dbdhalmarhalide
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EARLY WARNING SYSTEM FOR DENGUEHEMORRHAGIC FEVER (DHF) EPIDEMICS IN
MAKASSAR*
Halmar Halide 1 , Rais 1 and PeterRidd 2
1 Physics Dept. FMIPA Unhas, Makassar 90245, Indonesia2School of Engineering and Physical Sciences,
James Cook University, Townsville, 4811 Australia*Oral presentation at the 54 Th Anniversary of UniversitasHasanuddin,16-17 th August 2010, Makassar - Indonesia.
The paper is then published in Jurnal Matematika dan SainsITB vol 16 (1): pp. 26-34 year 2011 (it is free on theinternet)
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Introduction:What are the problems?
DHF is escalating and spreading
DHF is a costly disease, i.e. US$ 139 per case (4) almost twice the minimum monthly salary of US$79 (5)
DHF has no vaccine available (6)
DHF mosquito-control fails to prevent outbreaks
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Figure 1. DH/DHF cases around theworld from 1955-1999 (1))
Figure 2. DHF cases and places affectedin Indonesia from 1968-2003 (2)
DHF is escalating and spreading
Figure 3. Projected Dengue Fever spreads under warmer temperature.
Estimated population at risk in year 1990(A) and 2085 (B) (3).
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Figure 4. Fogging activity (depicted in arrows) has no-clear effecton preventing DHF outbreaks (2)
DHF mosquito-control fails to prevent outbreaks
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How should we response? be prepared for any outbreak by inventing atimely and accurate early warning system
Systems available at present require:
extensive data (7) and high computingpower (3).
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Aims of this study:To develop an early warning system for accurately predicting DHF outbreaks atsufficient lead times.
Here an outbreak is defined when thenumber of cases above 50% quartile (8)
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Data and Model
Model formulation : Multiple linear regression.
Predictant: present DHF case;Predictors: past meteo-climate dataand DHF cases.Predictors are selected using astepwise method (at significantlevel of 0.95).
Model cross-validation : Leaveone-out
Model skill : Peirce scoremeasures (9),(10)
SPSS, MATLAB and Visual Basic
packages for computation .Figure 5. Meteorological, climatological
DHF cases in Makassar (11) .
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1999 2000 2001 2002 2003 2004 2005 2006-50
0
50
100
150
200
250
300
350
Year
D H F c a s e s
data
P1P6
P12
Figure 6. Out-of-sample predictions(11)
Prediction Skill: time series
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Figure 7. Prediction skill using peirce skill score for both
the regression (o) and no-skill models (x)(11)
Prediction Skill: skill measures
0 2 4 6 8 10 12
-0.5
0
0.5
1
Leadtime(months)
P e i r c e
S c o r e
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Early warning system
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Conclusion:
An early warning system has been developedusing a simple model. The model is able topredict DHF outbreaks up to six month inadvance using past data. The system is a useful
tool for managing DHF epidemics(13)
.
Acknowledgement:We thank Mr. M. A. Abdi, Mr. B. Hargiyono, Dr. H. Ishak,and the CPC-NCEP-NOAA and JAMSTEC for data used.
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References:(1) D. Gubler, 2002 Trends Microbiol 10: 100-103.(2) Ndoen et al ., 2004http://www.pitt.edu/~super1/
lecture/lec17391/index.htm(3) Hales et al ., 2002. Lancet 360: 830-834.(4) Shepard et al., 2004 Vaccine 42: 1275-1280(5) D. Widarti, 2006http://www.ilo.org/public/english/
region/asro/jakarta/download/wagediah.pdf (6) Chadee et al ., 2005 Trop Med Int Health 10:748-754.
(7) Kuhn et al ., 2005. WHO report.(8). Nisalak et al ., 2003 Am J. Trop Med Hyg 68:191-202.(9) D.S. Wilks, 1995. Statistical methods in the atmospheric
sciences . Academic Press, San Diego.(10) D. B. Stephenson, 2000. Weather Forecast 15: 221-232.(11) Halide, H. and P. Ridd, 2008.. Int. J.Env. Health Res.
18 (4): 253-265.(12) Rais, 2010. Sistem Peringatan Dini DBD (kasus kota
Makassar), Thesis S1, Jur. Fisika FMIPA Unhas.(13) Halide, H., 2010. In: Handbook of Disease outbreaks:
Prevention, Detection and Control (Editors: A. Holmgrenand G. Borg), Nova Publisher, New York.
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