DESIGNING EARLY WARNING SYSTEM AND SPREAD HANDLING OF DENGUE FEVER - PowerPoint PPT Presentation

DESIGNING EARLY WARNING SYSTEM AND SPREAD HANDLING OF DENGUE FEVER USING TRANSMISSION DYNAMICS VECTOR APPROACH AND KNOWLEDGE SHARING RETNO WIDYANINGRUM 2509100010 SUPERVISOR : ARIEF RAHMAN, S.T,M.Sc. NIP : 197706212002121002 INDUSTRIAL ENGINEERING DEPARTMENT INSTITUT TEKNOLOGI SEPULUH NOPEMBER

Dengue Fever Cases Source : Health Department Surabaya, 2013 Dengue fever cases in 2010 was very high. It called Kejadian Luar Biasa that cause high fatality case rate in Indonesia especially on Surabaya .

Ineffective Policies Malathion dose to 10 liters per hectare, but in reality only use about 3-5 liters per hectare Larvicidal (abate) also has not been able to kill mosquito larvae effectively , because of Aedes agepthy female mosquitoes are able to spawn 100 pieces of egg per day

Road Map Research (Satwika,2010) (Hudanigsih,2011) Early Warning System Spread Early Warning System Tropical Map of Dengue Fever in Disease in Indonesia Surabaya 1. A month prediction of 1. Improve Knowledge about Dengue Fever Spread Map in Tropical Disease Epidemics Surabaya 2. Environmental Factor 2. Design mechanism of Influence in Tropical Disease Dengue Spread by Knowledge Sharing

Research Gap RESEARCH GAP Prediction only a month, inneficient way because difference of stella and wbsite coding Dynamics System Model without loop in model can’t accomodate Dengue epidemics

Research Question The way to design early warning 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 .

Research Objectives Determining variable in transmission dynamics vector in spreading of dengue fever epidemics. Developing and simulating variable in models with transmission dynamics vector in spreading of dengue fever epidemics. Designing an early warning mechanism system in the spread of dengue fever and determine the level of danger of the spread indicator in Surabaya. Designing an online early warning system based on sharing knowledge and website in anticipation of the spread of dengue fever. Designing the operating mechanism of early warning system online so it can be operated by health experts and public

The BENEFITS for HEALTH SPECIALIST 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 Health practitioners and the public can share knowledge and handle disease detection to minimize knowledge gap about dengue fever epidemics together. Assist the Government, Health Department in Surabaya, to make policies and control the spread of dengue fever epidemics effectively and efficiently.

The BENEFITS for COMUNITY Increase public knowledge about the development of the spread of dengue in their region. Increase public knowledge about the prevention and control of dengue fever epidemics. Improve health for the people in Surabaya

Scope of Research : LIMITATION (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) 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) 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.

Scope of Research : LIMITATION (2) The research area for designing early warning system in dengue fever epidemic is Surabaya . Horizon time in predicting the spread of dengue fever in Surabaya is three years .

Scope of Research : ASSUMPTIONS • There are no changes of the government policy in dengue fever epidemics during the research. 1. • There are no circumstances changes in social factors, environmental factors and medical factors in Surabaya. 2. • 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 3. Surabaya Health Department.

Literature Review Aedes Aegypti Susceptible Simulation Population Dengue Fever Dynamics Epidemics Transmission Vector Input : Output : 1. Data Climate in Surabaya (Temperature, Rainfall, Designing online Early and Wind Speed) Warning System Proces : 2. Variable data in Dynamic Mechanism of Dengue Developing and Simulating Transmission Vector Fever Epidemics in 3 year Prediction of 3. Dengue Fever Epidemic Surabaya Dengue Fever Spread Factor in Surabaya Knowledge Cognitive Website Human Computer Usability Interaction Management Ergonomics

RESEARCH METODOLOGY

Research Metodology

Research Metodology

Dengue Fever Data Sawahan is the one of sub districts with the highest number of dengue fever cases. The cases reach 80 per year .

FORMULATION IN AEDES AGEPTHY MOSQUITO • Ovipositon Rate y = -0,0163x 2 + 1,2897x -15,837 • • • Pre Adult Mosquito Maturation Rate y = -0,0000002x 5 + 0,00003x 4 – 0,0012x 3 + 0,0248x 2 – 0,2464x + 0,9089 • • • Adult Mosquito Death Rate • y = 205,03 -1,91x +0,15x1,5 – 725,9 / ln x + 1247,68 : x • Virus Incubation Rate in Mosquito • y = 0,008x – 0,1393

Simulation of Aedes agepty Mosquito 2012 Oviposition Rate Data on 2012 Pre Adult Mosquito Maturation Rate Data in 2012 50 45 5 40 35 4 Axis Title 30 Ovipotition Rate High Axis Title 25 3 Pre Adult Mosquito Average 20 Maturation Rate (High) Ovipotition Low High 2 15 Pre Adult Mosquito Average 10 1 Maturation Rate (Low) 5 0 0 Oct Jan Feb Mar Apr May Jun Jul Aug Sep Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Adult Mosquito Death Rate in 2012 Virus Incubation Rate in Mosquito 2012 0,7 0,6 0,14 0,5 0,12 Axis Title 0,4 0,1 Adult Mosquito Death Axis Title Rate (High) 0,08 Virus Incubation Rate 0,3 in Mosquito (High) Adult Mosquito Death 0,06 0,2 Rate (Low) Virus Incubation Rate 0,04 0,1 in Mosquito (Low) 0,02 0 0 Oct Jan Feb Mar Apr May Jun Jul Aug Sep Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Formulation in Infected Population  Variable bv, Iv, and ω come from mosquito simulation .  Proportion vertical infection rate constanta of Aedes agepty mosquito 0,028 (Adam & Boots, 2010)  Sv, Ev, and Ie are variables that needed data from Statistical Berau, Health Department, and Dr. Soetomo Hospital

Simulation of Infected Population in Tandes 2012 Variable Mean Value Result bv oviposition rate of the egg (per days) 33,1853225 v propotion vertical infection rate 0,028 4,809539 Iv/Sv+Ev+Iv Infected Probability 4,15776E-05 ω pre adult mosquito maturation rate (per days) 2,404788935 Ie Infected population 2

Formulation of Death Population  Rh or ˠ (Human recovery rate) is contanta for human rate in recovery theirself from dengue fever virus. The value is 0,1428. (Adam and Boots, 2010)

Simulation of Death Population in Tandes 2012

Simulation and Prediction Result

Validation Model Error measurement is performed to measure the error result from the simulation results and the real condition in infected and death population of dengue fever. The error measurement used is Mean Average Deviation

MAD Score of Infected and Death Simulation Death Population Infected Population MAD score of death simulation is higher than infected population. The model of death population less precision, because it is more than 1

MODEL AND SIMULATION ANLYSIS • 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. • Sanitation Environmental Aspect • Hygne in living area • Knowledge in Social Aspect Dengue Fever

Mechanism System of Dengue Fever

Severity Level Classification

Early Warning System Dengue Fever

Comparation in Existing and Improvement Webite (1/5) The improvent condition minimize human error in inputing data for dengue fever prediction Input data Manually Choose using Scrool Bar

Comparation in Existing and Improvement Webite (2/5) Google Map Version Map Ilustration Version Users can choose directly through the map they want to know predictions. The selected map will show pop up on the map, so it easy to identify which areas selected and easy to understand the severity level of area.

Comparation in Existing and Improvement Webite (3/5) Information of Infected and Death Population in Dengue Fever Informatif data in predicting dengue fever epidemics

Comparation in Existing and Improvement Webite (4/5) Anticipation information usefull for user in preventing dengue fever epidemics . Clinics information help users to give report about dengue fever epidemics .

Comparation in Existing and Improvement Webite (5/5) Add knowledge feature is very important and main point of this research. Sharing knowledge used to prevent and minimize dengue fever epidemics on every sub district in Surabaya.