Dataset Information

Predicting local dengue transmission in Guangzhou, China, through the influence of imported cases, mosquito density and climate variability.

ABSTRACT:

Introduction

Each year there are approximately 390 million dengue infections worldwide. Weather variables have a significant impact on the transmission of Dengue Fever (DF), a mosquito borne viral disease. DF in mainland China is characterized as an imported disease. Hence it is necessary to explore the roles of imported cases, mosquito density and climate variability in dengue transmission in China. The study was to identify the relationship between dengue occurrence and possible risk factors and to develop a predicting model for dengue's control and prevention purpose.

Methodology and principal findings

Three traditional suburbs and one district with an international airport in Guangzhou city were selected as the study areas. Autocorrelation and cross-correlation analysis were used to perform univariate analysis to identify possible risk factors, with relevant lagged effects, associated with local dengue cases. Principal component analysis (PCA) was applied to extract principal components and PCA score was used to represent the original variables to reduce multi-collinearity. Combining the univariate analysis and prior knowledge, time-series Poisson regression analysis was conducted to quantify the relationship between weather variables, Breteau Index, imported DF cases and the local dengue transmission in Guangzhou, China. The goodness-of-fit of the constructed model was determined by pseudo-R2, Akaike information criterion (AIC) and residual test. There were a total of 707 notified local DF cases from March 2006 to December 2012, with a seasonal distribution from August to November. There were a total of 65 notified imported DF cases from 20 countries, with forty-six cases (70.8%) imported from Southeast Asia. The model showed that local DF cases were positively associated with mosquito density, imported cases, temperature, precipitation, vapour pressure and minimum relative humidity, whilst being negatively associated with air pressure, with different time lags.

Conclusions

Imported DF cases and mosquito density play a critical role in local DF transmission, together with weather variables. The establishment of an early warning system, using existing surveillance datasets will help to control and prevent dengue in Guangzhou, China.

SUBMITTER: Sang S

PROVIDER: S-EPMC4097061 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Predicting local dengue transmission in Guangzhou, China, through the influence of imported cases, mosquito density and climate variability.

Sang Shaowei S Yin Wenwu W Bi Peng P Zhang Honglong H Wang Chenggang C Liu Xiaobo X Chen Bin B Yang Weizhong W Liu Qiyong Q

PloS one 20140714 7

<h4>Introduction</h4>Each year there are approximately 390 million dengue infections worldwide. Weather variables have a significant impact on the transmission of Dengue Fever (DF), a mosquito borne viral disease. DF in mainland China is characterized as an imported disease. Hence it is necessary to explore the roles of imported cases, mosquito density and climate variability in dengue transmission in China. The study was to identify the relationship between dengue occurrence and possible risk f ...[more]

PMID: 25019967

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Project description:BackgroundA key issue in achieving and sustaining malaria elimination is the need to prevent local transmission arising from imported cases of malaria. The likelihood of this occurring depends on a range of local factors, and these can be used to allocate resources to contain transmission. Therefore, a risk assessment and management strategy is required to identify risk indexes for malaria transmission when imported cases occur. These risks also need to be quantified and combined to give a weighted risk index score. This can then be used to allocate the resources to each administrative region to prevent transmission according to the degree of risk.MethodsA list of potential risk indexes were generated from a literature review, expert consultation and panel discussion. These were initially classified into 4 first-level indexes including infection source, transmitting conditions, population vulnerability and control capacity. Each of these was then expanded into more detailed second-level indexes. The Delphi method was then used to obtain expert opinion to review and revise these risk indexes over two consecutive rounds to quantify agreement among experts as to their level of importance. Risk indexes were included in the final Transmission Risk Framework if they achieved a weighted importance score ≥ 4. The Analytic Hierarchy Process was then used to calculate the weight allocated to each of the final risk indexes. This was then used to create an assessment framework that can be used to evaluate local transmission risk in different areas.ResultsTwo rounds of Delphi consultation were conducted. Twenty-three experts were used at each round with 100% recovery rate of participant questionnaires. The coordination coefficients (W) for the two rounds of Delphi consultation were 0.341 and 0.423, respectively (P < 0.05). Three first-level indexes and 13 second-level indexes were identified. The Analytic Hierarchy Process was performed to calculate the weight of the indexes. For the first-level indexes, infection source, transmitting conditions, and control capacity, the index weight was 0.5396, 0.2970 and 0.1634 respectively. For the three top second-level indexes, number of imported malaria cases, Anopheles species, and awareness of timely medical visit of patient, the index weight was 0.3382, 0.2475, and 0.1509 respectively.ConclusionsAn indexed system of transmission risk assessment for imported malaria was established using the Delphi method and the Analytic Hierarchy Process. This was assessed to be an objective and practical tool for assessing transmission risk from imported cases of malaria into China.

Dataset Information

Predicting local dengue transmission in Guangzhou, China, through the influence of imported cases, mosquito density and climate variability.

Introduction

Methodology and principal findings

Conclusions

Publications

Predicting local dengue transmission in Guangzhou, China, through the influence of imported cases, mosquito density and climate variability.

Similar Datasets

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