Final Presentation of 4th year research study

1. A Web-based Dengue Detection
and Prevention System
By
Ms. Nirthika R.
Level 4M
2012/SP/142
Department of Computer Science
University of Jaffna
Supervisors:
Dr. A. Ramanan & Dr. K. Gajapathy
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2. 1. Introduction
2. Related Work
3. Background
4. Methodology
5. Experimental Setup & Testing Results
6. Discussion & Conclusion
> References
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Outline

3. o Overview
• This study focuses on the speeded-up detection and prevention of dengue cases in the Jaffna district. In this
regard a web-based system is developed for fast notification and a prediction module using machine
learning approach is presented by considering the risk factors of dengue.
o Application
• The proposed system can be used at health Institutes (Jaffna Teaching Hospital, Private hospitals, Regional
Directorate of Health Services Jaffna, Medical Officer of Health, Epidemiological Unit).
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1. Introduction

4. o Objective
• To identify specific factors that influence the information flow in the notification process of dengue at
different levels, such as completeness, timeliness, sensitivity, etc.
• To speed-up the detection and prevention of dengue cases in the Jaffna district via a web-based
intelligent system
o Challenges
• Missing notification from private and indigenous medicine.
• Don’t care as a regional & national problem.
• Communication gap.
o Contribution
• Developed a web-based dengue detection and prevention system and create a model for predicting the
relationship between dengue outbreak and selected risk factors.
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5. 1. Notifiable disease surveillance in Sri Lanka and the United Kingdom: A comparative study (2013)
• Reports the pros & cons of the existing notifiable disease surveillance systems in the UK & Sri Lanka.
• Recommends
 Receiving notifications from indigenous medical practitioners, public health workers, laboratories &
the general public.
 Involvement of laboratories.
 Computerizing the existing surveillance system for notification would enhance the completeness
and timeliness of reporting.
2. An ICT-based Real-time Surveillance System for Controlling Dengue Epidemic In Sri Lanka (2014)
• Complementary web based open source software application – eDCS: electronic Dengue Control System
• Allows health care professionals and citizens to get early awareness about the dengue disease via
Internet or mobile phone and bring them for performing Dengue prevention and controlling operation
through the social media acceleration.
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2. Related Work

6. 3. Towards an Early Warning System to Combat Dengue (2013)
• Create a simulation model to predict the future dengue outbreaks, intensity of the disease,
and the population at risk of having the fever.
• A regression model for identifying the risk factors that causes an outbreak
• An Artificial Neural Network model for predictions
• GIS model for simulating the results.
• Climatic, socio-economic, and census factors have been considered for predictions.
4. Study of Potential Risk of Dengue Disease Outbreak in Sri Lanka Using GIS and Statistical Modelling
(2009)
• Develop a power regression model to quantitatively assess the relationship between rainfall & dengue outbreaks
• Evaluate the suitability of the model for predicting dengue outbreaks.
• The Inverse Distance Weighted (IDW) interpolator & Geographic Information System (GIS) techniques were used
in mapping the spatial distribution of dengue risk surfaces.
• An error analysis was conducted to assess the validity of the model comparing model outputs and actual
outbreaks.
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Workflow Diagram of Dengue Notification System
3. Background

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A Web-based Dengue Detection and Prevention System Architecture
4. Methodology

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5. Experimental setup & Testing results
o User requirement gathering
• Contact previous researcher (Ms. C. Kalpana, Mr. A. Rukshan)
 Gathered the details of exiting dengue reporting system.
 Identify the complexity and problem in that process.
• Met Regional Epidemiologist (Dr. Sivaganesh)
 Gathered the updated details of the notifications forms (H544, H399).
• Had a meeting with Jaffna Teaching Hospital doctors (Dr. T. Kumanan, Dr. N. Suganthan, Dr.
Arulmoli, Dr. (Mrs). M.G. Sathiadas, Dr. (Ms.) J.A.Pradeepan, ....)
 Gave more requirement to include in the system.
 Collected some dengue patients details through Prof. Noble Surendran.

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o Dataset (2015,2016)
For Jaffna district in Northern province
• Monthly dengue cases data -> Epidemiology
Unit, Ministry of health site
http://www.epid.gov.lk/web/
• Monthly temperature, humidity and rainfall
data ->
https://www.wunderground.com/history/wmo/
and Annual and monthly rainfall at observation
stations, 2010 – 2015

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Demonstrate
o System module

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Power model
Results:
General model Power1:
f(x) = a*x^b
Coefficients (with 95% confidence bounds):
a = 1.456e+23 (-3.038e+24, 3.329e+24)
b = -14.59 (-21.33, -7.849)
Goodness of fit:
SSE: 6.885e+04
R-square: 0.8068
Adjusted R-square: 0.7875
RMSE: 82.98
Temperature mean 25 26 28 30 30 30 30 30 29 28 26 26
Dengue cases 651 228 132 71 62 72 148 97 86 84 176 439
o Prediction module
• Plot Temperature mean Vs Dengue cases
25 25.5 26 26.5 27 27.5 28 28.5 29 29.5 30
100
200
300
400
500
600
tmean
dengue
dengue vs. tmean
powerTmean

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Power model
Results:
General model Power1:
f(x) = a*x^b
Coefficients (with 95% confidence bounds):
a = 0.001838 (-0.06682, 0.07049)
b = 2.735 (-6.079, 11.55)
Goodness of fit:
SSE: 3.389e+05
R-square: 0.04896
Adjusted R-square: -0.04614
RMSE: 184.1
Humidity mean 67 66 61 61 70 67 65 64 67 71 77 74
Dengue cases 651 228 132 71 62 72 148 97 86 84 176 439
• Plot Humidity mean Vs Dengue cases
62 64 66 68 70 72 74 76
100
200
300
400
500
600
hmean
dengue
dengue vs. hmean
powerHmean

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o Discussion
• Previous study
 Analyses existing system and recommends computerizing
 Identify the risk factors
 Power model to predict the relationship between rainfall and dengue outbreak
• My current study
 Analyzed the updated requirements.
 Developed a web-based fast notification system – Jaffna district
 Power regression model for predict relationship between temperature and dengue outbreak
• Further study
 More implementation of system for other region
 Analyze how to select the appropriate regression model for all the other factors.
6. Discussion & Conclusion

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o Conclusion
• Study and identify the risk in manual dengue surveillance processes.
• Developed a web-based fast notification system
• Present it to Jaffna regional epidemiologist and general hospital.
• Modify system from their suggestions.
• Identify the factors influence in the dengue outbreak
• Create a model for predicting the relationship between dengue outbreak and selected risk factors.

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o Kalpana, C., Mahesan, S. and Peter, A. Bath, “Notifiable disease surveillance in Sri Lanka and the
United Kingdom: a comparative study”, Sri Lanka Journal of Bio-Medical Informatics, 4(1):14-22, 2013,
doi: http://dx.doi.org/10.4038/sljbmi.v4i1.5190.
o Rukshan, A., Miroshan, A., “An ICT-based Real-time Surveillance System For Controlling Dengue
Epidemic In Sri Lanka ”, International Conference on Contemporary Management (ICCM): 14-15th Mar,
2014.
o Munasinghe, A., Premaratne, H.L. and Fernando, M.G.N.A.S., “Towards an Early Warning System to
Combat Dengue”, International Journal of Computer Science and Electronics Engineering (IJCSEE)
Volume 1, Issue 2 (2013) ISSN 2320–4028 (Online).
o Pathirana, S., Kawabata, M. and Goonetilake, R., “Study of potential risk of dengue disease outbreak in
Sri Lanka using GIS and statistical modelling”, Journal of Rural and Tropical Public Health, vol. 8, pp. 8-
17, 2009.
References

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Thank You…
Q&A