complex system. This study will attempt to predict the spread of dengue fever by using the Transmission Dynamics System. It is necessary to develop the communication media by adding the map of spread dengue fever as predictions for the next period. The research also provided additional information to users about the condition of the region in Surabaya about the critical level of dengue fever epidemics based on historical data of dengue fever. The development can integrate health expert, health department, and people in spread and prediction system to minimize the number of dengue victims. This system also can help the health department in making policy about prevent dengue fever epidemics. This research will design and build an effective spread and prediction system to determine the spread, prevention, and treatment efforts of dengue fever epidemic. It will develop an spread and prediction system, deployment patterns, and designing spread and prediction systems spread of dengue fever by using Dynamic Transmission Vector approach based on sharing knowledge using website. B. Research Question Research question in this research are the way to design spread and prediction system that it used to know the map of spreading dengue fever and the effective way in preventing, and handling dengue fever epidemics using Dynamics Transmission Vector and Knowledge Management with Sharing Knowledge and website based. C. Research Objectives This research has five objectives. They are: 1. Determining variable in transmission dynamics vector in spreading of dengue fever epidemics. 2. Developing and simulating variable in models with transmission dynamics vector in spreading of dengue fever epidemics. 3. Designing an spread and prediction mechanism system in the spread of dengue fever and determine the level of danger of the spread indicator in Surabaya. 4. Designing an online spread and prediction system based on sharing knowledge and website in anticipation of the spread of dengue fever. 5. Designing the operating mechanism of spread and prediction system online so it can be operated by health experts and public. D. Benefits of Research This research will provide advantages for two parties, which are the user community and health professionals such as doctors, government (Department of Health Surabaya), and practitioners the experts or health consultants. Research Benefits for the Community Benefits of research to society are: 1. Increase public knowledge about the development of the spread of dengue in the region. 2. Increase public knowledge about the prevention and control of dengue fever epidemics. 3. Improve health for the people in Surabaya. Research Benefits for Health Specialist Benefits of research for health expert are: 1. Assist the Government, Health Department in Surabaya, to predict the spread of dengue fever based of development function time to increase response level in preventing dengue fever epidemics. 2. Health practitioners and the public can share knowledge and handle disease detection to minimize knowledge gap about dengue fever epidemics together. 2
3. Assist the Government, Health Department in Surabaya, to make policies and control the spread of dengue fever epidemics effectively and efficiently. 3 E. Scope of Research This section consists of two parts, which are the limitations and assumptions that bounds the research. Limitations These are the limitations of the research, 1. The environmental factors that considered in the model are temperature, rainfall, and wind speed. (Uzwatun Hasanah, 2007), (Fitriyani, 2007), and (Szu-Chieh Chen and Meng-Huan Hsieh, 2012) 2. The social factors that considered in the model are the amount of population growth, growth rate, and mortality rate in dengue fever cases. (Adams and Boots, 2010) and (Szu-Chieh Chen and Meng-Huan Hsieh, 2012) 3. The medical factors that considered in the model are the recovery factor of infected person with dengue fever and the immune system in their body. 4. The research area for designing spread and prediction system in dengue fever epidemic is Surabaya. 5. Horizon time in predicting the spread of dengue fever in Surabaya is five years. Assumptions These are the assumption of the research, 1. There are no changes of the government policy in dengue fever epidemics during the research. 2. There are no circumstances changes in social factors, environmental factors and medical factors in Surabaya. 3. The medical data such as the number of recovery time and immune system in the human body are obtained from Puskesmas in Surabaya which has been collected by Surabaya Health Department. The Figure way 2. to Life eradicate Cycle transmission of Aedes Aegypti is breaking Mosquito the (Hoop chain of transmission as & a vaccine Foley, 2001) to prevent dengue in the early stages of research because drugs that were effective against the virus has not been found. (Andini, 2009). B. Dynamics Transmission Transmission dynamic models (often shortened to just dynamic models) are capable of reproducing the direct and indirect effects that may arise from a communicable disease control programmers. They differ from other (static) models used in decision sciences in that the risk of infection (sometimes referred to as the force of infection ) is a function of the number of infectious individuals (or infectious particles, such as eggs of intestinal worms) in the population (or environment) at a given point in time (Walinga,2009). This study adopts the vector host transmission model from (Adams and Boots, 2010) for modeling transmission dynamics of dengue fever in Surabaya. The diagram of the vector host dengue transmission model is shown in 2.4. It is assumed that the population is divided into host (human), vector (pre-adult), and vector (adult female mosquito population). II. LITERATURE STUDIES This chapter presents the relevant theories used in this research. It will discuss the theoretical frameworks which support this research. A. Dengue Fever Epidemics Dengue hemorrhagic fever (DHF) is an infectious disease caused by dengue virus. The disease is a public health problem in Indonesia because of the high prevalence and wide distribution. Dengue Hemorrhagic Fever (DHF), also called Hemoragic Hemorrhagic Fever (HHF), first reported in Indonesia in 1968. Until now, DBD is still one health problem in Indonesia because of the high prevalence and widespread distribution. Figure 3. Diagram of the vector-host transmission model (Adams and Boots, 2010) C. Knowledge Management
4 Knowledge Management (KM) comprises a range of strategies and practices used in an organization to identify, create, represent, distribute and enable adoption of insights and experiences. Insight and experience consists of knowledge, either embodied in individuals or embedded in organizational processes or practices. D. Cognitive Ergonomics Cognitive ergonomics or engineering is a branch of ergonomics appear putting special emphasis on the analysis of cognitive processes, for example, diagnosis, decision making and planning - which required operators in the modern industry. Ergonomics is a branch of cognitive ergonomics for humans discusses mental work is not only a passive receptor of the stimulus, the human mind is actively processing the information received and turn it into shape and given categories. Experience, imagery, problem solving, remembering and thinking are all terms that describe the stages of cognitive. Cognitive processes can be considered analogous to a computer, input information is processed in various ways (selected, compared, combined with other information that is already in memory, transformed, rearranged, etc.), then the response that comes out depends on the properties of the individual. In particular, cognitive ergonomics study of cognitive values of a user object product (Grace, 1992). E. Usability Web usability is an important factor in developing a web. Developers must understand the principles of usability before it implements on a web. According to Jacob Nielsen, usability is a quality attribute that assesses how easy user interface. Usability also refers to methods for improving ease of use during the design process. Usability is defined by five components, namely learn ability, efficiency, memorability, errors, satisfaction. A website with poor usability will be abandoned by users. III. RESEARCH METHODOLOGY This research conducted in eight stages which are introduction/preliminary stage, data collecting stage, implementation of Dynamics Transmission Vector Model, data processing stage and validation stage, mechanism system in operating spread and prediction system, data analyse and interpretation stage, evaluation and conclusion and suggestions stage. A. Introduction/Preliminary Stage This stage is the beginning of the research. This introduction/preliminary stage consists of problems identification stage, literature studies, field studies and determination of research objectives. B. Introduction/Preliminary Stage This stage is the beginning of the research. This introduction/preliminary stage consists of problems identification stage, literature studies, and determination of research objectives. C. Data Collecting Stage After doing all the preparation stages, the next step is data collecting stage. The processes will be done in this data collecting stage is the data of dengue fever cases on every sub district in Surabaya. The data of dengue fever cases from Surabaya Health Department and Dr. Soetomo Hospital and population data and growth rate on every sub district in Surabaya. D. Implementation Dynamics Transmission Vector Model. After collecting all the data needed, the next steps are implementation stage. The implementation of Dynamics Transmission Vector is predicting growth of Aedes agepty mosquito, infected and death cases of dengue fever. Ovipositon Rate y = -0,0163x 2 + 1,2897x -15,837 (1) Pre Adult Mosquito Maturation Rate y = -0,0000002x 5 + 0,00003x 4 0,0012x 3 + 0,0248x 2-0,2464x + 0,9089 (2) where : x = temperature dependent in observation area. Infeceted Population (Ie) Where : bv = oviposition rate of the egg (per days) v = propotion vertical infection rate Iv = infectious female mousquito Sv = the number at time t of suspceptible Ev = infected but not infectious ω = pre adult mosquito maturation rate (per days) Ie = infected population Death Population (Dd) Where : bv = oviposition rate of the egg (per days) v = propotion vertical infection rate Iv = infectious female mousquito Sv = the number at time t of suspceptible Ev = infected but not infectious ω = pre adult mosquito maturation rate (per days) Ie = infected population Rh = Human recovery rate E. Data Processing Dynamics transmission vector model implemented in mosquito simulation, infected and death population of dengue fever. The result of mosquito simulation, the oviposition rate and pre adult mosquito maturation rate is highest rate on October and lowest on August. It is depend on temperature in observed area. Infected and death population in dengue fever simulate based on the model formulation with the input from mosquito formulation. (3) (4)
F. Validation Stage Validation model of transmission dynamics vector is using mean average deviation. Mean Average Deviation (MAD) used to measure the error in simulation of infected population and death population in every sub districs that caused by dengue fever virus. Formulation that used in validation model is: e t = F t-p,t -D t (5) MAD (1/ n) n e i i 1 The recap of MAD for infected and death cases of dengue fever shows the deviation between simulation and real condition. Table 1. Recap of MAD for Infected and Death Cases of Dengue Fever MAD Infected Death MAD 2011 0,209001 0,637097 MAD 2012 0,270383 0,610215 MAD 2013 1,076757 2,43871 TOTAL 1,556141 3,686022 AVERAGE 0,518714 1,228674 The average of MAD is 0,5187 that quite a bit for the error in forecasting and predicting infected popoluation and with that result the model can represent the prediction of dengue fever infected people quite precision.the average of MAD is 1,228674 that quite big error in forecasting and predicting death cases and with that result the model can not represent the prediction of dengue fever death cases because the validation result is less precition. G. Mechanism in Spread and prediction System CLINIC (6) dengue fever for Health Department, Clinics, and society.there are two kinds of action in mechanism of spread and prediction system. It is coordinative action and socialize and prevention action. The action will perform after the entities of system know about the condition and severity level of dengue fever epidemics in the sub district from spread and prediction system of dengue fever website based. Severity level clasification is method that used to clasify the condition of sub distric in dengue fever ephidemics. Severity level classification is devided into three condition by colour red, yellow, and green. Table 2. Color Classification on Severity Level Colour Mean Dangerous Warning Safe Variable that consider in it is death population and infected population. The table bellow will show the category and value on severity level classification. Table 3. Severity Level Classification of Dengue Fever Ephidemic VARIABLE RED YELLOW GREEN Death Population 1 < 0 < 0 Infected Population 7 6-3 2-0 In the table above, death population is one factor that was used to determine the severity level of sub distric from dengue fever virus. When there is a death cases a minimum 7 person infected by dengue fever then the sub district declared as dangerous category. In warning category, there is 6-3 infected population in dengue fever. The last is safe category there are 2-0 infected population in dengue fever. Severity level classification as a reference to decided the best policy to minimize spread of dengue fever virus and apply it effectively and efficiently. The figure above will show the design of dengue fever spread and prediction system : 5 HEALTH DEPARTMENT SOCIETY RT/RW Coordinative Action Socialize and Prevention Action Figure.4 Mechanism of Spread and prediction System The mechanism of spread and prediction system is mechanism in coordinating, socializing, and preventing Figure 5. Spread and prediction System Dengue Fever
In dengue fever spread and prediction system allow users to identify the location that is needed to predict using Surabaya maps with 31 sub districts. The knowledge sharing for spread and prediction system that needed as anticipation in preventing dengue fever. This is one example of knowledge sharing in: Prevention and Eradication Action from Health Department with Dangerous Condition (Red) in Sub Distric. 1 Epidemiological investigations and fogging focus on any cases of dengue were reported. 2 Effective coordination with clinic and hospitals in regarding treatment of dengue fever victims 3 Counseling about treatment of dengue fever victims in critical areas 4 Distributes abate to kill mosquito larvae through the clinic to society This knowledge can help health department in deciding the effective and efficently policy to minimize spread of dengue fever. H. Analysis and Interpretation Stage The model is used to predict the growth of mosquitos, dengue fever victim by Dynamics Transmission Vector approach using mathematical model approach using differential. The models use to simulate three aspects include oviposition rate and pre adult mosquito maturation rate, infected population, and death cases caused by dengue fever. The implementation of dynamics transmission model for infected population prediction is using MAD (Mean Average Deviation) to validate the models. The result of MAD is small enough, 0.5187. So the model is good and appropriate in predicting the number of dengue victims. The low levels of accuracy between simulation and real outcomes in deaths cases caused this model needs modification with add some variables that can represent between the simulation results with the real value of the death cases in dengue fever. The website of spread and prediction system in dengue fever consider usability and cognitive ergonomics for users. I. Conclusion and Suggestions Stage After conducting all of the previous stage, a set of conclusion and suggestion will be generated. The conclusion will provide answer for the research question. The suggestion will provide advice in accordance to further development of research. Some suggestion for this research are model in this study is limited in temperature as input variable in spread of dengue fever. It is important to consider social factor, enviromental factor, and people s behaviour in model in order to capture the real condition of dengue fever epidemics. Requires advanced studies related to the effective website design and website content to appropiate it with cognitive principles. Lack of data and information about dengue fever cases in Surabaya, so it is needed more accurate and integralictic data. REFERENCES Adams B, Boots M. How important is vertical transmission in mosquitoes for the persistence of dengue? Insights from a mathematical model. Epidemics 2010;2:1-10. Anderson RM, May RM. Infectious Diseases of Humans: Dynamics and Control. New York: Oxford University Press; 1991. Artoni, Kurniawan Yuli. 2011. Pengertian Ergonomi Kognitif. Viewed at http://ergonomikognitif.blogspot.com/2011/12/pengerti an-ergonomi-kognitif.html. Last updated April, 14 th, 2013 at 6.34 p.m. Chen, Szu Chieh. Hsieh, Meng Huan. Modelling the Transmission Dynamics of Dengue Fever : Implication of Temperature Effects. 2012. Vol 431. P. 385-391 Depkes RI. 1998. Pencegahan, Pemberantasan, dan Pembasmian Penyakit Menular, Jakarta Depkes RI. 2009. Profil Kesehatan Indonesia 2009, Jakarta. Health Department, 2013, Data Dengue Fever Cases in Surabaya, Surabaya. Kayani, Jawad. Zia, M.Qamar. 2012. The Analysis of Knowledge, Knowledge Management and Knowledge Management Cycles: A Broad Review. Vol. 1, No. 6 Fitriyani. 2007. Penentuan Wilayah Rawan Demam Berdarah Dengue di Indonesia dan Analisis Pengaruh Pola Hujan terhadap Tingkat Serangan (Studi Kasus : Kabupaten Indramayu). Tugas Akhir Departemen Geofisika dan Meteorologi. Fakultas Matematika dan Ilmu Pengetahuan Alam.Institut Pertanian Bogor. Gubler, Duane J. Epidemic Dengue / Dengue Hemorrhagic Fever as a Public Health, Social and Economic Problem i the 21 st Century. 2002. Vol. 10. No. 2. Hidayat. 2010. Web Usability (Introduction). Viewed at http://bangkitkansemangat.wordpress.com/2010/06/26/ web-usability/. Last updated April, 14th, 2013 at 6.45 p.m. Hudaningsih, Nurul. 2011. Perancangan Sistem Peringatan Dini dan Penanganan Sebaran Demam Berdarah Dengue (DBD) dengan Pendekatan Sistem Dinamik dan Sistem berbagi Pengetahuan. Tugas Akhir Jurusan Teknik Industri, Institut Teknilogi Sepuluh Nopember, Surabaya. Judarwanto, Widodo. 2007. Profil Nyamuk Aedes dan Pembasminya. Viewed at http://indonesiaindonesia.com/f/13744-profil-nyamuk- Aedes-pembasmiannya/. Last updated April, 6th, 2013 at 9.17 p.m. Satwika, I. 2010. Perancangan Web Based- Knowledge Management untuk Mengontrol Penyebaran Penyakit Tropis dengan Memperhatikan Aspek Usability. Tugas Akhir Jurusan Teknik Industri, Institut Teknilogi Sepuluh Nopember, Surabaya. Uswatun Hasanah. 2007. Analisis Hubungan Cuaca dan Jumlah Penderita Demam Berdarah Dengue (DBD) dengan Fungsi Transfer. Tugas Akhir Departemen Statistika. Fakultas Matematika dan Ilmu Pengetahuan Alam. Institut Pertanian Bogor. 6
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