Re-assess Vector Indices Threshold as an Early Warning Tool for Predicting Dengue Epidemic in a Dengue Non-endemic Country

Abstract

Background: Despite dengue dynamics being driven by complex interactions between human hosts, mosquito vectors and viruses that are influenced by climate factors, an operational model that will enable health authorities to anticipate the outbreak risk in a dengue non-endemic area has not been developed. The objectives of this study were to evaluate the temporal relationship between meteorological variables, entomological surveillance indices and confirmed dengue cases; and to establish the threshold for entomological surveillance indices including three mosquito larval indices [Breteau (BI), Container (CI) and House indices (HI)] and one adult index (AI) as an early warning tool for dengue epidemic.

Methodology/principal findings: Epidemiological, entomological and meteorological data were analyzed from 2005 to 2012 in Kaohsiung City, Taiwan. The successive waves of dengue outbreaks with different magnitudes were recorded in Kaohsiung City, and involved a dominant serotype during each epidemic. The annual indigenous dengue cases usually started from May to June and reached a peak in October to November. Vector data from 2005-2012 showed that the peak of the adult mosquito population was followed by a peak in the corresponding dengue activity with a lag period of 1-2 months. Therefore, we focused the analysis on the data from May to December and the high risk district, where the inspection of the immature and mature mosquitoes was carried out on a weekly basis and about 97.9% dengue cases occurred. The two-stage model was utilized here to estimate the risk and time-lag effect of annual dengue outbreaks in Taiwan. First, Poisson regression was used to select the optimal subset of variables and time-lags for predicting the number of dengue cases, and the final results of the multivariate analysis were selected based on the smallest AIC value. Next, each vector index models with selected variables were subjected to multiple logistic regression models to examine the accuracy of predicting the occurrence of dengue cases. The results suggested that Model-AI, BI, CI and HI predicted the occurrence of dengue cases with 83.8, 87.8, 88.3 and 88.4% accuracy, respectively. The predicting threshold based on individual Model-AI, BI, CI and HI was 0.97, 1.16, 1.79 and 0.997, respectively.

Conclusion/significance: There was little evidence of quantifiable association among vector indices, meteorological factors and dengue transmission that could reliably be used for outbreak prediction. Our study here provided the proof-of-concept of how to search for the optimal model and determine the threshold for dengue epidemics. Since those factors used for prediction varied, depending on the ecology and herd immunity level under different geological areas, different thresholds may be developed for different countries using a similar structure of the two-stage model.

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).

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.