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. We obtained weekly dengue case data from 1st January, 2005 to 31st 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. 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. 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.
Currently, dengue is considered the most prevalent and rapidly growing mosquito-borne viral disease of humans [1], with 50% of the world’s population at risk [2]. In recent decades, it has spread from endemic areas, mainly tropical and subtropical regions, to dengue-free regions where social and environmental conditions were suitable [3]. Recent estimates of dengue include 390 million infections per year worldwide, including 96 million symptomatic [4]. There are currently no licensed vaccines or specific therapeutics, and the only way of controlling dengue is vector control, though with limited success [5]. However, interventions are still believed to be effective with increased resources [6]. In order to trigger timely interventions, alerts for potential outbreaks are of particular importance to mobilize vector control and to prime or reorganize healthcare services in preparation for a surge in dengue infections. Determining the factors influencing the transmission of dengue is also important for helping to choose appropriate interventions. Dengue is usually transmitted by the bite of a mosquito infected with one of the four dengue virus serotypes. There are many factors involved in the transmission of dengue, including social, demographic, entomological, and environmental factors [1]. Temperature, humidity and rainfall all play important roles in mosquito biology and several studies have demonstrated a relationship between these variables and dengue transmission [7,8]. Additionally, due to the limited flight range of dengue mosquitoes, Aedes aegypti and Aedes albopictus [9,10], it has been recognized that human movement is another conclusive underlying driver of dengue virus dispersal at broad spatial scales such as between communities, regionally and globally [11–14], as well as at a narrow spatial scale from house to house [15,16]. Dengue re-emerged in mainland China in 1978, after more than 30 years’ absence. Since then, outbreaks and epidemics have been reported from time to time in varying scales, mainly in south China such as in Hainan province and Guangdong province [17]. In these periods, the two most severe outbreaks occurred in 1980 and 1986, before incidence rates of dengue became relatively stable and low after 1990 [17]. However, peak outbreaks were documented in Guangdong province again during 2013 (total 4,662 cases in mainland China, 62. 08% in Guangdong province) and 2014 (total 48,162 cases, 93. 83% in Guangdong province) [18–21]. Lai et al. [19] showed that the average annual incidence rate of dengue in mainland China from 1990 to 2014 was 2. 2 cases per million people, while roughly calculated average annual incidence in Guangzhou reached as high as 29. 14 cases per million from 2006 to 2013 [22–24], and 2888. 57 cases per million in 2014 alone (37,305 indigenous cases [25] among 12,9368 million residents [26]). Average annual incidence rate of dengue in Zhongshan is also higher than the national level, with a rate of 30. 46 cases per million people from 1990 to 2014, according to our estimate (S1 Table). In order to reduce dengue incidence in these cities, it is important to analyze the relationship of epidemics between these cities, to find any potential factor influencing the transmission of dengue and to set up an early warning system to initiate timely interventions. In this study, we aimed to examine whether the dengue epidemics in Guangzhou can impact Zhongshan, a nearby city, and developed an early warning system for Zhongshan based on local weather conditions and dengue surveillance information in Guangzhou.
Guangzhou, located at the northern tip of the Pearl River Delta (PRD), is the provincial capital of Guangdong Province (Fig 1). Because of its location, Guangzhou possessed exceptional conditions as a port with a nickname “southern gate of China” [27]. Zhongshan, a medium-size city in Guangdong Province, adjacent to Guangzhou, is located along the west side of the mouth of the Pearl River, directly opposite Shenzhen and Hong Kong, south of Guangzhou and Foshan, east of Jiangmen, and north of Zhuhai and Macau (Fig 1), occupying an area of 1,800. 14 square kilometers with 3. 12 million permanent residents (Census reference 2010). It has a typical subtropical monsoon climate with hot and humid summer, mild to cool winter, monthly average temperature range from 13. 8°C to 28. 6°C, and annual rainfall of about 1,750 mm. We selected these two cities as the study areas due to the high density of Aedes albopictus, the favorable climate, and the frequent outbreaks of dengue with recorded cases of both indigenous and imported cases [28–30]. Data on dengue cases in Guangzhou and Zhongshan for the period 1st January 2005 to 31st December 2014 were collected from the Chinese National Disease Surveillance Reporting System (NDSRS). The NDSRS is a web-based system set up in 2004 by the Chinese CDC. The system covers the entire population (1. 3 billion people) living in all provinces, prefectures, and counties that make up mainland China. Thirty-nine reportable infectious diseases are currently included in this system. Individual cases, including clinically diagnosed and laboratory confirmed cases, are required to be reported by doctors within specified hours after diagnosis to the online system. Dengue has been included in the system as one of the surveilled diseases since 2005 [19], and dengue cases are required to be reported within 24 hours of diagnosis. Since dengue fever is a legally national notifiable infectious disease in China, case data was collected systematically and continuously. Dengue fever cases were diagnosed based on standardized laboratory tests and clinical/epidemiological investigations according to the Dengue Diagnostic Criteria enacted by the China Health Committee in 2001 and 2008. The data we collected from the NDSRS was anonymized. Overseas imported dengue cases were excluded in this study, as they did not represent the native transmission situation. Meteorological data including weekly average minimum temperature, relative humidity, and rainfall for the period January 2005 to December 2014 in Zhongshan (S1 Fig) were collected from the Zhongshan Weather Bureau. Population data of Zhongshan (S2 Fig) was obtained from the Zhongshan Statistical Bureau. In order to calculate the prevalence of dengue infections in the study areas, the time series of weekly case counts were plotted. In order to obtain a robust and smooth model, meteorological data was transformed into new variables using prior moving average of 5 weeks. Cross-correlations were also conducted between natural logarithm transformed weekly dengue case counts in Zhongshan (natural logarithm transformed case counts = Ln (case counts + 1) ) and each new variable (the treated meteorological variable) to determine the best lag of meteorological parameter that leads to Zhongshan’s epidemics. The epidemic data of Zhongshan (n = 522 weeks) was used as the dependent variable in the construction of log-linked negative binomial regression models. The model is one of the generalized linear models (GLMs) which can deal with count data to generate predition models in particular when data are over-dispersed (α>0), and where variance is larger than the mean [31]. The independent variables included were lagged weekly average minimum temperature, relative humidity, rainfall and an index based on the last 5 natural logarithm transformed weekly dengue case counts in Guangzhou in order to model weekly dengue case counts in Zhongshan. The natural logarithm of annual average population in Zhongshan was added as an offset variable. We ran several models with varying combinations of independent variables to obtain the final best-fit model. Here we only displayed the model with all four independent variables as follows: Y~NB (r, p) E (Y) =μ=r (1−p) p Var (Y) =μ+μ2r Log (μ) =β0+log (N) +β1X1+β2X2+β3X3+β4X4 (1) Where Y is the observed weekly dengue case counts in Zhongshan; r and p are parameters of the negative binomial distribution; μ is the expectation of Y; β0 is the intercept; N is the annual population in Zhongshan so that log (N) is put as an offset in the model. X1, X2, X3 and X4 are the treated weekly dengue case counts in Guangzhou, weekly average minimum temperature, relative humidity and rainfall in Zhongshan, with β1, β2, β3, β4 as their coefficient vectors, respecitvely. After we set up every possible model according to different combinations of the independent variables, we used the Akaike information criterion (AIC) [32], the first theoretic criterion to have gained widespread acceptance, to select the best-fit model, which can deal with the trade-off between the goodness of fit of the models and the complexity of the models. Receiver operating characteristic (ROC) curve was used to validate our forecasting models at first. It can illustrate the performance of our models as a binary classifier at varied discrimination thresholds, and provide the most optimal cut-off for forecasting [33]. In order to validate the robustness of the model and avoid overfitting, k-fold cross-validation [34] was also applied. The original dataset was partitioned randomly into k subsets using random numbers generating from SPSS. For each cross-validation step, a single subset was retained as the test set, and the remaining k-1 subsets were used as the training set. The cross-validation process was then repeated k times with each of the k subsets used exactly once as the test set. The k results from the folds were then averaged to produce a single estimation. In this study, the k value is set to 10. In addition, we used the samples from 1th January 2005 to 27th December 2013 (369 weeks in total) as training data and the samples from 28th December 2013 to 26th December 2014 (52 weeks in total) as test data to conduct another out-of-sample prediction. The data management and statistical analyses were carried out using SPSS (version 19. 0). All p values are two-sided and statistical significance was determined at the p<0. 05 level.
The result on the cross-correlations of natural logarithm transformed weekly case numbers of Zhongshan and Guangzhou (Fig 3) showed that the previous dengue epidemics in Guangzhou may have a great impact on the current week in Zhongshan, especially the last 5 weeks (correlation coefficient > 0. 5). Taking this into consideration, an index was created based on the last 5 weekly Guangzhou case numbers via prior moving average, to be a predictor for forecasting epidemics in Zhongshan. Table 1 showed the results on log-linked negative binomial regression models. The model including dengue cases in Guangzhou, minimum temperature, relative humidity, and rainfall was considered as the best-fit model (AIC = 841. 442). In this model, the treated Guangzhou dengue cases had a possitive correlation with Zhangshan’s weekly dengue cases (Relative Risk (RR) = 2. 016,95% Confidence Interval (CI): 1. 845–2. 203). Similarly, treated minimum temperature (RR = 1. 335,95% CI: 1. 125–1. 583), treated relative humidity (1. 207,95% CI: 1. 151–1. 265), and treated rainfall (RR = 1. 031,95% CI: 1. 026–1. 037) also had possitive correlation with dengue case counts in Zhongshan. When validated via ROC analysis using entire data, the forecasting model performed well in generating early warnings of dengue epidemics in Zhongshan at a reporting threshold of 3 cases in a single week, which means that the model can successfully estimate and announce epidemic risks of an outbreak involving ≥3 cases per week, with 0. 969 area under the receiver operating characteristic curve (AUC), sensitivity of 92. 5% and specificity of 92. 6% (Fig 5A). Similarly, at a threshold of 2 cases per week, the forecasting model showed robust results, with 0. 957 AUC, 88. 9% sensitivity and 90. 6% specificity; and at a threshold of 1 case per week, it had 0. 938 AUC, 83. 0% sensitivity and 90. 0% specificity (Fig 5B and 5C). The out-of-sample prediction for dengue epidemics in Zhongshan from 28th December 2013 to 26th December 2014 (52 weeks in total) was showed in Fig 6 and summarized in Table 2. The predictive model performed well in generating early warnings of dengue epidemics at the epidemic threshold of 3 cases per week, with 94. 12% sensitivity and 91. 43% specificity.
The study has demonstrated that dengue outbreaks in Guangzhou could impact outbreaks in Zhongshan, if suitable climate is available. A reliable and robust model, that predicts the occurrence of epidemics at a thredshold of 1,2, or 3 dengue cases per week in Zhongshan was developed. The study results could be used by local health departments in developing strategies towards dengue prevention and control measures in Zhongshan. Future studies need to include social factors like human movement in the models.
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.
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Developing a Time Series Predictive Model for Dengue in Zhongshan, China Based on Weather and Guangzhou Dengue Surveillance Data