Fig 1.
The location of Kaohsiung city in Taiwan.
The inset shows the 38 districts, including 11 districts from the old Kaohsiung administrative districts. All districts were further classified into high, middle (mid) and low risk areas based on the household density and the average number of households with the presence of A. aegypti from the historical entomological data.
Fig 2.
Secular trend of the meteorological data and the dengue cases from 2005 to 2012.
(A) Comparison between Kaohsiung city and whole Taiwan of all laboratory-confirmed indigenous dengue cases from 2005 to 2012 based on the residential area. (B) Comparison among high, middle and low risk areas of all laboratory-confirmed indigenous dengue cases from 2005 to 2012. All dengue virus serotypes detected during each epidemic was indicated accordingly, with the dominant serotype labeled with asterisk based on the major serotype detected from more than 80% of dengue cases in the specific year. (C) The quarterly total numbers of the laboratory-confirmed imported and indigenous dengue cases in Kaohsiung city from 2005 to 2012. (D) The weekly average of temperature (temp, oC), rainfall (rain, mmHg) and relative humidity (rh, %) from 2005 to 2012.
Fig 3.
The temporal relationship between the indigenous dengue cases and the vector indices from the entomological surveillance data from 2005 to 2012 including Breteau index (A), Container index (B), House index (C) and adult A. aegypti index (D).
Table 1.
Univariate analysis of risk factors for dengue incidence by Poisson regression model.
Fig 4.
The weekly number of dengue cases from 2005 to 2012 based on the observation (solid line) and prediction (dahs line) from each vector index model including Model-BI: Breteau index model (A), Model-AI: adult A. aegypti index model (B), Model-CI: Container index model (C) and Model-HI: House index model (D).
The values of coefficient of determination (R-square) from each vector index model were also indicated.
Table 2.
Multivariate analysis of risk factors for dengue incidence.
Table 3.
Prediction accuracy of different mosquito indices by univariate and multivariate logistic regressions.