Epidemiologic characteristics of dengue in China (2010–2014)

Spatiotemporally comparative analysis of three common infectious diseases in China during 2013-2015

BACKGROUND

Dengue fever (DF), influenza, and hand, foot, and mouth disease (HFMD) have had several various degrees of outbreaks in China since the 1900s, posing a serious threat to public health. Previous studies have found that these infectious diseases were often prevalent in the same areas and during the same periods in China.

METHODS

This study combined traditional descriptive statistics and spatial scan statistic methods to analyze the spatiotemporal features of the epidemics of DF, influenza, and HFMD during 2013-2015 in mainland China at the provincial level.

RESULTS

DF got an intensive outbreak in 2014, while influenza and HFMD were stable from 2013 to 2015. DF mostly occurred during August-November, influenza appeared during November-next March, and HFMD happened during April-November. The peaks of these diseases form a year-round sequence; Spatially, HFMD generally has a much higher incidence than influenza and DF and covers larger high-risk areas. The hotspots of influenza tend to move from North China to the southeast coast. The southeastern coastal regions are the high-incidence areas and the most significant hotspots of all three diseases.

CONCLUSIONS

This study suggested that the three diseases can form a year-round sequence in southern China, and the southeast coast of China is a particularly high-risk area for these diseases. These findings may have important implications for the local public health agency to allocate the prevention and control resources.

Molecular characterization of an imported dengue virus serotype 4 isolate from Thailand

The epidemic of dengue virus infections has spread markedly in Yunnan province of China in recent years due to an increase in the number of imported dengue cases. To the best of our knowledge, the present study is the first to report a whole genome sequence and molecular characterization of an imported DENV-4 isolate from Thailand. The current strain, 2013JH285, has an RNA genome of 10,772 nucleotides that shares 99.0% nucleotide and 99.7% amino acid sequence identity with the 2013 Thailand strain CTI2-13. Phylogenetic analysis of the whole genome sequence revealed that the 2013JH285 strain belongs to genotype I of DENV-4. Recombination analysis suggested that the 2013JH285 strain originated from inter-genotypic recombination of DENV-4 strains. The new complete DENV-4 genome sequence reported here might contribute to further understanding of the molecular epidemiology and disease surveillance of DENV-4 in China.

Epidemiological and Virological Characterizations of the 2014 Dengue Outbreak in Guangzhou, China

Dengue used to be recognized as an imported and sporadic disease in China. Since June 2014, an unexpected large dengue outbreak has attacked Guangzhou, China, resulting in more than 40,000 cases. Among the 1,942 laboratory-confirmed hospitalized dengue cases, 121 were diagnosed as severe dengue according to the 2009 WHO guideline, and 2 patients finally died. Laboratory diagnosis and virus isolation demonstrated that the majority (96%) cases were caused by dengue virus serotype 1 (DENV-1), and the others by serotype 2 (DENV-2). 14 DENV strains were isolated from the sera of acute-phase dengue patients during this outbreak, and the complete envelope (E) gene of 12 DENV-1 strains and two DENV-2 strains were determined using RT-PCR assay. Phylogenetic analysis based on the E gene revealed the DENV-1 strains isolated during the outbreak belonged to genotype I and V, respectively. These isolates formed three clades. DENV-2 isolates were assigned to the same clade belonging to genotype cosmopolitan. These strains isolated in 2014 were closely related to the isolates obtained from the same province, Guangdong, in 2013. No amino acid mutations known to increase virulence were identified throughout the E protein of isolates in 2014. These results indicate that dengue is turning into endemic in Guangdong, China, and extensive seroepidemiological investigation and mosquito control measures are critically needed in the future.

Developing a Time Series Predictive Model for Dengue in Zhongshan, China Based on Weather and Guangzhou Dengue Surveillance Data

Background

Dengue is a re-emerging infectious disease of humans, rapidly growing from endemic areas to dengue-free regions due to favorable conditions. In recent decades, Guangzhou has again suffered from several big outbreaks of dengue; as have its neighboring cities. This study aims to examine the impact of dengue epidemics in Guangzhou, China, and to develop a predictive model for Zhongshan based on local weather conditions and Guangzhou dengue surveillance information.

Methods

We obtained weekly dengue case data from 1^st January, 2005 to 31^st December, 2014 for Guangzhou and Zhongshan city from the Chinese National Disease Surveillance Reporting System. Meteorological data was collected from the Zhongshan Weather Bureau and demographic data was collected from the Zhongshan Statistical Bureau. A negative binomial regression model with a log link function was used to analyze the relationship between weekly dengue cases in Guangzhou and Zhongshan, controlling for meteorological factors. Cross-correlation functions were applied to identify the time lags of the effect of each weather factor on weekly dengue cases. Models were validated using receiver operating characteristic (ROC) curves and k-fold cross-validation.

Results

Our results showed that weekly dengue cases in Zhongshan were significantly associated with dengue cases in Guangzhou after the treatment of a 5 weeks prior moving average (Relative Risk (RR) = 2.016, 95% Confidence Interval (CI): 1.845–2.203), controlling for weather factors including minimum temperature, relative humidity, and rainfall. ROC curve analysis indicated our forecasting model performed well at different prediction thresholds, with 0.969 area under the receiver operating characteristic curve (AUC) for a threshold of 3 cases per week, 0.957 AUC for a threshold of 2 cases per week, and 0.938 AUC for a threshold of 1 case per week. Models established during k-fold cross-validation also had considerable AUC (average 0.938–0.967). The sensitivity and specificity obtained from k-fold cross-validation was 78.83% and 92.48% respectively, with a forecasting threshold of 3 cases per week; 91.17% and 91.39%, with a threshold of 2 cases; and 85.16% and 87.25% with a threshold of 1 case. The out-of-sample prediction for the epidemics in 2014 also showed satisfactory performance.

Conclusion

Our study findings suggest that the occurrence of dengue outbreaks in Guangzhou could impact dengue outbreaks in Zhongshan under suitable weather conditions. Future studies should focus on developing integrated early warning systems for dengue transmission including local weather and human movement.

Emerging and re-emerging infectious diseases in an urban city could expand due to increased urbanization, population density, and travel. Dengue, as a mosquito-borne viral disease, has rapidly spread from endemic areas to dengue-free regions, with social, demographic, entomological, and environmental factors affecting its transmission. In recent decades, Guangzhou has again suffered from several big outbreaks of dengue; as have its neighboring cities. In this study, we demonstrated that the dengue outbreaks in Guangzhou could impact outbreaks in Zhongshan, one of its neighboring cities, if suitable climate conditions are present. Such associations between dengue epidemics in two cities may also suggest the important role human movement has played in the transmission of the disease. Based on the association between dengue epidemics in Guangzhou and Zhongshan, and the association between dengue epidemics and weather conditions, we developed a reliable and robust model that predicts the occurrence of epidemics at diffrent thresholds in Zhongshan. These results could be used by local health departments in developing strategies towards dengue prevention and control, and push the public to pay more attention to social factors like human movement in disease transmission.