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

Dengue fever cases in 2010 was very high. It called Kejadian Luar Biasa that cause high fatality case rate in Indonesia especially on Surabaya.

Source : Health Department Surabaya, 2013

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)

Early Warning System Tropical Disease in Indonesia 1. Improve Knowledge about Tropical Disease Epidemics 2. Environmental Factor Influence in Tropical Disease

(Hudanigsih,2011)

Early Warning System Spread Map of Dengue Fever in Surabaya 1. A month prediction of Dengue Fever Spread Map in Surabaya 2. Design mechanism of Dengue Spread by Knowledge Sharing

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 GAP

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

• nline so it can be operated by health experts and public

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

The BENEFITS for HEALTH SPECIALIST

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

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.

Literature Review

Input :

1. Data Climate in Surabaya (Temperature, Rainfall, and Wind Speed) 2. Variable data in Dynamic Transmission Vector 3. Dengue Fever Epidemic Factor

Proces :

Developing and Simulating 3 year Prediction of Dengue Fever Spread in Surabaya

Output :

Designing online Early Warning System Mechanism of Dengue Fever Epidemics in Surabaya

Knowledge Management Dengue Fever Epidemics Aedes Aegypti Simulation Dynamics Transmission Vector Cognitive Ergonomics Website Usability Susceptible Population Human Computer Interaction

RESEARCH METODOLOGY

Research Metodology

Research Metodology

Dengue Fever Data

Sawahan is the one of sub districts with the highest number

• f dengue fever cases. The cases reach 80 per year.

FORMULATION IN AEDES AGEPTHY MOSQUITO

• Ovipositon Rate
• y = -0,0163x2 + 1,2897x -15,837
• Pre Adult Mosquito Maturation Rate
• y = -0,0000002x5 + 0,00003x4 – 0,0012x3 + 0,0248x2 – 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

5 10 15 20 25 30 35 40 45 50 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Axis Title

Oviposition Rate Data on 2012

Ovipotition Rate High Average Ovipotition Low High Average 1 2 3 4 5 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Axis Title

Pre Adult Mosquito Maturation Rate Data in 2012

Pre Adult Mosquito Maturation Rate (High) Pre Adult Mosquito Maturation Rate (Low) 0,1 0,2 0,3 0,4 0,5 0,6 0,7 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Axis Title

Adult Mosquito Death Rate in 2012

Adult Mosquito Death Rate (High) Adult Mosquito Death Rate (Low) 0,02 0,04 0,06 0,08 0,1 0,12 0,14 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Axis Title

Virus Incubation Rate in Mosquito 2012

Virus Incubation Rate in Mosquito (High) Virus Incubation Rate in Mosquito (Low)

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

• viposition rate of the egg (per days)

33,1853225 4,809539 v propotion vertical infection rate 0,028 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

MAD score of death simulation is higher than infected

• population. The model of death population less precision,

because it is more than 1 Infected Population Death Population

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
• Hygne in living area

Environmental Aspect

• Knowledge in

Dengue Fever

Social Aspect

Mechanism System of Dengue Fever

Severity Level Classification

Early Warning System Dengue Fever

Comparation in Existing and Improvement Webite (1/5)

Input data Manually Choose using Scrool Bar

The improvent condition

minimize human error in inputing data for

dengue fever prediction

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.

Conclusion (1/4)

1. The variable used in mosquito simulation is temperature. The variables used in infected population simulation are oviposition rate of the egg (per days), proportion vertical infection rate, infected probability, pre adult mosquito maturation rate (per days), and infected population. The variable used in death cases are oviposition rate of the egg (per days), proportion vertical infection rate, infected probability, pre adult mosquito maturation rate (per days), infected population, and recovery rate. 2.

• 2. In mosquito simulation and infected population the deviation

between simulation and real condition is small. The MAD (Mean Average Deviation) for infected population is 0,519. In death cases simulation the result is less precition with real condition and the MAD score is 1,229. So, in death cases model need improve by adding some variables that influence to dengue fever death cases.

Conclusion (2/4)

3. Severity level of dengue fever categorize in three condition, namely dengerous, warning, and safe condition. Dangerous condition occurs when one person died and seven people infected of dengue fever virus. Warning condition occurs when six until three people infected of dengue fever virus. Safe condition occurs when maximum two people infected of dengue fever epidemics.

• 4. Sharing knowledge help entities in system understand the step to

prevent dengue fever epidemics. Sharing knowledge aimed for Health Department, Clinics, and Society. It can help the entities understand the effective and efficient way in preventing dengue fever epidemics.

• 5. The mechanism of dengue fever mechanism is divided into two kinds of

action in mechanism of early warning system. It is coordinative action and socialize and prevention action.

Suggestion

The model in this study is limited in temperature as input variable in spread of dengue fever. So, it is important to consider social factor, environmental factor, and people’s behavior 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 appropriate it with cognitive principles.

Lack of data and information about dengue fever cases in Surabaya, so it is needed more accurate and intergalistic

data.

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