All these findings suggest that the entrance of imported cases, in conjunction with suitable
meteorological conditions, may have the potential to precipitate severe epidemics involving more
DHF cases. Careful monitoring and clinical management of imported dengue cases, along with
relevant meteorological information, are able to provide earlier warning signals for emerging
indigenous dengue epidemics than current surveillance systems [4,52]. For those high risk seasons,
the recommendations include: (1) to have both rigorous fever screening at airports and public health
efforts for case management once it is a laboratory-confirmed dengue cases that is more efficient to
catch the cases and then minimize further possible local transmission chains after the entrance of
imported cases; (2) to use the DRI serving as a forecasting system, to alert the public and the
government for disease prevention and control. These early alerts allow for the proper
implementation of targeted public health interventions and valuable buffer time for preventing
subsequent large-scale epidemics of dengue/DHF locally and in affected countries.
44
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Table 1: Abbreviations of variables
Abbreviation Explanation Unit
Independent variables
imported number of imported dengue cases in the area cases
rain daily accumulative rainfall mm
rainhr daily accumulative rainy hours hours
rh daily mean relative humidity %
sunhr daily sunshine accumulation hour hours
sunrate daily mean sunshine rate (from sunrise to sunset) %
tmax daily maximum temperature oC
tmean daily mean temperature oC
tmin daily minimum temperature oC
area_TN, area_KH dummy variables: for cases from Tainan (TN) area, area_TN=1 and area_KH=0; for cases from Kaohsiung (KH) area, area_TN=0 and area_KH=1; cases from Pingtung (PT) area, area_TN=0 and area_KH=0)
none
popd area-specific population density population/km2
sin24 Oscillation function sin (2πt/T), T (period) = 12 months none
cos24 Oscillation function cos (2πt/T), T (period) = 12 months none
Dependent variables Occurrence
(Figure 2)
Occurrence= 1 when any new indigenous confirmed dengue cases were present in the studied bi-week
none
52 intervals, else Occurrence=0.
Increase (Figure 3)
Increase=1 when relative risk= [(number of indigenous dengue cases in the studied bi-week interval+0.5)/
(number of indigenous cases in the prior interval+0.5)]
was larger than 1.2#, else Increase=0.
none
Case (Figure 4)
the number of indigenous dengue per bi-week in an area cases/bi-week
#: The threshold of 1.2 was chosen for optimizing the apportionment ratio, in order to increase
statistical efficiency. Use of alternative threshold, such as 1.5 or 2.0, decreased statistical efficiency
for the analysis. Because that the number of indigenous cases per area during a bi-week in low
transmission season is mostly zero, we calculate the ratio by adding 0.5 to both the denominator and
numerator.
Table 2: The definitions of risk category for dengue epidemics
1 1-9 Sporadic cases Start investigation on reported
cases
Insecticide use and severe case management
4 1000- Super-spreading
of epidemic
Severe case management
54
Table 3: The monthly frequency of dengue risk category (DRI) in Taiwan and Thailand
A. Taiwan: NKP areas during 1998-2008, biweekly DRI
month
2. TN: Tainan area, including Tainan City and County; KH: Kaohsiung area, including
Kaohsiung City and County; PT: Pingtung area, including Pingtung City and County.
56
B. Thailand: coastal provinces during 1996-2005, monthly DRI
month
1. Five coastal provinces are (a) Phetchaburi, (b) Prachuap Khirikhan, (c) Chumphon, (d) Surat
Thani and (e) Nakhon Sitammarat.
2. No record of DRI=4 in provinces a-d, and no record of DRI=0 in province e
58
Table 4: Area-specific polytomous logistic regression models based on local meteorology for
predicting dengue risk index (DRI) in Taiwan from 1998 to 2007
area
variables recruited
β SE p OR
TN
rh3 -0.0920 0.0457 0.0442 0.912(0.834-0.998) tmin4 0.2885 0.0754 0.0001 1.334(1.151-1.547) DRI1 2.6614 0.3083 <0.0001 14.317(7.824-26.197)
popd 0.0105 0.0308 0.7331 1.011(0.951-1.073)
KH
rh8 -0.0838 0.0384 0.0293 0.920(0.853-0.992) tmean3 0.2967 0.0602 <0.0001 1.345(1.196-1.514)
DRI1 3.3144 0.3113 <0.0001 27.506(14.943-50.631) popd -0.0064 0.0229 0.7790 0.994(0.950-1.039)
PT
tmax6 0.8228 0.1808 <0.0001 2.277(1.598-3.245) tmax7 -0.4172 0.1462 0.0043 0.659(0.495-0.878)
DRI1 3.2207 0.4518 <0.0001 25.045(10.331-60.717) popd 0.0123 0.0991 0.9016 1.012(0.834-1.229)
Note:
1. rh: relative humidity; tmax/mean/min: daily maximum/mean/minimum temperature
2. DRI1: value of DRI in the previous time interval; popd: population density
3. TN: Tainan area, including Tainan City and County; KH: Kaohsiung area, including Kaohsiung
City and County; PT: Pingtung area, including Pingtung City and County.
Table 5: The consistence of DRI between observed and expected values for dengue epidemics
in Taiwan from 1998 to 2008
A. Distribution of frequencies
Observed Predicted TN KH PT
0 1 2 3 0 1 2 3 0 1 2 3
1998-2007 0 *171 6 1 0 *111 9 0 0 *178 7 0 0
1 16 *9 4 0 19 *30 9 0 13 *10 1 0
2 0 7 *14 1 1 10 *25 2 0 2 *12 0
3 0 0 2 *5 0 0 2 *14 0 0 0 0
2008 0 *11 4 0 0 *10 0 0 0 *23 0 0 0
1 6 *2 1 0 2 *0 2 0 1 0 0 0
2 0 0 0 0 0 2 *8 0 0 0 0 0
3 0 0 0 0 0 0 0 0 0 0 0 0
Note:
1. *: concordant pairs
2. TN: Tainan area, including Tainan City and County; KH: Kaohsiung area, including Kaohsiung
City and County; PT: Pingtung area, including Pingtung City and County.
60
B. Diagnoses of the predictability of risk category for dengue epidemics in Taiwan
Area
1998-2007 (Fit) 2008 (Out-of-fit)
Model DRI1 only Model DRI1 only
TN
Gamma 0.9620+0.0144 0.9455+0.0177 0.2131+0.4213 0.1667+0.3977 Spearman 0.8570+0.0291 0.7539+0.0479 0.1018+0.2104 0.0861+0.2100
N 236 239 24 24
KH
Gamma 0.9552+0.0133 0.9398+0.0163 0.9444+0.0499 0.9333+0.0505 Spearman 0.8380+0.0278 0.8131+0.0229 0.8680+0.0563 0.8500+0.0641
N 232 239 24 24
PT
Gamma 0.9623+0.0183 0.9183+0.0299 NA(C=23, D=1) -1.00+0.00 (C=22, D=2) Spearman 0.7061+0.0654 0.6316+0.0659 NA -0.0435+0.0307
N 223 239 24 24
Note:
1. DRI1: value of DRI in the previous time interval
2. C: concordant pairs, D: discordant pairs
3. TN: Tainan area, including Tainan City and County; KH: Kaohsiung area, including Kaohsiung
City and County; PT: Pingtung area, including Pingtung City and County.
Table 6: The logistic regression model based on meteorology for predicting risk category of
dengue epidemics, Thailand, 1996-2004 area variables
recruited β SE p OR
a
rh5 -0.2015 0.1051 0.0552 0.818(0.665-1.004) tmin2 -0.6259 0.1964 0.0014 0.535(0.364-0.786) tmin7 -0.3821 0.1549 0.0136 0.682(0.504-0.924) tmean1 0.7759 0.2340 0.0009 2.173(1.373-3.437)
DRI1 4.9065 1.0692 <0.0001 135.170(16.625->999.99) popd 1.1618 0.8280 0.1605 3.196(0.631-16.194)
b
tmean1 0.5577 0.1460 0.0001 1.747(1.312-2.325) DRI1 3.0966 0.5348 <0.0001 22.122(7.755-63.101)
popd 0.4555 0.2383 0.0560 1.577(0.988-2.516)
c
rh3 -0.3132 0.0998 0.0017 0.731(0.601-0.889) rh4 0.2803 0.1071 0.0089 1.324(1.073-1.633) tmax2 0.5459 0.1681 0.0012 1.726(1.242-2.400)
DRI1 3.0583 0.6373 <0.0001 21.291(6.106-74.240) popd 0.3274 0.1642 0.0462 1.387(1.006-1.914)
d
tmin1 0.5476 0.2534 0.0307 1.729(1.052-2.841) tmean1 0.3838 0.2005 0.0556 1.468(0.991-2.175)
DRI1 3.4051 0.6008 <0.0001 30.117(9.277-97.772) popd 0.1795 0.1397 0.1988 1.197(0.910-1.574)
e
tmin8 0.5421 0.2348 0.0210 1.720(1.085-2.724) tmax2 0.4179 0.1132 0.0002 1.519(1.217-1.896) DRI1 2.9168 0.4440 <0.0001 18.482(7.741-44.125)
popd 0.2022 0.3153 0.5213 1.224(0.660-2.271)
Note: Five coastal provinces are (a) Phetchaburi, (b) Prachuap Khirikhan, (c) Chumphon, (d) Surat
Thani and (e) Nakhon Sitammarat.
62
Table 7: The consistency comparison of observed and predicted category values from
Thailand models
A. Distribution of frequency
Obs. Predicted
2. Five coastal provinces are (a) Phetchaburi, (b) Prachuap Khirikhan, (c) Chumphon, (d) Surat
Thani and (e) Nakhon Sitammarat.
B. Diagnoses of the predictability of risk category for dengue epidemics in Thailand
Province
1996-2004 (Fit) 2005 (Out-of-fit)
Model DRI1 only Model DRI1 only
a
Gamma 0.9845+0.0105 0.9534+0.0251 0.6667+0.3043 0.9231+0.1127 Spearman 0.7718+0.0735 0.6915+0.0776 0.2883+0.1725 0.6538+0.1952
N 101 107 12 12
b
Gamma 0.9683+0.0179 0.9405+0.0268 1.00+0.00 1.00+0.00
Spearman 0.7237+0.0670 0.6767+0.0683 NA(C=12) NA(C=12)
N 107 107 12 12
c
Gamma 0.9565+0.0276 0.8987+0.0426 1.00+0.00 1.00+0.00 Spearman 0.6617+0.0785 0.6363+0.0736 0.5477+0.2588 0.6742+0.2611
N 104 107 12 12
d
Gamma 0.9506+0.0260 0.9426+0.0271 0.9091+0.1366 0.5000+0.4841 Spearman 0.6827+0.0709 0.6741+0.0688 0.6250+0.2398 0.2500+0.2915
N 107 107 12 12
e
Gamma 0.9448+0.0239 0.9204+0.0282 1.00+0.00 0.00+0.2357 Spearman 0.7499+0.0510 0.7336+0.0484 0.4771+0.1780 -0.00+0.0601
N 100 107 12 12
Note:
1. DRI1: value of DRI in the previous time interval
64 2. C: concordant pairs
3. Five coastal provinces are (a) Phetchaburi, (b) Prachuap Khirikhan, (c) Chumphon, (d)
Surat Thani and (e) Nakhon Sitammarat.
Figure 1: The rationale of local climate and imported effects on indigenous dengue epidemics
The area within red line demonstrates the local correlation among climate, mosquito
density and epidemics of dengue. Imported dengue cases also involve local mosquito vector and
human population in initiating indigenous epidemics.
66
Figure 2: Numbers of laboratory-confirmed dengue (including dengue and dengue
hemorrhagic fever) cases in Taiwan, 1998-2007.
A. Spatial distributions of cumulative indigenous dengue cases.
B. Biweekly number of imported dengue cases.
C. Biweekly number of indigenous dengue cases in the studied areas. TN: Tainan area,
including Tainan City and County; KH: Kaohsiung area, including Kaohsiung City and
County; PT: Pingtung area, including Pingtung City and County. The Roman numbers denote
predominant serotypes of dengue virus isolated during major epidemics in that area [31].
D. Biweekly number of imported dengue cases in the studied areas.
68
Figure 3: Correlation between bi-weekly “occurrence” of indigenous dengue cases and 1) the
number of imported cases as well as 2)meteorological variables across time lags from 1 to 12
bi-weeks.
Note:
1. Each vertical line segment corresponds to a 95% confidence interval of the regression coefficient
and the red solid squares indicate the value of each coefficient estimate. (*: p<0.05)
2. The X-axis displays different time lags: 1 for two weeks lag, 2 for four weeks lag, and so on.
3. Abbreviations: see Table.
Figure 4: Correlation between bi-weekly “increase” of indigenous dengue cases and 1) the
number of imported cases as well as 2)meteorological variables across time lags from 1 to 12
bi-weeks.
Note:
1. Each vertical line segment corresponds to a 95% confidence interval of the regression
coefficient and the red solid squares indicate the value of each coefficient estimate. (*: p<0.05)
2. The X-axis displays different time lags: 1 for two weeks lag, 2 for four weeks lag, and so on.
3. Abbreviations: see Table.
70
Figure 5: Correlation between bi-weekly number of indigenous dengue cases and 1) the
number of imported cases as well as 2)meteorological variables across time lags from 1 to 12
bi-weeks.
Note:
1. Each vertical line segment corresponds to a 95% confidence interval of the regression
coefficient and the red solid squares indicate the value of each coefficient estimate. (*: p<0.05)
2. The X-axis displays different time lags: 1 for two weeks lag, 2 for four weeks lag, and so on.
3. Abbreviations: see Table.
Figure 6: Correlation between bi-weekly number of indigenous dengue cases and the number
of imported cases over time lags from 1 to 12 bi-weeks.
A. Period of “low intensity transmission”:
Those bi-week intervals were from March to
May. #: 95% Confidence interval=[-419930,
419876.8]
B. Period of “early phase of outbreaks”:
Those bi-week intervals presenting <10
indigenous dengue cases for months
excluding March to May.
C. Period of “late phase of outbreaks”: Those
bi-week intervals presenting 10≧
indigenous dengue cases.
Note:
1. Each vertical line segment corresponds to a 95% confidence interval of the regression
coefficient and the red solid squares indicate the value of each coefficient estimate. (*: p<0.05)
2. The X-axis displays different time lags: 1 for two weeks lag, 2 for four weeks lag, and so on.
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Figure 7: Fitting the number of dengue cases with meteorological regression models
A. Principal component analysis (PCA)
B. Square/cubic root of case number
Figure 8: Distribution of monthly Dengue Risk Index in Taiwan and Thailand, 1998-2005
Note:
74 1. Taiwan:
TN: Tainan area, including Tainan City and County; KH: Kaohsiung area, including Kaohsiung
City and County; PT: Pingtung area, including Pingtung City and County.
2. Thailand:
five coastal provinces are (a) Phetchaburi, (b) Prachuap Khirikhan, (c) Chumphon, (d) Surat
Thani and (e) Nakhon Sitammarat.
Figure 9: Fitting (1998-2007) and predicting (2008, after red dash line) DRI in Taiwan with
meteorological regression model
76 Note:
TN: Tainan area, including Tainan City and County; KH: Kaohsiung area, including Kaohsiung
City and County; PT: Pingtung area, including Pingtung City and County.
Figure 10: Fitting (1996-2004) and predicting (2005, after red dash line) DRI in Thailand with
meteorological regression model
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Note:
Five coastal provinces are (a) Phetchaburi, (b) Prachuap Khirikhan, (c) Chumphon, (d) Surat Thani
and (e) Nakhon Sitammarat.
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Appendix: Diagnostic tests for consistence between expected and observed DRI
(adapted from User’s manual of SAS)
1. Gamma=(P-Q)/(P+Q) P=ΣΣnijAij (twice the number of concordance)
Q=ΣΣnijDij (twice the number of discordance)
Aij=ΣΣnkl(k>i, l>j)+ΣΣnkl(k<i, l<j)
Dij=ΣΣnkl(k>i, l<j)+ΣΣnkl(k<i, l>j)
2. The Spearman correlation coefficient (ρs) is computed by using rank scores. This measure is
appropriate only when both variables lie on an ordinal scale. The range of the Spearman
correlation is -1≤ρs≤1.
Autobiography
Ms. Chuin-Shee Shang has been interested in ecology of infectious diseases since her first
medical entomology class in sophomore. Shang received her Master degree of science with the
thesis titled “Establishment of a Dengue Virus Surveillance System in Mosquitoes by
Reverse-Transcriptase Polymerase Chain Reaction (RT-PCR)”.
In recent years, Chuin-Shee has involved in several study projects, including (1) “The
surveillance and control of dengue and mosquito vectors in Taiwan” (DOH-96-DC-1101,
2007-present), (2) “The impact analysis of climate change on public health and infectious diseases
in Taiwan” (NSC94-2621-Z-002-010, 2005-present), (3) “The evaluation of health effect of
vegetarian food on high blood cholesterol patients” (in Taiwan Adventist Hospital, 1999-2000), and
(4) “Modification of RT-PCR system on surveillance of dengue virus in Taiwan”(including human
and mosquito systems, 1998-1999).
All the above research findings have been presented in several international conferences in
Hong Kong, Tahiti, Singapore, USA (annual meeting of the American Society of Tropical Medicine
and Hygiene) and Beijing.
Chuin-Shee has teaching experiences at National Taiwan University involving liberal
education classes on the course of “Scientific Attitude and Social Responsibility of Infectious
Diseases”. She guiding younger students and has worked voluntarily as a counselor in church’s
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teenager group for seven years and a coordinator in the re-establishment conducted by local
churches after the 921 earthquake in central Taiwan in year 1999.