Burden of Disease Measured by Disability-Adjusted Life Years and a Disease Forecasting Time Series Model of Scrub Typhus in Laiwu,China

Background

Laiwu District is recognized as a hyper-endemic region for scrub typhus in Shandong Province, but the seriousness of this problem has been neglected in public health circles.

Methodology/Principal Findings

A disability-adjusted life years (DALYs) approach was adopted to measure the burden of scrub typhus in Laiwu, China during the period 2006 to 2012. A multiple seasonal autoregressive integrated moving average model (SARIMA) was used to identify the most suitable forecasting model for scrub typhus in Laiwu. Results showed that the disease burden of scrub typhus is increasing yearly in Laiwu, and which is higher in females than males. For both females and males, DALY rates were highest for the 60–69 age group. Of all the SARIMA models tested, the SARIMA(2,1,0)(0,1,0)₁₂ model was the best fit for scrub typhus cases in Laiwu. Human infections occurred mainly in autumn with peaks in October.

Conclusions/Significance

Females, especially those of 60 to 69 years of age, were at highest risk of developing scrub typhus in Laiwu, China. The SARIMA (2,1,0)(0,1,0)₁₂ model was the best fit forecasting model for scrub typhus in Laiwu, China. These data are useful for developing public health education and intervention programs to reduce disease.     

Mixed Poisson regression models with covariate dependent rates

This paper studies a class of Poisson mixture models that includes covariates in rates. This model contains Poisson regression and independent Poisson mixtures as special cases. Estimation methods based on the EM and quasi-Newton algorithms, properties of these estimates, a model selection procedure, residual analysis, and goodness-of-fit test are discussed. A Monte Carlo study investigates implementation and model choice issues. This methodology is used to analyze seizure frequency and Ames salmonella assay data.     

The Effect of Meteorological Variables on the Transmission of Hand,Foot and Mouth Disease in Four Major Cities of Shanxi Province,China: A Time Series Data Analysis (2009-2013)

Increased incidence of hand, foot and mouth disease (HFMD) has been recognized as a critical challenge to communicable disease control and public health response. This study aimed to quantify the association between climate variation and notified cases of HFMD in selected cities of Shanxi Province, and to provide evidence for disease control and prevention. Meteorological variables and HFMD cases data in 4 major cities (Datong, Taiyuan, Changzhi and Yuncheng) of Shanxi province, China, were obtained from the China Meteorology Administration and China CDC respectively over the period 1 January 2009 to 31 December 2013. Correlations analyses and Seasonal Autoregressive Integrated Moving Average (SARIMA) models were used to identify and quantify the relationship between the meteorological variables and HFMD. HFMD incidence varied seasonally with the majority of cases in the 4 cities occurring from May to July. Temperatures could play important roles in the incidence of HFMD in these regions. The SARIMA models indicate that a 1° C rise in average, maximum and minimum temperatures may lead to a similar relative increase in the number of cases in the 4 cities. The lag times for the effects of temperatures were identified in Taiyuan, Changzhi and Yuncheng. The numbers of cases were positively associated with average and minimum temperatures at a lag of 1 week in Taiyuan, Changzhi and Yuncheng, and with maximum temperature at a lag of 2 weeks in Yuncheng. Positive association between the temperature and HFMD has been identified from the 4 cities in Shanxi Province, although the role of weather variables on the transmission of HFMD varied in the 4 cities. Relevant prevention measures and public health action are required to reduce future risks of climate change with consideration of local climatic conditions.     

Climate Variability,Weather and Enteric Disease Incidence in New Zealand: Time Series Analysis

Background

Evaluating the influence of climate variability on enteric disease incidence may improve our ability to predict how climate change may affect these diseases.

Objectives

To examine the associations between regional climate variability and enteric disease incidence in New Zealand.

Methods

Associations between monthly climate and enteric diseases (campylobacteriosis, salmonellosis, cryptosporidiosis, giardiasis) were investigated using Seasonal Auto Regressive Integrated Moving Average (SARIMA) models.

Results

No climatic factors were significantly associated with campylobacteriosis and giardiasis, with similar predictive power for univariate and multivariate models. Cryptosporidiosis was positively associated with average temperature of the previous month (β =  0.130, SE =  0.060, p <0.01) and inversely related to the Southern Oscillation Index (SOI) two months previously (β =  −0.008, SE =  0.004, p <0.05). By contrast, salmonellosis was positively associated with temperature (β  = 0.110, SE = 0.020, p<0.001) of the current month and SOI of the current (β  = 0.005, SE = 0.002, p<0.050) and previous month (β  = 0.005, SE = 0.002, p<0.05). Forecasting accuracy of the multivariate models for cryptosporidiosis and salmonellosis were significantly higher.

Conclusions

Although spatial heterogeneity in the observed patterns could not be assessed, these results suggest that temporally lagged relationships between climate variables and national communicable disease incidence data can contribute to disease prediction models and early warning systems.     

Sensitivity analysis of common statistical models used to study the short-term effects of air pollution on health

The relationship between photochemical air pollutants (nitrogen dioxide and ozone) and emergency room admissions for asthma in Madrid (Spain) for the period 1995-1998 was analysed using the statistical models commonly used to studying the short-term effects of air pollution on health: linear and Cochrane-Orcutt regression, standard Poisson and Poisson corrected by overdispersion, Poisson autoregressive models, and generalised additive models. Linear regression models presented residual autocorrelation, Poisson regression models also showed overdispersion, and generalised additive models did not show residual autocorrelation and overdispersion was substantially reduced. Linear models provided biased estimates because our health outcome is non-normally distributed. Estimates from Poisson regression allowing for overdispersion and autocorrelation did not differ substantially from those reported by generalised additive models, which present the best model fit in terms of the absence of autocorrelation and reduction of overdispersion.     

Investigating the relationships between the distribution of British ground beetle species (Coleoptera, Carabidae) and temperature, precipitation and altitude

Aims We examine the relationships between the distribution of British ground beetle species and climatic and altitude variables with a view to developing models for evaluating the impact of climate change. Location Data from 1684 10‐km squares in Britain were used to model species–climate/altitude relationships. A validation data set was composed of data from 326 British 10‐km squares not used in the model data set. Methods The relationships between incidence and climate and altitude variables for 137 ground beetle species were investigated using logistic regression. The models produced were subjected to a validation exercise using the Kappa statistic with a second data set of 30 species. Distribution patterns for four species were predicted for Britain using the regression equations generated. Results As many as 136 ground beetle species showed significant relationships with one or more of the altitude and climatic variables but the amount of variation explained by the models was generally poor. Models explaining 20% or more of the variation in species incidence were generated for only 10 species. Mean summer temperature and mean annual temperature were the best predictors for eight and six of these 10 species respectively. Few models based on altitude, annual precipitation and mean winter temperature were good predictors of ground beetle species distribution. The results of the validation exercise were mixed, with models for four species showing good or moderate fits whilst the remainder were poor. Main conclusions Whilst there were many significant relationships between British ground beetle species distributions and altitude and climatic variables, these variables did not appear to be good predictors of ground beetle species distribution. The poor model performance appears to be related to the coarse nature of the response and predictor data sets and the absence of key predictors from the models.     

Remote sensing-based time series models for malaria early warning in the highlands of Ethiopia

ABSTRACT: BACKGROUND: Malaria is one of the leading public health problems in most of sub-Saharan Africa, particularly in Ethiopia. Almost all demographic groups are at risk of malaria because of seasonal and unstable transmission of the disease. Therefore, there is a need to develop malaria early-warning systems to enhance public health decision making for control and prevention of malaria epidemics. Data from orbiting earth-observing sensors can monitor environmental risk factors that trigger malaria epidemics. Remotely sensed environmental indicators were used to examine the influences of climatic and environmental variability on temporal patterns of malaria cases in the Amhara region of Ethiopia. METHODS: In this study seasonal auto regressive integrated moving average (SARIMA) models were used to quantify the relationship between malaria cases and remotely sensed environmental variables, including rainfall, land-surface temperature (LST), vegetation indices (NDVI and EVI), and actual evapotranspiration (ETa) with lags ranging from one to three months. Predictions from the best model with environmental variables were compared to the actual observations from the last 12 months of the time series. RESULTS: Malaria cases exhibited positive associations with LST at a lag of one month and positive associations with indicators of moisture (rainfall, EVI and ETa) at lags from one to three months. SARIMA models that included these environmental covariates had better fits and more accurate predictions, as evidenced by lower AIC and RMSE values, than models without environmental covariates. CONCLUSIONS: Malaria risk indicators such as satellite-based rainfall estimates, LST, EVI, and ETa exhibited significant lagged associations with malaria cases in the Amhara region and improved model fit and prediction accuracy. These variables can be monitored frequently and extensively across large geographic areas using data from earth-observing sensors to support public health decisions.     

The contribution of vegetation and landscape configuration for predicting environmental change impacts on Iberian birds

Although climate is known to be one of the key factors determining animal species distributions amongst others, projections of global change impacts on their distributions often rely on bioclimatic envelope models. Vegetation structure and landscape configuration are also key determinants of distributions, but they are rarely considered in such assessments. We explore the consequences of using simulated vegetation structure and composition as well as its associated landscape configuration in models projecting global change effects on Iberian bird species distributions. Both present-day and future distributions were modelled for 168 bird species using two ensemble forecasting methods: Random Forests (RF) and Boosted Regression Trees (BRT). For each species, several models were created, differing in the predictor variables used (climate, vegetation, and landscape configuration). Discrimination ability of each model in the present-day was then tested with four commonly used evaluation methods (AUC, TSS, specificity and sensitivity). The different sets of predictor variables yielded similar spatial patterns for well-modelled species, but the future projections diverged for poorly-modelled species. Models using all predictor variables were not significantly better than models fitted with climate variables alone for ca. 50% of the cases. Moreover, models fitted with climate data were always better than models fitted with landscape configuration variables, and vegetation variables were found to correlate with bird species distributions in 26-40% of the cases with BRT, and in 1-18% of the cases with RF. We conclude that improvements from including vegetation and its landscape configuration variables in comparison with climate only variables might not always be as great as expected for future projections of Iberian bird species.     

The importance of climatic factors and outliers in predicting regional monthly campylobacteriosis risk in Georgia,USA

Incidence of Campylobacter infection exhibits a strong seasonal component and regional variations in temperate climate zones. Forecasting the risk of infection regionally may provide clues to identify sources of transmission affected by temperature and precipitation. The objectives of this study were to (1) assess temporal patterns and differences in campylobacteriosis risk among nine climatic divisions of Georgia, USA, (2) compare univariate forecasting models that analyze campylobacteriosis risk over time with those that incorporate temperature and/or precipitation, and (3) investigate alternatives to supposedly random walk series and non-random occurrences that could be outliers. Temporal patterns of campylobacteriosis risk in Georgia were visually and statistically assessed. Univariate and multivariable forecasting models were used to predict the risk of campylobacteriosis and the coefficient of determination (R ²) was used for evaluating training (1999–2007) and holdout (2008) samples. Statistical control charting and rolling holdout periods were investigated to better understand the effect of outliers and improve forecasts. State and division level campylobacteriosis risk exhibited seasonal patterns with peaks occurring between June and August, and there were significant associations between campylobacteriosis risk, precipitation, and temperature. State and combined division forecasts were better than divisions alone, and models that included climate variables were comparable to univariate models. While rolling holdout techniques did not improve predictive ability, control charting identified high-risk time periods that require further investigation. These findings are important in (1) determining how climatic factors affect environmental sources and reservoirs of Campylobacter spp. and (2) identifying regional spikes in the risk of human Campylobacter infection and their underlying causes.     

Association of Climatic Variability,Vector Population and Malarial Disease in District of Visakhapatnam,India: A Modeling and Prediction Analysis

Background

Malarial incidence, severity, dynamics and distribution of malaria are strongly determined by climatic factors, i.e., temperature, precipitation, and relative humidity. The objectives of the current study were to analyse and model the relationships among climate, vector and malaria disease in district of Visakhapatnam, India to understand malaria transmission mechanism (MTM).

Methodology

Epidemiological, vector and climate data were analysed for the years 2005 to 2011 in Visakhapatnam to understand the magnitude, trends and seasonal patterns of the malarial disease. Statistical software MINITAB ver. 14 was used for performing correlation, linear and multiple regression analysis.

Results/Findings

Perennial malaria disease incidence and mosquito population was observed in the district of Visakhapatnam with peaks in seasons. All the climatic variables have a significant influence on disease incidence as well as on mosquito populations. Correlation coefficient analysis, seasonal index and seasonal analysis demonstrated significant relationships among climatic factors, mosquito population and malaria disease incidence in the district of Visakhapatnam, India. Multiple regression and ARIMA (I) models are best suited models for modeling and prediction of disease incidences and mosquito population. Predicted values of average temperature, mosquito population and malarial cases increased along with the year. Developed MTM algorithm observed a major MTM cycle following the June to August rains and occurring between June to September and minor MTM cycles following March to April rains and occurring between March to April in the district of Visakhapatnam. Fluctuations in climatic factors favored an increase in mosquito populations and thereby increasing the number of malarial cases. Rainfall, temperatures (20°C to 33°C) and humidity (66% to 81%) maintained a warmer, wetter climate for mosquito growth, parasite development and malaria transmission.

Conclusions/Significance

Changes in climatic factors influence malaria directly by modifying the behaviour and geographical distribution of vectors and by changing the length of the life cycle of the parasite.     

Comparing models for early warning systems of neglected tropical diseases

Background

Early warning systems (EWS) are management tools to predict the occurrence of epidemics of infectious diseases. While climate-based EWS have been developed for malaria, no standard protocol to evaluate and compare EWS has been proposed. Additionally, there are several neglected tropical diseases whose transmission is sensitive to environmental conditions, for which no EWS have been proposed, though they represent a large burden for the affected populations.

Methodology/Principal Findings

In the present paper, an overview of the available linear and non-linear tools to predict seasonal time series of diseases is presented. Also, a general methodology to compare and evaluate models for prediction is presented and illustrated using American cutaneous leishmaniasis, a neglected tropical disease, as an example. The comparison of the different models using the predictive R ² for forecasts of “out-of-fit” data (data that has not been used to fit the models) shows that for the several linear and non-linear models tested, the best results were obtained for seasonal autoregressive (SAR) models that incorporate climatic covariates. An additional bootstrapping experiment shows that the relationship of the disease time series with the climatic covariates is strong and consistent for the SAR modeling approach. While the autoregressive part of the model is not significant, the exogenous forcing due to climate is always statistically significant. Prediction accuracy can vary from 50% to over 80% for disease burden at time scales of one year or shorter.

Conclusions/Significance

This study illustrates a protocol for the development of EWS that includes three main steps: (i) the fitting of different models using several methodologies, (ii) the comparison of models based on the predictability of “out-of-fit” data, and (iii) the assessment of the robustness of the relationship between the disease and the variables in the model selected as best with an objective criterion.     

Biometeorological and autoregressive indices for predicting olive pollen intensity

This paper reports on modelling to predict airborne olive pollen season severity, expressed as a pollen index (PI), in Córdoba province (southern Spain) several weeks prior to the pollen season start. Using a 29-year database (1982–2010), a multivariate regression model based on five indices—the index-based model—was built to enhance the efficacy of prediction models. Four of the indices used were biometeorological indices: thermal index, pre-flowering hydric index, dormancy hydric index and summer index; the fifth was an autoregressive cyclicity index based on pollen data from previous years. The extreme weather events characteristic of the Mediterranean climate were also taken into account by applying different adjustment criteria. The results obtained with this model were compared with those yielded by a traditional meteorological-based model built using multivariate regression analysis of simple meteorological-related variables. The performance of the models (confidence intervals, significance levels and standard errors) was compared, and they were also validated using the bootstrap method. The index-based model built on biometeorological and cyclicity indices was found to perform better for olive pollen forecasting purposes than the traditional meteorological-based model.     

Ice age legacies in the geographical distribution of tree species richness in Europe

Aim This study uses a high‐resolution simulation of the Last Glacial Maximum (LGM) climate to assess: (1) whether LGM climate still affects the geographical species richness patterns in the European tree flora and (2) the relative importance of modern and LGM climate as controls of tree species richness in Europe. Location The parts of Europe that were unglaciated during the LGM. Methods Atlas data on the distributions of 55 tree species were linked with data on modern and LGM climate and climatic heterogeneity in a geographical information system with a 60‐km grid. Four measures of species richness were computed: total richness, and richness of the 18 most restricted species, 19 species of medium incidence (intermediate species) and 18 most widespread species. We used ordinary least‐squares regression and spatial autoregressive modelling to test and estimate the richness–climate relationships. Results LGM climate constituted the best single set of explanatory variables for richness of restricted species, while modern climate and climatic heterogeneity was best for total and widespread species richness and richness of intermediate species, respectively. The autoregressive model with all climatic predictors was supported for all richness measures using an information‐theoretic approach, albeit only weakly so for total species richness. Among the strongest relationships were increases in total and intermediate richness with climatic heterogeneity and in restricted richness with LGM growing‐degree‐days. Partial regression showed that climatic heterogeneity accounted for the largest unique variation fraction for intermediate richness, while LGM climate was particularly important for restricted richness. Main conclusions LGM climate appears to still affect geographical patterns of tree species richness in Europe, albeit the relative importance of modern and LGM climate depends on range size. Notably, LGM climate is a strong richness control for species with a restricted range, which appear to still be associated with their glacial refugia.     

The timing of life-history events in a changing climate

Although empirical and theoretical studies suggest that climate influences the timing of life-history events in animals and plants, correlations between climate and the timing of events such as egg-laying, migration or flowering do not reveal the mechanisms by which natural selection operates on life-history events. We present a general autoregressive model of the timing of life-history events in relation to variation in global climate that, like autoregressive models of population dynamics, allows for a more mechanistic understanding of the roles of climate, resources and competition. We applied the model to data on 50 years of annual dates of first flowering by three species of plants in 26 populations covering 4 degrees of latitude in Norway. In agreement with earlier studies, plants in most populations and all three species bloomed earlier following warmer winters. Moreover, our model revealed that earlier blooming reflected increasing influences of resources and density-dependent population limitation under climatic warming. The insights available from the application of this model to phenological data in other taxa will contribute to our understanding of the roles of endogenous versus exogenous processes in the evolution of the timing of life-history events in a changing climate.     

Regional Impact of Climate on Japanese Encephalitis in Areas Located near the Three Gorges Dam

Background

In this study, we aim to identify key climatic factors that are associated with the transmission of Japanese encephalitis virus in areas located near the Three Gorges Dam, between 1997 and 2008.

Methods

We identified three geographical regions of Chongqing, based on their distance from the Three Gorges Dam. Collectively, the three regions consisted of 12 districts from which study information was collected. Zero-Inflated Poisson Regression models were run to identify key climatic factors of the transmission of Japanese encephalitis virus for both the whole study area and for each individual region; linear regression models were conducted to examine the fluctuation of climatic variables over time during the construction of the Three Gorges Dam.

Results

Between 1997 and 2008, the incidence of Japanese encephalitis decreased throughout the entire city of Chongqing, with noticeable variations taking place in 2000, 2001 and 2006. The eastern region, which is closest to the Three Gorges Dam, suffered the highest incidence of Japanese encephalitis, while the western region experienced the lowest incidence. Linear regression models revealed that there were seasonal fluctuations of climatic variables during this period. Zero-Inflated Poisson Regression models indicated a significant positive association between temperature (with a lag of 1 and 3 months) and Japanese encephalitis incidence, and a significant negative association between rainfall (with a lag of 0 and 4 months) and Japanese encephalitis incidence.

Conclusion

The spatial and temporal trends of Japanese encephalitis incidence that occurred in the City of Chongqing were associated with temperature and rainfall. Seasonal fluctuations of climatic variables during this period were also observed. Additional studies that focus on long-term data collection are needed to validate the findings of this study and to further explore the effects of the Three Gorges Dam on Japanese encephalitis and other related diseases.     

人参潜在地理分布以及气候变化对其影响预测

本文以人参为研究对象,基于人参分布点位数据和22个气候环境因子数据,运用BioMod2平台10个物种分布模型对当前我国东北地区人参潜在生境分布进行预测.以受试者工作特征曲线(ROC)为权重集成10个模型的模拟结果,构建组合模型,并基于该模型预测了IPCC 第五次评估报告中RCP 8.5、RCP 6.0、RCP 4.5和RCP 2.6等4种排放情景下21世纪50和70年代人参潜在分布范围.结果表明: 在基准气候条件下,人参适宜生境面积占研究区总面积的10.4%,此类地区主要分布于研究区东北部长白山地区以及小兴安岭东南部区域的森林地带.在未来不同的排放情景下研究区人参的适宜生境变化显著,总体上分布范围将有一定程度的缩小.同时参与建模的10种模型在统计学精度、预测结果以及变量权重上都有差异.模型精度计算结果表明,MAXENT模拟效果最好,GAM、RF和ANN次之,SRE模拟精度最低.本文构建的组合模型在一定程度上提高了现有物种分布模型的预测精度,从而使模拟效果更优.     

Multi-Step Polynomial Regression Method to Model and Forecast Malaria Incidence

Malaria is one of the most severe problems faced by the world even today. Understanding the causative factors such as age, sex, social factors, environmental variability etc. as well as underlying transmission dynamics of the disease is important for epidemiological research on malaria and its eradication. Thus, development of suitable modeling approach and methodology, based on the available data on the incidence of the disease and other related factors is of utmost importance. In this study, we developed a simple non-linear regression methodology in modeling and forecasting malaria incidence in Chennai city, India, and predicted future disease incidence with high confidence level. We considered three types of data to develop the regression methodology: a longer time series data of Slide Positivity Rates (SPR) of malaria; a smaller time series data (deaths due to Plasmodium vivax) of one year; and spatial data (zonal distribution of P. vivax deaths) for the city along with the climatic factors, population and previous incidence of the disease. We performed variable selection by simple correlation study, identification of the initial relationship between variables through non-linear curve fitting and used multi-step methods for induction of variables in the non-linear regression analysis along with applied Gauss-Markov models, and ANOVA for testing the prediction, validity and constructing the confidence intervals. The results execute the applicability of our method for different types of data, the autoregressive nature of forecasting, and show high prediction power for both SPR and P. vivax deaths, where the one-lag SPR values plays an influential role and proves useful for better prediction. Different climatic factors are identified as playing crucial role on shaping the disease curve. Further, disease incidence at zonal level and the effect of causative factors on different zonal clusters indicate the pattern of malaria prevalence in the city. The study also demonstrates that with excellent models of climatic forecasts readily available, using this method one can predict the disease incidence at long forecasting horizons, with high degree of efficiency and based on such technique a useful early warning system can be developed region wise or nation wise for disease prevention and control activities.     

不同模型对拟合大兴安岭林火发生与气象因素关系的适用性

选用符合林火发生数据结构的Poisson和零膨胀Poisson(ZIP)模型对大兴安岭林区1980—2005年间林火发生与气象因素关系进行建模分析,并与普通最小二乘回归(ordinary least squares,OLS)方法的结果进行了对比分析.结果表明:OLS模型对研究区域林火发生与气象因素关系的拟合结果较差(R2=0.215);Poisson和ZIP模型的拟合效果较好,具有较好的火灾次数预测能力,且ZIP模型的预测能力高于Poisson模型.运用AIC和Vuong检验方法对Poisson和ZIP模型的拟合水平进行进一步检验,表明ZIP模型的数据拟合度优于Poisson模型.     

Cutaneous Leishmaniasis and Sand Fly Fluctuations Are Associated with El Ni?o in Panamá

Background

Cutaneous Leishmaniasis (CL) is a neglected tropical vector-borne disease. Sand fly vectors (SF) and Leishmania spp parasites are sensitive to changes in weather conditions, rendering disease transmission susceptible to changes in local and global scale climatic patterns. Nevertheless, it is unclear how SF abundance is impacted by El Niño Southern Oscillation (ENSO) and how these changes might relate to changes in CL transmission.

Methodology and Findings

We studied association patterns between monthly time series, from January 2000 to December 2010, of: CL cases, rainfall and temperature from Panamá, and an ENSO index. We employed autoregressive models and cross wavelet coherence, to quantify the seasonal and interannual impact of local climate and ENSO on CL dynamics. We employed Poisson Rate Generalized Linear Mixed Models to study SF abundance patterns across ENSO phases, seasons and eco-epidemiological settings, employing records from 640 night-trap sampling collections spanning 2000–2011. We found that ENSO, rainfall and temperature were associated with CL cycles at interannual scales, while seasonal patterns were mainly associated with rainfall and temperature. Sand fly (SF) vector abundance, on average, decreased during the hot and cold ENSO phases, when compared with the normal ENSO phase, yet variability in vector abundance was largest during the cold ENSO phase. Our results showed a three month lagged association between SF vector abundance and CL cases.

Conclusion

Association patterns of CL with ENSO and local climatic factors in Panamá indicate that interannual CL cycles might be driven by ENSO, while the CL seasonality was mainly associated with temperature and rainfall variability. CL cases and SF abundance were associated in a fashion suggesting that sudden extraordinary changes in vector abundance might increase the potential for CL epidemic outbreaks, given that CL epidemics occur during the cold ENSO phase, a time when SF abundance shows its highest fluctuations.     

Predicting Local Dengue Transmission in Guangzhou,China, through the Influence of Imported Cases,Mosquito Density and Climate Variability

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.