Spatiotemporal analysis of indigenous and imported dengue fever cases in Guangdong province, China

The epidemiology and evolutionary dynamics of massive dengue outbreak in China, 2019

Introduction

In 2019, China experienced massive dengue outbreaks with high incidence and expanded outbreak areas. The study aims to depict dengue's epidemiology and evolutionary dynamics in China and explore the possible origin of these outbreaks.

Methods

Records of confirmed dengue cases in 2019 were obtained from the China Notifiable Disease Surveillance System. The sequences of complete envelope gene detected from the outbreak provinces in China in 2019 were retrieved from GenBank. Maximum Likelihood trees were constructed to genotype the viruses. The median-joining network was used to visualize fine-scale genetic relationships. Four methods were used to estimate the selective pressure.

Results

A total of 22,688 dengue cases were reported, 71.4% of which were indigenous cases and 28.6% were imported cases (including from abroad and from other domestic provinces). The abroad cases were predominantly imported from Southeast Asia countries (94.6%), with Cambodia (3,234 cases, 58.9%), and Myanmar (1,097 cases, 20.0%) ranked as the top two. A total of 11 provinces with dengue outbreaks were identified in the central-south of China, of which Yunnan and Guangdong provinces had the highest number of imported and indigenous cases. The primary source of imported cases in Yunnan was from Myanmar, while in the other ten provinces, the majority of imported cases were from Cambodia. Guangdong, Yunnan and Guangxi provinces were China's primary sources of domestically imported cases. Phylogenetic analysis of the viruses in outbreak provinces revealed three genotypes: (I, IV, and V) in DENV 1, Cosmopolitan and Asian I genotypes in DENV 2, and two genotypes (I and III) in DENV 3. Some genotypes concurrently circulated in different outbreak provinces. Most of the viruses were clustered with those from Southeast Asia. Haplotype network analysis showed that Southeast Asia, possibly Cambodia and Thailand, was the respective origin of the viruses in clade 1 and 4 for DENV 1. Positive selection was detected at codon 386 in clade 1.

Conclusion

Dengue importation from abroad, especially from Southeast Asia, resulted in the dengue epidemic in China in 2019. Domestic transmission between provinces and positive selection on virus evolution may contribute to the massive dengue outbreaks.

Epidemiological characteristics of imported acute infectious diseases in Guangzhou, China, 2005-2019

BACKGROUND

The global spread of infectious diseases is currently a prominent threat to public health, with the accelerating pace of globalization and frequent international personnel intercourse. The present study examined the epidemiological characteristics of overseas imported cases of acute infectious diseases in Guangzhou, China.

METHODS

We retrospectively investigated the distribution of diseases, demographic characteristics, and temporal and spatial variations of imported cases of acute infectious diseases in Guangzhou based on the surveillance data of notifiable infectious diseases from 2005 to 2019, provided by Guangzhou center for Disease Control and Prevention. The Cochran-Armitage trend test was applied to examine the trend in the number of imported cases over time.

RESULTS

A total of 1,025 overseas imported cases of acute infectious diseases were identified during the study period. The top three diseases were dengue (67.12%), malaria (12.39%), and influenza A (H1N1) pdm09 (4.10%). Imported cases were predominantly males, with a sex ratio of 2.6: 1 and 75.22% of the cases were those aged 20-49 years. Businessmen, workers, students and unemployed persons accounted for a large proportion of the cases (68.49%) and many of the cases came from Southeast Asia (59.02%). The number of imported cases of acute infectious diseases increased during the study period and hit 318 in 2019. A clear seasonal pattern was observed in the number of imported cases with a peak period between June and November. Imported cases were reported in all of the 11 districts in Guangzhou and the central districts were more seriously affected compared with other districts.

CONCLUSIONS

The burden of dengue imported from overseas was substantial and increasing in Guangzhou, China, with the peak period from June to November. Dengue was the most common imported disease. Most imported cases were males aged 20-49 years and businessmen. Further efforts, such as strengthening surveillance of imported cases, paying close attention to the epidemics in hotspots, and improving the ability to detect the imported cases from overseas, are warranted to control infectious diseases especially in the center of the city with a higher population density highly affected by imported cases.

Spatio-temporal evolution and trend prediction of the incidence of Class B notifiable infectious diseases in China: a sample of statistical data from 2007 to 2020

BACKGROUND

With the accelerated global integration and the impact of climatic, ecological and social environmental changes, China will continue to face the challenge of the outbreak and spread of emerging infectious diseases and traditional ones. This study aims to explore the spatial and temporal evolutionary characteristics of the incidence of Class B notifiable infectious diseases in China from 2007 to 2020, and to forecast the trend of it as well. Hopefully, it will provide a reference for the formulation of infectious disease prevention and control strategies.

METHODS

Data on the incidence rates of Class B notifiable infectious diseases in 31 provinces, municipalities and autonomous regions of China from 2007 to 2020 were collected for the prediction of the spatio-temporal evolution and spatial correlation as well as the incidence of Class B notifiable infectious diseases in China based on global spatial autocorrelation and Autoregressive Integrated Moving Average (ARIMA).

RESULTS

From 2007 to 2020, the national incidence rate of Class B notifiable infectious diseases (from 272.37 per 100,000 in 2007 to 190.35 per 100,000 in 2020) decreases year by year, and the spatial distribution shows an "east-central-west" stepwise increase. From 2007 to 2020, the spatial clustering of the incidence of Class B notifiable infectious diseases is significant and increasing year by year (Moran's I index values range from 0.189 to 0.332, p < 0.05). The forecasted incidence rates of Class B notifiable infectious diseases nationwide from 2021 to 2024 (205.26/100,000, 199.95/100,000, 194.74/100,000 and 189.62/100,000) as well as the forecasted values for most regions show a downward trend, with only some regions (Guangdong, Hunan, Hainan, Tibet, Guangxi and Guizhou) showing an increasing trend year by year.

CONCLUSIONS

The current study found that since there were significant regional disparities in the prevention and control of infectious diseases in China between 2007 and 2020, the reduction of the incidence of Class B notifiable infectious diseases requires the joint efforts of the surrounding provinces. Besides, special attention should be paid to provinces with an increasing trend in the incidence of Class B notifiable infectious diseases to prevent the re-emergence of certain traditional infectious diseases in a particular province or even the whole country, as well as the outbreak and spread of emerging infectious diseases.

Spatial dispersal of <em>Aedes albopictus</em> mosquitoes captured by the modified sticky ovitrap in Selangor, Malaysia

Dengue is a major mosquito-borne disease in many tropical and sub-tropical countries worldwide, with entomological surveillance and control activities as the key management approaches. This study aimed to explore the spatial dispersal of the vector Aedes albopictus, captured by the modified sticky ovitrap (MSO) in residential areas with low-rise buildings in Selangor, Malaysia. Distribution maps were created and shown as temporally distinguished classes based on hotspot analysis by Getis-Ord; spatial autocorrelation assessed by semivariograms using the exponential Kernel function; and universal Kriging showing areas with estimated high and low vector densities. Distribution, hotspot and interpolated maps were analysed based on the total number of mosquitoes by month and week. All maps in the present study were generated and visualised in ArcMap. Spatial autocorrelation of Ae. albopictus based on the monthly occurrence of Ae. albopictus was found in March, April, October, November and December 2018, and when based on the weekly numbers, in weeks 1, 2, 3, 5, 7, 12, 14, 25, 26, 27, 31, 33, 42, 49 and 52. Semivariograms, based on the monthly and weekly numbers of Ae. albopictus, indicated spatial autocorrelation of the species extending between 50 and 70 m. The mosquito density maps reported in this study may provide beneficial information to facilitate implementation of more efficient entomological control activities.

The epidemiological characteristics of dengue in high-risk areas of China, 2013-2016

Introduction

Dengue has become a more serious human health concern in China, with increased incidence and expanded outbreak regions. The knowledge of the cross-sectional and longitudinal epidemiological characteristics and the evolutionary dynamics of dengue in high-risk areas of China is limited.

Methods

Records of dengue cases from 2013 to 2016 were obtained from the China Notifiable Disease Surveillance System. Full envelope gene sequences of dengue viruses detected from the high-risk areas of China were collected. Maximum Likelihood tree and haplotype network analyses were conducted to explore the phylogenetic relationship of viruses from high-risk areas of China.

Results

A total of 56,520 cases was reported in China from 2013 to 2016. During this time, Yunnan, Guangdong and Fujian provinces were the high-risk areas. Imported cases occurred almost year-round, and were mainly introduced from Southeast Asia. The first indigenous case usually occurred in June to August, and the last one occurred before December in Yunnan and Fujian provinces but in December in Guangdong Province. Seven genotypes of DENV 1–3 were detected in the high-risk areas, with DENV 1-I the main genotype and DENV 2-Cosmopolitan the secondary one. The Maximum Likelihood trees show that almost all the indigenous viruses separated into different clusters. DENV 1-I viruses were found to be clustered in Guangdong Province, but not in Fujian and Yunnan, from 2013 to 2015. The ancestors of the Guangdong viruses in the cluster in 2013 and 2014 were most closely related to strains from Thailand or Singapore, and the Guangdong virus in 2015 was most closely related to the Guangdong virus of 2014. Based on closest phylogenetic relationships, viruses from Myanmar possibly initiated further indigenous cases in Yunnan, those from Indonesia in Fujian, while viruses from Thailand, Malaysia, Singapore and Indonesia were predominant in Guangdong Province.

Conclusions

Dengue is still an imported disease in China, although some genotypes continued to circulate in successive years. Viral phylogenies based on the envelope gene suggested periodic introductions of dengue strains into China, primarily from Southeast Asia, with occasional sustained, multi-year transmission in some regions of China.

Dengue is the most prevalent and rapidly spreading mosquito-borne viral disease globally. Because of the multiple introductions, dengue outbreaks occurred in epidemic seasons in Southern China, supported by suitable weather conditions. Surveillance data from 2013 to 2016 in China showed that Guangdong, Yunnan and Fujian provinces were the high-risk areas, with dengue outbreaks occurring almost every year. However, knowledge has been lacking of the epidemiological characteristics and the evolution pattern of dengue virus in these high-risk areas. This study shows a variety of epidemiological characteristics and sources of imported cases among the high-risk areas in China, with likely origins primarily from countries in Southeast Asia. Seven genotypes of the DENV 1–3 variety co-circulated with DENV1-I, the main genotype, and DENV 2-Cosmopolitan, the secondary. Genetic relationships among viral strains suggest that the indigenous viruses in the high-risk areas arose from imported viruses and sometimes persisted between years into the next epidemic season, especially in Guangdong Province. Population movement has played a vital role in dengue epidemics in China. This information may be useful in dengue control, especially during epidemic seasons and in the development of an early warning system within the region, in collaboration with bordering countries.

Complete genome characterization of the 2018 dengue outbreak in Hunan, an inland province in central South China

In 2018, a small-scale dengue epidemic broke out in Hunan Province, an inland province in central South China, with 172 people infected. To verify the causative agent, complete genome information was obtained by PCR and sequencing based on the viral RNAs extracted from patient serum samples. Mutation and evolutionary analysis were performed by MEGA7.0 software. The online softwares "Predict protein" and "Mfold" were used to predict the secondary structure of proteins and untranslated regions, respectively. Phylogenetic analysis showed that all five isolates in this study were DENV type 2, which is most closely related to the Zhejiang strain (2017-MH110588). Compared with the DENV-2 standard strain, 773 nucleotide mutations occurred in the isolated strain, of which 666 were nonsense mutations. Of the 80 mutated amino acids, 22 occurred in the structural protein region (2 in C region, 8 in PrM/M region, 12 in E region), and 58 in the non-structural (NS) protein region (9 in NS1 region, 10 in NS2 region, 12 in NS3 region, 7 in NS4 region, 20 in NS5 region). The prM/M region had the highest AA mutation rate, while NS4B was conservative. Three amino acid mutations (E: N390^thS, and NS5: S676^thN, K800^thT) may important for the replication and virulence of the DENV. Secondary structure prediction observed 28 changes in polynucleotide binding regions and 110 changes in protein binding sites of coding sequence. 2 and 4 base substitutions resulted in 2 and 6 significant changes in the RNA secondary structure of 5' UTR and 3' UTR, respectively. Two significant positive selection sites were observed in NS5. To our knowledge, this research is the first complete genome analysis of the epidemic strain of the 2018 dengue outbreak in Hunan and will benefit further research for virus traceability and vaccine development.

Kinetics of IgG Antibodies in Previous Cases of Dengue Fever-A Longitudinal Serological Survey

Guangzhou is believed to be the most important epicenter of dengue outbreaks in southern China. In this study, a longitudinal serological investigation of previous cases of dengue fever in Guangzhou was conducted to explore the persistence of IgG antibodies and related factors affecting the changes of antibody level. We recruited 70 dengue virus type 1 (DENV-1) primary infection cases at two years post infection for serological investigation and conducted a second follow-up in the 5th year of prognosis. An enzyme-linked immunosorbent assay (ELISA) for DENV IgG antibody was examined in all study subjects. Potential factors associated with the concentration of serum total IgG antibody were determined by the generalized estimation equation (GEE). No significant difference in serum total IgG antibody positive rate between two follow-ups was observed (χ² = 3.066, p = 0.080). However, there was a significant difference in the concentration of serum total IgG antibody between the two follow-ups (Z = 7.154, p < 0.001). The GEE showed that the antibody level in the five-year prognosis was mainly affected by the antibody level in the two-year prognosis (OR: 1.007, 95%CI: 1.005–1.009). In conclusion, the serum IgG antibodies of previous dengue fever cases can persist for a long time.

Effects of natural and socioeconomic factors on dengue transmission in two cities of China from 2006 to 2017

Dengue fever (DF) is a common and rapidly spreading vector-borne viral disease in tropical and subtropical regions. In recent years, in China, DF still poses an increasing threat to public health in many cities; but the incidence shows significant spatiotemporal differences. The purpose of this study was to identify the key factors affecting the spatial and temporal distribution of DF. We collected natural environmental and socio-economic data for two adjacent cities, Guangzhou (73 variables) and Foshan (71 variables), with the most DF cases in China. We performed random forest modelling to rank the factors according to their level of importance, and used negative binomial regression analysis to compare the risk factors between outbreak years and non-outbreak years. The natural environmental factors contributing to DF incidence for Guangzhou were temperature (relative risk (RR) = 18.80, 95% confidence interval (CI) = 3.11-113.67), humidity (RR = 1.85, 95% CI = 1.17-2.90) and green area (RR = 12.11, 95% CI = 6.14-55.50), and for Foshan was forest coverage (RR = 5.83, 95% CI = 2.72-12.45). Socio-economic impact were shown in Guangzhou with foreign visitor (RR = 1.18, 95% CI = 1.05-1.34) and oversea air passenger transport (RR = 7.34, 95% CI = 2.26-23.86); in Foshan, with oversea tourism (RR = 1.65, 95% CI = 1.34-2.04); and in Guangzhou-Foshan, with the development of intercity metro (RR = 1.26, 95% CI = 1.10-1.44). The difference between the two cities was the greater impact of the foreign visitor, green spaces and climate factor on DF in Guangzhou; the impact of the construction of intercity metro; and the more significant impact of oversea tourism on DF in Foshan. Our results suggest meaningful clues to public health authorities implementing joint interventions on DF prevention and early warning, to increase health education on DF prevention for international visitors and oversea travelers, and cross-city metro passengers; using rapid body temperature detection in public places such as airports, metros and parks can help detect cases early.

Construction and application of surveillance and response systems for parasitic diseases in China, led by NIPD-CTDR

Parasitic diseases have been widely epidemic in China with a long history. Great endeavours made in past 70 years led to significant decrease in morbidity and mortablity caused by several major parasitic diseases, while challenges existed to eliminate parasitic diseases. Surveillance-response system has play a crucial role in identifying public health problems, ascertaining the distribution and epidemic dynamics, discovering outbreaks and epidemic anomalies, evaluating the effects of on-site intervention activities and identifying risk factors. In this article, we reviewed the progress of the surveillance system for parasitic diseases, analysed the role of NIPD in the construction and application of surveillance-response system of parasitic diseases through elaborating the surveillance activities and typical surveillance-response events led by NIPD. Suggestion and comments for improve the surveillance-response system were put forward for further control or elimination of parasitic diseases.

Increase in cases of dengue in China, 2004-2016: A retrospective observational study

BACKGROUND

Dengue fever (DF) is a vector-bore infectious disease that can infect humans, and has been recognized as a global public health threat, with significant morbidity and mortality rates.

METHOD

To describe the epidemiological profile of DF in China during 2004-2016, the morbidity data of DF by age-group, season (different months) and geographic location (different provinces) were obtained from the public health science data center of China for subsequent epidemiological analysis.

RESULTS

The results showed that the incidence of DF shows striking annual variations, and two large outbreaks occurred in 2006-2007 and during 2012-2015. The results of the average morbidity rates (cases/100,000 population) for human DF indicated that among all dengue fever cases, Guangdong in southern area of China had the highest rates (3.8160 cases/100,000 population), followed by Yunnan (0.6614 cases/100,000 population), Fujian (0.3463 cases/100,000 population) and Guangxi (0.1474 cases/100,000 population). Epidemic peaks occurred in late June and early November, and the incidence rate among middle-aged people (30-45 years old) was relatively high, followed by rates among 15-29 and 45-59 age groups.

CONCLUSION

In this study, we demonstrated the epidemiological profile of DF circulating in China and revealed the geographic distribution, dynamic transmission, seasonal asymmetries and age distribution, which will provide guidelines on the prevention and control of DF in China. The present investigation is useful in the risk assessment of DF transmission, to predict DF outbreaks and the prevention and control strategies should be used along with surveillance to reduce the spread of DF in China.

Development of a mechanistic dengue simulation model for Guangzhou

Dengue infection in China has increased dramatically in recent years. Guangdong province (main city Guangzhou) accounted for more than 94% of all dengue cases in the 2014 outbreak. Currently, there is no existing effective vaccine and most efforts of control are focused on the vector itself. This study aimed to evaluate different dengue management strategies in a region where this disease is emerging. This work was done by establishing a dengue simulation model for Guangzhou to enable the testing of control strategies aimed at vector control and vaccination. For that purpose, the computer-based dengue simulation model (DENSiM) together with the Container-Inhabiting Mosquito Simulation Model (CIMSiM) has been used to create a working dengue simulation model for the city of Guangzhou. In order to achieve the best model fit against historical surveillance data, virus introduction scenarios were run and then matched against the actual dengue surveillance data. The simulation model was able to predict retrospective outbreaks with a sensitivity of 0.18 and a specificity of 0.98. This new parameterisation can now be used to evaluate the potential impact of different control strategies on dengue transmission in Guangzhou. The knowledge generated from this research would provide useful information for authorities regarding the historic patterns of dengue outbreaks, as well as the effectiveness of different disease management strategies.

Online Advanced Analytical Service: Profiles for Dengue Hemorrhagic Fever Transmission in Southern Thailand

BACKGROUND

Southern Thailand has the highest Dengue Hemorrhagic Fever (DHF) incidence and fatality rate in Thailand. Geographic Information Systems (GIS) technology and spatial analysis techniques are powerful tools to describe epidemiological patterns. The aim of this study was to develop an Online Advanced Analytical Service: Profiles for Dengue Hemorrhagic Fever Transmission (OSD) in Southern Thailand.

METHODS

The system was developed using JavaServer Pages (JSP) and Database Management System (DBMS) with Structured Query Language (SQL) technology as the web database tool for data entry and data access, web Mathematica technology for data analysis and Google Maps™ API technology for online data display as the map service implementing GIS technology.

RESULTS

The OSD system has been available online at URL http://www.s-cm.co/dengue . Users performed data entry using the web-service with login by social network (i.e. Facebook) account, used data analysis tools with online real-time statistical analysis and data display with transparent color circles overlaid on Google Maps™.

CONCLUSION

The OSD system display represents the distribution of DHF cases with spatial information. This system enables health planners to provide interventions for DHF focusing on prevention, control, and strategic planning.

Detecting space-time clusters of dengue fever in Panama after adjusting for vector surveillance data

Long term surveillance of vectors and arboviruses is an integral aspect of disease prevention and control systems in countries affected by increasing risk. Yet, little effort has been made to adjust space-time risk estimation by integrating disease case counts with vector surveillance data, which may result in inaccurate risk projection when several vector species are present, and when little is known about their likely role in local transmission. Here, we integrate 13 years of dengue case surveillance and associated Aedes occurrence data across 462 localities in 63 districts to estimate the risk of infection in the Republic of Panama. Our exploratory space-time modelling approach detected the presence of five clusters, which varied by duration, relative risk, and spatial extent after incorporating vector species as covariates. The Ae. aegypti model contained the highest number of districts with more dengue cases than would be expected given baseline population levels, followed by the model accounting for both Ae. aegypti and Ae. albopictus. This implies that arbovirus case surveillance coupled with entomological surveillance can affect cluster detection and risk estimation, potentially improving efforts to understand outbreak dynamics at national scales.

Dengue cases have increased in tropical regions worldwide owing to urbanization, globalization, and climate change facilitating the spread of Aedes mosquito vectors. National surveillance programs monitor trends in dengue fever and inform the public about epidemiological scenarios where outbreak preventive actions are most needed. Yet, most estimations of dengue risk so far derive only from disease case data, ignoring Aedes occurrence as a key aspect of dengue transmission dynamic. Here we illustrate how incorporating vector presence and absence as a model covariate can considerably alter the characteristics of space-time cluster estimations of dengue cases.

Spatial and temporal analysis of dengue infections in Queensland, Australia: Recent trend and perspectives

Dengue is a public health concern in northern Queensland, Australia. This study aimed to explore spatial and temporal characteristics of dengue cases in Queensland, and to identify high-risk areas after a 2009 dengue outbreak at fine spatial scale and thereby help in planning resource allocation for dengue control measures. Notifications of dengue cases for Queensland at Statistical Local Area (SLA) level were obtained from Queensland Health for the period 2010 to 2015. Spatial and temporal analysis was performed, including plotting of seasonal distribution and decomposition of cases, using regression models and creating choropleth maps of cumulative incidence. Both the space-time scan statistic (SaTScan) and Geographical Information System (GIS) were used to identify and visualise the space-time clusters of dengue cases at SLA level. A total of 1,773 dengue cases with 632 (35.65%) autochthonous cases and 1,141 (64.35%) overseas acquired cases were satisfied for the analysis in Queensland during the study period. Both autochthonous and overseas acquired cases occurred more frequently in autumn and showed a geographically expanding trend over the study period. The most likely cluster of autochthonous cases (Relative Risk, RR = 54.52, p<0.001) contained 50 SLAs in the north-east region of the state around Cairns occurred during 2013-2015. A cluster of overseas cases (RR of 60.81, p<0.001) occurred in a suburb of Brisbane during 2012 to 2013. These results show a clear spatiotemporal trend of recent dengue cases in Queensland, providing evidence in directing future investigations on risk factors of this disease and effective interventions in the high-risk areas.

20 Years Spatial-Temporal Analysis of Dengue Fever and Hemorrhagic Fever in Mexico

BACKGROUND AND AIM

Dengue Fever (DF) is a human vector-borne disease and a major public health problem worldwide. In Mexico, DF and Dengue Hemorrhagic Fever (DHF) cases have increased in recent years. The aim of this study was to identify variations in the spatial distribution of DF and DHF cases over time using space-time statistical analysis and geographic information systems.

METHODS

Official data of DF and DHF cases were obtained in 32 states from 1995-2015. Space-time scan statistics were used to determine the space-time clusters of DF and DHF cases nationwide, and a geographic information system was used to display the location of clusters.

RESULTS

A total of 885,748 DF cases was registered of which 13.4% (n = 119,174) correspond to DHF in the 32 states from 1995-2015. The most likely cluster of DF (relative risk = 25.5) contained the states of Jalisco, Colima, and Nayarit, on the Pacific coast in 2009, and the most likely cluster of DHF (relative risk = 8.5) was in the states of Chiapas, Tabasco, Campeche, Oaxaca, Veracruz, Quintana Roo, Yucatán, Puebla, Morelos, and Guerrero principally on the Gulf coast over 2006-2015.

CONCLUSION

The geographic distribution of DF and DHF cases has increased in recent years and cases are significantly clustered in two coastal areas (Pacific and Gulf of Mexico). This provides the basis for further investigation of risk factors as well as interventions in specific areas.

Urban villages as transfer stations for dengue fever epidemic: A case study in the Guangzhou, China

Background

Numerous urban villages (UVs) and frequent infectious disease outbreaks are major environmental and public health concerns in highly urbanized regions, especially in developing countries. However, the spatial and quantitative associations between UVs and infections remain little understood on a fine scale.

Methodology and principal findings

In this study, the relationships between reported dengue fever (DF) epidemics during 2012–2017, gross domestic product (GDP), the traffic system (road density, bus and/or subway stations), and UVs derived from high-resolution remotely sensed imagery in the central area of Guangzhou, were explored using geographically weighted regression (GWR) models based on a 1 km × 1 km grid scale. Accounting for 16.53%–18.07% of residential area and 16.84%–18.02% of population, UVs possessed 28.55%–38.24% of total reported DF cases in the core area of Guangzhou. The density of DF cases and the DF incidence rates in UVs were 1.81–3.13 and 1.82–3.06 times of that of normal construction land. Approximately 90% of the total cases were concentrated in the UVs and their buffering zones of radius ranged from 0 to 500 m. Significantly positive associations were observed between gridded DF incidence rates and UV area (r = 0.33, P = 0.000), the number of bus stops (r = 0.49, P = 0.000) and subway stations (r = 0.27, P = 0.000), and road density (r = 0.39, P = 0.000). About 60% of spatial variations in the gridded DF incidence rates were interpreted by the different variables of GDP, UVs, and bus stops integrated in GWR models.

Conclusions

UVs likely acted as special transfer stations, receiving and/or exporting DF cases during epidemics. This work increases our understanding of the influences of UVs on vector-borne diseases in highly urbanized areas, supplying valuable clues to local authorities making targeted interventions for the prevention and control of DF epidemics.

Due to the rapid urbanization of China, many villages in the urban fringe are enveloped by ever-expanding cities and become so-called urban villages (UVs). UVs are widely distributed in not only the Guangzhou core areas but also the other cities in the highly urbanized region of China (e.g., Shenzhen, Wuhan). UVs are commonly featured by poor sanitation, overcrowding population, absent infrastructure, and some environmental pollution due to the development is neither authorized nor planned, resulting in a high environmental suitability for some vectors (e.g., Aedes albopictus), as well as the vetor-borne diseases (i.e., dengue fever) in these regions. In this study, we demonstrated that UVs may serve as transfer stations for the transmission of DF epidemic in the regions with developed transportation, higher GDP and dense population. This is manifested as that the rates of DF incidences were significantly positively associated with UV area. Furthermore, the density of DF cases and the DF incidence rates in UVs were 1.81–3.13 and 1.82–3.06 times of that of normal construction land and about 90% of the total DF cases were concentrated in 500m radius of UVs’ buffers. And the aggregation effects of UVs on this epidemic in the central region were obviously affected by public traffic conditions at the grid level. This study is the first quantitative analysis of the spatial relationship between UVs, public transportation, road density, population density, GDP and DF epidemics, which will provide a useful reference for accurately preventing and controlling DF epidemic in urban regions with numerous UVs.

Effects of human mobility, temperature and mosquito control on the spatiotemporal transmission of dengue

Dengue transmission exhibits evident geographic variations and seasonal differences. Such heterogeneity is caused by various impact factors, in which temperature and host/vector behaviors could drive its spatiotemporal transmission, but mosquito control could stop its progression. These factors together contribute to the observed distributions of dengue incidence from surveillance systems. To effectively and efficiently monitor and response to dengue outbreak, it would be necessary to systematically model these factors and their impacts on dengue transmission. This paper introduces a new modeling framework with consideration of multi-scale factors and surveillance data to clarify the hidden dynamics accounting for dengue spatiotemporal transmission. The model is based on compartmental system which takes into account the biting-based interactions among humans, viruses and mosquitoes, as well as the essential impacts of human mobility, temperature and mosquito control. This framework was validated with real epidemic data by applying retrospectively to the 2014 dengue epidemic in the Pearl River Delta (PRD) in southern China. The results indicated that suitable condition of temperature could be responsible for the explosive dengue outbreak in the PRD, and human mobility could be the causal factor leading to its spatial transmission across different cities. It was further found that mosquito intervention has significantly reduced dengue incidence, where a total of 52,770 (95% confidence interval [CI]: 29,231-76,308) dengue cases were prevented in the PRD in 2014. The findings can offer new insights for improving the predictability and risk assessment of dengue epidemics. The model also can be readily extended to investigate the transmission dynamics of other mosquito-borne diseases.

Epidemiological profile of dengue in Zhejiang Province, southeast China

BACKGROUND

Dengue is one of the most important vector-bore infectious diseases in China because of its drastic increase in incidence, geographic extension and profound influence on China's economy. This study aims to retrospectively uncover the epidemiological profile of dengue in Zhejiang, one of the most developed provinces in China, and to find the problem existing in dengue control and prevention.

METHODOLOGY

Descriptive analyses on the dengue incidence and associated factors were performed. We also identified potential space-time cluster and generated the risk map of dengue.

PRINCIPAL FINDINGS

A total of 529 cases were reported in Zhejiang Province from 2005 to 2016, and 44.4% were imported. 67.7% of cases were 25~60 years old and the overall male-to-female sex ratio was 1.09:1. Dengue was reported all year round and 70.7% of cases occurred between August and October. Indigenous cases were only reported in the period between July to November and more than half occurred in September. Geographically, dengue was most distributed in Jinghua (3.62 per million), Shaoxing (1.00 per million) and Taizhou (0.81 per million) prefecture level cities. Outbreaks were confirmed in Yiwu, Keqiao and Huangyan counties in 2009, 2015, and 2016, respectively. 73.9% cases would seek medical advice within two days after onset and be confirmed within 9 days after onset. 75.6% would be recognized as dengue within 8 days after their first visit. The time intervals between onset and confirmation (median 7 vs 6 days; Wilcoxon rank sum test Z = -2.40, P = 0.016), first visit and confirmation (median 7 vs 6 days; Wilcoxon rank sum test Z = -2.59, P = 0.009) of indigenous cases were significantly longer than those of imported ones. However, the time intervals between onset and first visit for indigenous cases was shorter (median 0 vs 1 days; Wilcoxon rank sum test Z = -2.10, P = 0.036). Fever (99.1%), fatigue (81.9), rash (63.7%), headache (67.2%) and myalgia (52.60%) were the most frequently mentioned symptoms.

CONCLUSIONS

Zhejiang has recently witnessed an increase in incidence and geographic extension of dengue. Timely diagnosis is important to stop local transmission and outbreak.

Spatiotemporal patterns and determinants of dengue at county level in China from 2005-2017

OBJECTIVE

To identify the high risk spatiotemporal clusters of dengue cases and explore the associated risk factors.

METHODS

Monthly indigenous dengue cases in 2005-2017 were aggregated at county level. Spatiotemporal cluster analysis was used to explore dengue distribution features using SaTScan9.4.4 and Arcgis10.3.0. In addition, the influential factors and potential high risk areas of dengue outbreaks were analyzed using ecological niche models in Maxent 3.3.1 software.

RESULTS

We found a heterogeneous spatial and temporal distribution pattern of dengue cases. The identified high risk region in the primary cluster covered 13 counties in Guangdong Province and in the secondary clusters included 14 counties in Yunnan Province. Additionally, there was a nonlinear association between meteorological and environmental factors and dengue outbreaks, with 8.5%-57.1%, 6.7%-38.3% and 3.2%-40.4% contribution from annual average minimum temperature, land cover and annual average precipitation, respectively.

CONCLUSIONS

The high risk areas of dengue outbreaks mainly are located in Guangdong and Yunnan Provinces, which are significantly shaped by environmental and meteorological factors, such as temperature, precipitation and land cover.

Space-time clusters and co-occurrence of chikungunya and dengue fever in Colombia from 2015 to 2016

Vector-borne diseases (VBDs) infect over one billion people and are responsible for over one million deaths each year, globally. Chikungunya (CHIK) and Dengue Fever (DENF) are emerging VBDs due to overpopulation, increases in urbanization, climate change, and other factors. Colombia has recently experienced severe outbreaks of CHIK AND DENF. Both viruses are transmitted by the Aedes mosquitoes and are preventable with a variety of surveillance and vector control measures (e.g. insecticides, reduction of open containers, etc.). Spatiotemporal statistics can facilitate the surveillance of VBD outbreaks by informing public health officials where to allocate resources to mitigate future outbreaks. We utilize the univariate Kulldorff space-time scan statistic (STSS) to identify and compare statistically significant space-time clusters of CHIK and DENF in Colombia during the outbreaks of 2015 and 2016. We also utilize the multivariate STSS to examine co-occurrences (simultaneous excess incidences) of DENF and CHIK, which is critical to identify regions that may have experienced the greatest burden of VBDs. The relative risk of CHIK and DENF for each Colombian municipality belonging to a univariate and multivariate cluster is reported to facilitate targeted interventions. Finally, we visualize the results in a three-dimensional environment to examine the size and duration of the clusters. Our approach is the first of its kind to examine multiple VBDs in Colombia simultaneously, while the 3D visualizations are a novel way of illustrating the dynamics of space-time clusters of disease.

Spatiotemporal patterns of hemorrhagic fever with renal syndrome in Hebei Province, China, 2001-2016

Hemorrhagic fever with renal syndrome (HFRS) is highly endemic in China, where approximately 90% of the total human cases in the world are reported. The Hebei province, one of areas with the highest prevalence, has reported HFRS cases every year in the last two decades. This study describes the spatiotemporal patterns of HFRS in the Hebei province from 2001 to 2016, detects the high-risk spatiotemporal clusters of HFRS, and provides valuable information for planning and implementation of local preventive measures. For the purpose of the analysis, HFRS cases recorded during the sixteen years in the Hebei province were aggregated into three temporal periods (2001-2006, 2007-2012, and 2013-2016). Spatiotemporal analyses, including Global spatial autocorrelation analysis and Kulldorff's scan statistical analysis, were applied to analyze te spatiotemporal clusters of HFRS at the county level. The results revealed that the spatial extent of the HFRS epidemic in the Hebei province changed dynamically from 2001 to 2016, which indicated that a comprehensive preventative strategy should be implemented in the northeastern regions of the Hebei province in spring.

Spatiotemporal patterns of severe fever with thrombocytopenia syndrome in China, 2011-2016

Severe fever with thrombocytopenia syndrome (SFTS) is emerging and the number of SFTS cases have increased year by year in China. However, spatiotemporal patterns and trends of SFTS are less clear up to date. In order to explore spatiotemporal patterns and predict SFTS incidences, we analyzed temporal trends of SFTS using autoregressive integrated moving average (ARIMA) model, spatial patterns, and spatiotemporal clusters of SFTS cases at the county level based on SFTS data in China during 2011-2016. We determined the optimal time series model was ARIMA (2, 0, 1) × (0, 0, 1)₁₂ which fitted the SFTS cases reasonably well during the training process and forecast process. In the spatial clustering analysis, the global autocorrelation suggested that SFTS cases were not of random distribution. Local spatial autocorrelation analysis of SFTS identified foci mainly concentrated in Hubei Province, Henan Province, Anhui Province, Shandong Province, Liaoning Province, and Zhejiang Province. A most likely cluster including 21 counties in Henan Province and Hubei Province was observed in the central region of China from April 2015 to August 2016. Our results will provide a sound evidence base for future prevention and control programs of SFTS such as allocation of the health resources, surveillance in high-risk regions, health education, improvement of diagnosis and so on.

Dynamic spatiotemporal analysis of indigenous dengue fever at street-level in Guangzhou city, China

BACKGROUND

This study aimed to investigate the spatiotemporal clustering and socio-environmental factors associated with dengue fever (DF) incidence rates at street level in Guangzhou city, China.

METHODS

Spatiotemporal scan technique was applied to identify the high risk region of DF. Multiple regression model was used to identify the socio-environmental factors associated with DF infection. A Poisson regression model was employed to examine the spatiotemporal patterns in the spread of DF.

RESULTS

Spatial clusters of DF were primarily concentrated at the southwest part of Guangzhou city. Age group (65+ years) (Odd Ratio (OR) = 1.49, 95% Confidence Interval (CI) = 1.13 to 2.03), floating population (OR = 1.09, 95% CI = 1.05 to 1.15), low-education (OR = 1.08, 95% CI = 1.01 to 1.16) and non-agriculture (OR = 1.07, 95% CI = 1.03 to 1.11) were associated with DF transmission. Poisson regression results indicated that changes in DF incidence rates were significantly associated with longitude (β = -5.08, P<0.01) and latitude (β = -1.99, P<0.01).

CONCLUSIONS

The study demonstrated that social-environmental factors may play an important role in DF transmission in Guangzhou. As geographic range of notified DF has significantly expanded over recent years, an early warning systems based on spatiotemporal model with socio-environmental is urgently needed to improve the effectiveness and efficiency of dengue control and prevention.

Spatiotemporal-based clusters as a method for dengue surveillance

Objectives.

To develop and demonstrate the use of a new method for epidemiological surveillance of dengue.

Methods.

This was a retrospective cohort study using data from the Health Department of São José do Rio Preto (São Paulo, Brazil). The geographical coordinates were obtained using QGIS™ (Creative Commons Corporation, Mountain View, California, United States), based on patient addresses in the dengue notification system of the Government of Brazil. SaTScan™ (Martin Kulldorff, Boston, Massachusetts, United States) was then used to create a space-time scan analysis to find statistically significant clusters of dengue. These results were plotted and visualized using Google Earth™ mapping service (Google Incorporated, Mountain View, California, United States).

Results.

More clusters were detected when the maximum number of households per cluster was set to 10% (11 statistically significant clusters) rather than 50% (8 statistically significant clusters). The cluster radius varied from 0.18 – 2.04 km and the period of time varied from 6 days – 6 months. The infection rate was more than 0.5 cases/household.

Conclusions.

When using SaTScan for space-time analysis of dengue cases, the maximum number of households per cluster should be set to 10%. This methodology may be useful to optimizing dengue surveillance systems, especially in countries where resources are scarce and government programs have not had much success controlling the disease.

Early rigorous control interventions can largely reduce dengue outbreak magnitude: experience from Chaozhou, China

Background

Dengue fever is a severe public heath challenge in south China. A dengue outbreak was reported in Chaozhou city, China in 2015. Intensified interventions were implemented by the government to control the epidemic. However, it is still unknown the degree to which intensified control measures reduced the size of the epidemics, and when should such measures be initiated to reduce the risk of large dengue outbreaks developing?

Methods

We selected Xiangqiao district as study setting because the majority of the indigenous cases (90.6%) in Chaozhou city were from this district. The numbers of daily indigenous dengue cases in 2015 were collected through the national infectious diseases and vectors surveillance system, and daily Breteau Index (BI) data were reported by local public health department. We used a compartmental dynamic SEIR (Susceptible, Exposed, Infected and Removed) model to assess the effectiveness of control interventions, and evaluate the control effect of intervention timing on dengue epidemic.

Results

A total of 1250 indigenous dengue cases was reported from Xiangqiao district. The results of SEIR modeling using BI as an indicator of actual control interventions showed a total of 1255 dengue cases, which is close to the reported number (n = 1250). The size and duration of the outbreak were highly sensitive to the intensity and timing of interventions. The more rigorous and earlier the control interventions implemented, the more effective it yielded. Even if the interventions were initiated several weeks after the onset of the dengue outbreak, the interventions were shown to greatly impact the prevalence and duration of dengue outbreak.

Conclusions

This study suggests that early implementation of rigorous dengue interventions can effectively reduce the epidemic size and shorten the epidemic duration.

Space and space-time distributions of dengue in a hyper-endemic urban space: the case of Girardot, Colombia

Background

Dengue is a widely spread vector-borne disease. Dengue cases in the Americas have increased over the last few decades, affecting various urban spaces throughout these continents, including the tourism-oriented city of Girardot, Colombia. Interactions among mosquitoes, pathogens and humans have recently been examined using different temporal and spatial scales in attempts to determine the roles that social and ecological systems play in dengue transmission. The current work characterizes the spatial and temporal behaviours of dengue in Girardot and discusses the potential territorial dynamics related to the distribution of this disease.

Methods

Based on officially reported dengue cases (2012–2015) corresponding to epidemic (2013) and inter-epidemic years (2012, 2014, 2015), space (Getis-Ord index) and space-time (Kulldorff’s scan statistics) analyses were performed.

Results

Geocoded dengue cases (n = 2027) were slightly overrepresented by men (52.1%). As expected, the cases were concentrated in the 0- to 15-year-old age group according to the actual trends of Colombia. The incidence rates of dengue during the rainy and dry seasons as well as those for individual years (2012, 2013 and 2014) were significant using the global Getis-Ord index. Local clusters shifted across seasons and years; nevertheless, the incidence rates clustered towards the southwest region of the city under different residential conditions. Space-time clusters shifted from the northeast to the southwest of the city (2012–2014). These clusters represented only 4.25% of the total cases over the same period (n = 1623). A general trend was observed, in which dengue cases increased during the dry seasons, especially between December and February.

Conclusions

Despite study limitations related to official dengue records and available fine-scale demographic information, the spatial analysis results were promising from a geography of health perspective. Dengue did not show linear association with poverty or with vulnerable peripheral spaces in intra-urban settings, supporting the idea that the pathogenic complex of dengue is driven by different factors. A coordinated collaboration of epidemiological, public health and social science expertise is needed to assess the effect of “place” from a relational perspective in which geography has an important role to play.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-017-2610-7) contains supplementary material, which is available to authorized users.

Ecological Niche Modeling Identifies Fine-Scale Areas at High Risk of Dengue Fever in the Pearl River Delta, China

Dengue fever (DF) is one of the most common and rapidly spreading mosquito-borne viral diseases in tropical and subtropical regions. In recent years, this imported disease has posed a serious threat to public health in China, especially in the Pearl River Delta (PRD). Although the severity of DF outbreaks in the PRD is generally associated with known risk factors, fine scale assessments of areas at high risk for DF outbreaks are limited. We built five ecological niche models to identify such areas including a variety of climatic, environmental, and socioeconomic variables, as well as, in some models, extracted principal components. All the models we tested accurately identified the risk of DF, the area under the receiver operating characteristic curve (AUC) were greater than 0.8, but the model using all original variables was the most accurate (AUC = 0.906). Socioeconomic variables had a greater impact on this model (total contribution 55.27%) than climatic and environmental variables (total contribution 44.93%). We found the highest risk of DF outbreaks on the border of Guangzhou and Foshan (in the central PRD), and in northern Zhongshan (in the southern PRD). Our fine-scale results may help health agencies to focus epidemic monitoring tightly on the areas at highest risk of DF outbreaks.

Association between dengue fever incidence and meteorological factors in Guangzhou, China, 2005-2014

This study aims to (1) investigate the associations between climatic factors and dengue; and (2) identify the susceptible subgroups. De-identified daily dengue cases in Guangzhou for 2005-2014 were obtained from the Chinese Center for Disease Control and Prevention. Weather data were downloaded from the China Meteorological Data Sharing Service System. Distributed lag non-linear models (DLNM) were used to graphically demonstrate the three-dimensional temperature-dengue association. Generalised estimating equation models (GEE) with piecewise linear spline functions were used to quantify the temperature-dengue associations. Threshold values were estimated using a broken-stick model. Middle-aged and older people, people undertaking household duties, retirees, and those unemployed were at high risk of dengue. Reversed U-shaped non-linear associations were found between ambient temperature, relative humidity, extreme wind velocity, and dengue. The optimal maximum temperature (T_max) range for dengue transmission in Guangzhou was 21.6-32.9°C, and 11.2-23.7°C for minimum temperature (T_min). A 1°C increase of T_max and T_min within these ranges was associated with 11.9% and 9.9% increase in dengue at lag0, respectively. Although lag effects of temperature were observed for up to 141 days for T_max and 150 days for T_min, the maximum lag effects were observed at 32 days and 39 days respectively. Average relative humidity was negatively associated with dengue when it exceeded 78.9%. Maximum wind velocity (>10.7m/s) inhibited dengue transmission. Climatic factors had significant impacts on dengue in Guangzhou. Lag effects of temperature on dengue lasted the local whole epidemic season. To reduce the likely increasing dengue burden, more efforts are needed to strengthen the capacity building of public health systems.

Analysis of heterogeneous dengue transmission in Guangdong in 2014 with multivariate time series model

Guangdong experienced the largest dengue epidemic in recent history. In 2014, the number of dengue cases was the highest in the previous 10 years and comprised more than 90% of all cases. In order to analyze heterogeneous transmission of dengue, a multivariate time series model decomposing dengue risk additively into endemic, autoregressive and spatiotemporal components was used to model dengue transmission. Moreover, random effects were introduced in the model to deal with heterogeneous dengue transmission and incidence levels and power law approach was embedded into the model to account for spatial interaction. There was little spatial variation in the autoregressive component. In contrast, for the endemic component, there was a pronounced heterogeneity between the Pearl River Delta area and the remaining districts. For the spatiotemporal component, there was considerable heterogeneity across districts with highest values in some western and eastern department. The results showed that the patterns driving dengue transmission were found by using clustering analysis. And endemic component contribution seems to be important in the Pearl River Delta area, where the incidence is high (95 per 100,000), while areas with relatively low incidence (4 per 100,000) are highly dependent on spatiotemporal spread and local autoregression.

Estimating Dengue Transmission Intensity from Case-Notification Data from Multiple Countries

BACKGROUND

Despite being the most widely distributed mosquito-borne viral infection, estimates of dengue transmission intensity and associated burden remain ambiguous. With advances in the development of novel control measures, obtaining robust estimates of average dengue transmission intensity is key for assessing the burden of disease and the likely impact of interventions.

METHODOLOGY/PRINCIPLE FINDINGS

We estimated the force of infection (λ) and corresponding basic reproduction numbers (R0) by fitting catalytic models to age-stratified incidence data identified from the literature. We compared estimates derived from incidence and seroprevalence data and assessed the level of under-reporting of dengue disease. In addition, we estimated the relative contribution of primary to quaternary infections to the observed burden of dengue disease incidence. The majority of R0 estimates ranged from one to five and the force of infection estimates from incidence data were consistent with those previously estimated from seroprevalence data. The baseline reporting rate (or the probability of detecting a secondary infection) was generally low (<25%) and varied within and between countries.

CONCLUSIONS/SIGNIFICANCE

As expected, estimates varied widely across and within countries, highlighting the spatio-temporally heterogeneous nature of dengue transmission. Although seroprevalence data provide the maximum information, the incidence models presented in this paper provide a method for estimating dengue transmission intensity from age-stratified incidence data, which will be an important consideration in areas where seroprevalence data are not available.

Perceptions of capacity for infectious disease control and prevention to meet the challenges of dengue fever in the face of climate change: A survey among CDC staff in Guangdong Province, China

BACKGROUND

Dengue fever is an important climate-sensitive mosquito-borne viral disease that poses a risk to half the world's population. The disease is a major public health issue in China where in 2014 a major outbreak occurred in Guangdong Province. This study aims to gauge health professionals' perceptions about the capacity of infectious disease control and prevention to meet the challenge of dengue fever in the face of climate change in Guangdong Province, China.

METHODS

A cross-sectional questionnaire survey was administered among staff in the Centers for Disease Control and Prevention (CDCs) in Guangdong Province. Data analysis was undertaken using descriptive methods and logistic regression.

RESULTS

In total, 260 questionnaires were completed. Most participants (80.7%) thought climate change would have a negative effect on population health, and 98.4% of participants reported dengue fever had emerged or re-emerged in China in recent years. Additionally, 74.9% of them indicated that the capability of the CDCs to detect infectious disease outbreak/epidemic at an early stage was excellent; 86.3% indicated laboratories could provide diagnostic support rapidly; and 83.1% believed levels of current staff would be adequate in the event of a major outbreak. Logistic regression analysis showed higher levels of CDCs were perceived to have better capacity for infectious disease control and prevention. Only 26.8% of participants thought they had a good understanding of climate change, and most (85.4%) thought they needed more information about the health impacts of climate change. Most surveyed staff suggested the following strategies to curb the public health impact of infectious diseases in relation to climate change: primary prevention measures, strengthening the monitoring of infectious diseases, the ability to actively forecast disease outbreaks by early warning systems, and more funding for public health education programs.

CONCLUSION

Vigilant disease and vector surveillance, preventive practice and health promotion programs will likely be significant in addressing the threat of dengue fever in the future. Further efforts are needed to strengthen the awareness of climate change among health professionals, and to promote relevant actions to minimize the health burden of infectious diseases in a changing climate. Results will be critical for policy makers facing the current and future challenges associated with infectious disease prevention and control in China.

Characterizing a large outbreak of dengue fever in Guangdong Province, China

BACKGROUND

Dengue cases have been reported each year for the past 25 years in Guangdong Province, China with a recorded historical peak in 2014. This study aims to describe the epidemiological characteristics of this large outbreak in order to better understand its epidemic factors and to inform control strategies.

METHODS

Data for clinically diagnosed and laboratory-confirmed dengue fever cases in 2014 were extracted from the China Notifiable Infectious Disease Reporting System. We analyzed the incidence and characteristics of imported and indigenous cases in terms of population, temporal and spatial distributions.

RESULTS

A total of 45 224 dengue fever cases and 6 deaths were notified in Guangdong Province in 2014, with an incidence of 47.3 per 100 000 people. The elderly (65+ years) represented 11.7 % of total indigenous cases with the highest incidence (72.3 per 100 000). Household workers and the unemployed accounted for 23.1 % of indigenous cases. The majority of indigenous cases occurred in the 37(th) to 44(th) week of 2014 (September and October) and almost all (20 of 21) prefecture-level cities in Guangdong were affected. Compared to the non-Pearl River Delta Region, the Pearl River Delta Region accounted for the majority of dengue cases and reported cases earlier in 2014. Dengue virus serotypes 1 (DENV-1), 2 (DENV-2) and 3 (DENV-3) were detected and DENV-1 was predominant (88.4 %).

CONCLUSIONS

Dengue fever is a serious public health problem and is emerging as a continuous threat in Guangdong Province. There is an urgent need to enhance dengue surveillance and control, especially for the high-risk populations in high-risk areas.

Inferring the Spatio-temporal Patterns of Dengue Transmission from Surveillance Data in Guangzhou, China

Background

Dengue is a serious vector-borne disease, and incidence rates have significantly increased during the past few years, particularly in 2014 in Guangzhou. The current situation is more complicated, due to various factors such as climate warming, urbanization, population increase, and human mobility. The purpose of this study is to detect dengue transmission patterns and identify the disease dispersion dynamics in Guangzhou, China.

Methodology

We conducted surveys in 12 districts of Guangzhou, and collected daily data of Breteau index (BI) and reported cases between September and November 2014 from the public health authority reports. Based on the available data and the Ross-Macdonald theory, we propose a dengue transmission model that systematically integrates entomologic, demographic, and environmental information. In this model, we use (1) BI data and geographic variables to evaluate the spatial heterogeneities of Aedes mosquitoes, (2) a radiation model to simulate the daily mobility of humans, and (3) a Markov chain Monte Carlo (MCMC) method to estimate the model parameters.

Results/Conclusions

By implementing our proposed model, we can (1) estimate the incidence rates of dengue, and trace the infection time and locations, (2) assess risk factors and evaluate the infection threat in a city, and (3) evaluate the primary diffusion process in different districts. From the results, we can see that dengue infections exhibited a spatial and temporal variation during 2014 in Guangzhou. We find that urbanization, vector activities, and human behavior play significant roles in shaping the dengue outbreak and the patterns of its spread. This study offers useful information on dengue dynamics, which can help policy makers improve control and prevention measures.

Dengue transmission is a spatio-temporal process with interactions between hosts, vectors, and viruses. Its transmission also involves multiple complex or even hidden factors, such as climate, social environment, vector ecology, and host mobility. These complexities make the underlying process of dengue transmission difficult to clarify. We address how the patterns of dengue transmission can be inferred by investigating the 2014 dengue outbreak in the city of Guangzhou, China, taking the available surveillance data and applying mathematical models and computational methods. We can then estimate the distribution of dengue infections and identify the transmission mechanisms. In our study, we systematically investigate the critical factors, enabling us to estimate the real patterns and dynamics of dengue transmission beyond the surveillance data.

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.

The distinct distribution and phylogenetic characteristics of dengue virus serotypes/genotypes during the 2013 outbreak in Yunnan, China: Phylogenetic characteristics of 2013 dengue outbreak in Yunnan, China

Since 2000, sporadic imported cases of dengue fever were documented almost every year in Yunnan Province, China. Unexpectedly, a large-scale outbreak of dengue virus (DENV) infection occurred from August to December 2013, with 1538 documented cases. In the current study, 81 dengue-positive patient samples were collected from Xishuangbanna, the southernmost prefecture of the Yunnan province, and 23 from Dehong, the westernmost prefecture of the Yunnan province. The full-length envelope genes were amplified and sequenced. Phylogenetic analysis revealed that nine strains (39.1%) and 14 strains (60.9%) from the Dehong prefecture were classified as genotype I of DENV-1 and Asian I genotype of DENV-2, respectively. All strains from Xishuangbanna were identified as genotype II of DENV-3. Bayesian coalescent analysis indicates that the outbreak originated from bordering southeastern Asian countries. These three epidemic genotypes were predicted to originate in Thailand and then migrate into Yunnan through different routes.

The Effects of Socioeconomic and Environmental Factors on the Incidence of Dengue Fever in the Pearl River Delta, China, 2013

Background

An outbreak of dengue fever (DF) occurred in Guangdong Province, China in 2013 with the highest number of cases observed within the preceding ten years. DF cases were clustered in the Pearl River Delta economic zone (PRD) in Guangdong Province, which accounted for 99.6% of all cases in Guangdong province in 2013. The main vector in PRD was Aedes albopictus. We investigated the socioeconomic and environmental factors at the township level and explored how the independent variables jointly affect the DF epidemic in the PRD.

Methodology/Principal Findings

Six factors associated with the incidence of DF were identified in this project, representing the urbanization, poverty, accessibility and vegetation, and were considered to be core contributors to the occurrence of DF from the perspective of the social economy and the environment. Analyses were performed with Generalized Additive Models (GAM) to fit parametric and non-parametric functions to the relationships between the response and predictors. We used a spline-smooth technique and plotted the predicted against the observed co-variable value. The distribution of DF cases was over-dispersed and fit the negative binomial function better. The effects of all six socioeconomic and environmental variables were found to be significant at the 0.001 level and the model explained 45.1% of the deviance by DF incidence. There was a higher risk of DF infection among people living at the prefectural boundary or in the urban areas than among those living in other areas in the PRD. The relative risk of living at the prefectural boundary was higher than that of living in the urban areas. The associations between the DF cases and population density, GDP per capita, road density, and NDVI were nonlinear. In general, higher “road density” or lower “GDP per capita” were considered to be consistent risk factors. Moreover, higher or lower values of “population density” and “NDVI” could result in an increase in DF cases.

Conclusion

In this study, we presented an effect analysis of socioeconomic and environmental factors on DF occurrence at the smallest administrative unit (township level) for the first time in China. GAM was used to effectively detect the nonlinear impact of the predictors on the outcome. The results showed that the relative importance of different risk factors may vary across the PRD. This work improves our understanding of the differences and effects of socioeconomic and environmental factors on DF and supports effectively targeted prevention and control measures.

Dengue fever is an infectious disease transmitted by mosquitoes. It is a major public health problem in tropical and subtropical regions around the world. Dengue fever is of great interest in the Pearl River Delta economic zone (PRD) of Guangdong province, China because the outbreak in 2013 was the largest in the previous 10 years. Due to the low degree of diversity in the climatic conditions in the PRD, socioeconomic and environmental factors may be the major contributing factors. The objective of this paper was to perform an assessment and detect the socioeconomic and environmental impact on cases at the smallest administrative unit (the township level). Six factors were identified in this work, representing urbanization, poverty, accessibility and vegetation. The effects of all these factors were found to be significant. The results showed that the relative importance of different risk factors may vary across the PRD. The higher risk areas and vulnerable populations identified in this paper will provide guidance for public health practitioners to create targeted, strategic plans and implement effective public health prevention and control measures.

Covering the Last Kilometer: Using GIS to Scale-Up Voluntary Medical Male Circumcision Services in Iringa and Njombe Regions, Tanzania

Interactive GIS maps created by overlapping facility data including roads and infrastructure with population and service delivery data permitted strategic deployment of mobile voluntary medical male circumcision (VMMC) services to underserved rural communities. The percentage of VMMCs performed in rural areas jumped from 48% in 2011 to 93% in 2014.

Background:

Based on the established protective effect of voluntary medical male circumcision (VMMC) in reducing female-to-male HIV transmission, Tanzania's Ministry of Health and Social Welfare (MOHSW) embarked on the scale-up of VMMC services in 2009. The Maternal and Child Health Integrated Project (MCHIP) supported the MOHSW to roll out VMMC services in Iringa and Njombe, 2 regions of Tanzania with among the highest HIV and lowest circumcision prevalence. With ambitious targets of reaching 264,990 males aged 10–34 years with VMMC in 5 years, efficient and innovative program approaches were necessary.

Program Description:

Outreach campaigns, in which mobile teams set up temporary services in facilities or non-facility settings, are used to reach lesser-served areas with VMMC. In 2012, MCHIP began using geographic information systems (GIS) to strategically plan the location of outreach campaigns. MCHIP gathered geocoded data on variables such as roads, road conditions, catchment population, staffing, and infrastructure for every health facility in Iringa and Njombe. These data were uploaded to a central database and overlaid with various demographic and service delivery data in order to identify the VMMC needs of the 2 regions.

Findings:

MCHIP used the interactive digital maps as decision-making tools to extend mobile VMMC outreach to “the last kilometer.” As of September 2014, the MOHSW with MCHIP support provided VMMC to 267,917 men, 259,144 of whom were men were aged 10–34 years, an achievement of 98% of the target of eligible males in Iringa and Njombe. The project reached substantially more men through rural dispensaries and non-health care facilities each successive year after GIS was introduced in 2012, jumping from 48% of VMMCs performed in rural areas in fiscal year 2011 to 88% in fiscal year 2012 and to 93% by the end of the project in 2014.

Conclusion:

GIS was an effective tool for making strategic decisions about where to prioritize VMMC service delivery, particularly for mobile and outreach services. Donors may want to consider funding mapping initiatives that support numerous interventions across implementing partners to spread initial start-up costs.

Spatiotemporal Clustering Analysis and Risk Assessments of Human Cutaneous Anthrax in China, 2005-2012

Objective

To investigate the epidemic characteristics of human cutaneous anthrax (CA) in China, detect the spatiotemporal clusters at the county level for preemptive public health interventions, and evaluate the differences in the epidemiological characteristics within and outside clusters.

Methods

CA cases reported during 2005–2012 from the national surveillance system were evaluated at the county level using space-time scan statistic. Comparative analysis of the epidemic characteristics within and outside identified clusters was performed using using the χ² test or Kruskal-Wallis test.

Results

The group of 30–39 years had the highest incidence of CA, and the fatality rate increased with age, with persons ≥70 years showing a fatality rate of 4.04%. Seasonality analysis showed that most of CA cases occurred between May/June and September/October of each year. The primary spatiotemporal cluster contained 19 counties from June 2006 to May 2010, and it was mainly located straddling the borders of Sichuan, Gansu, and Qinghai provinces. In these high-risk areas, CA cases were predominantly found among younger, local, males, shepherds, who were living on agriculture and stockbreeding and characterized with high morbidity, low mortality and a shorter period from illness onset to diagnosis.

Conclusion

CA was geographically and persistently clustered in the Southwestern China during 2005–2012, with notable differences in the epidemic characteristics within and outside spatiotemporal clusters; this demonstrates the necessity for CA interventions such as enhanced surveillance, health education, mandatory and standard decontamination or disinfection procedures to be geographically targeted to the areas identified in this study.

The changing epidemiology of dengue in China, 1990-2014: a descriptive analysis of 25 years of nationwide surveillance data

BACKGROUND

Dengue has been a notifiable disease in China since 1 September 1989. Cases have been reported each year during the past 25 years of dramatic socio-economic changes in China, and reached a historical high in 2014. This study describes the changing epidemiology of dengue in China during this period, to identify high-risk areas and seasons and to inform dengue prevention and control activities.

METHODS

We describe the incidence and distribution of dengue in mainland China using notifiable surveillance data from 1990-2014, which includes classification of imported and indigenous cases from 2005-2014.

RESULTS

From 1990-2014, 69,321 cases of dengue including 11 deaths were reported in mainland China, equating to 2.2 cases per one million residents. The highest number was recorded in 2014 (47,056 cases). The number of provinces affected has increased, from a median of three provinces per year (range: 1 to 5 provinces) during 1990-2000 to a median of 14.5 provinces per year (range: 5 to 26 provinces) during 2001-2014. During 2005-2014, imported cases were reported almost every month and 28 provinces (90.3%) were affected. However, 99.8% of indigenous cases occurred between July and November. The regions reporting indigenous cases have expanded from the coastal provinces of southern China and provinces adjacent to Southeast Asia to the central part of China. Dengue virus serotypes 1, 2, 3, and 4 were all detected from 2009-2014.

CONCLUSIONS

In China, the area affected by dengue has expanded since 2000 and the incidence has increased steadily since 2012, for both imported and indigenous dengue. Surveillance and control strategies should be adjusted to account for these changes, and further research should explore the drivers of these trends.

Spatial and temporal patterns of dengue in Guangdong province of China

The aim of the study was to describe the spatial and temporal patterns of dengue in Guangdong for 1978 to 2010. Time series analysis was performed using data on annual dengue incidence in Guangdong province for 1978-2010. Annual average dengue incidences for each city were mapped for 4 periods by using the geographical information system (GIS). Hot spot analysis was used to identify spatial patterns of dengue cases for 2005-2010 by using the CrimeStat III software. The incidence of dengue in Guangdong province had fallen steadily from 1978 to 2010. The time series was a random sequence without regularity and with no fixed cycle. The geographic range of dengue fever had expanded from 1978 to 2010. Cases were mostly concentrated in Zhanjiang and the developed regions of Pearl River Delta and Shantou.

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.

Spatiotemporal characterizations of dengue virus in mainland China: insights into the whole genome from 1978 to 2011

Temporal-Spatial of dengue virus (DENV) analyses have been performed in previous epidemiological studies in mainland China, but few studies have examined the whole genome of the DENV. Herein, 40 whole genome sequences of DENVs isolated from mainland China were downloaded from GenBank. Phylogenetic analyses and evolutionary distances of the dengue serotypes 1 and 2 were calculated using 14 maximum likelihood trees created from individual genes and whole genome. Amino acid variations were also analyzed in the 40 sequences that included dengue serotypes 1, 2, 3 and 4, and they were grouped according to temporal and spatial differences. The results showed that none of the phylogenetic trees created from each individual gene were similar to the trees created using the complete genome and the evolutionary distances were variable with each individual gene. The number of amino acid variations was significantly different (p = 0.015) between DENV-1 and DENV-2 after 2001; seven mutations, the N290D, L402F and A473T mutations in the E gene region and the R101K, G105R, D340E and L349M mutations in the NS1 region of DENV-1, had significant substitutions, compared to the amino acids of DENV-2. Based on the spatial distribution using Guangzhou, including Foshan, as the indigenous area and the other regions as expanding areas, significant differences in the number of amino acid variations in the NS3 (p = 0.03) and NS1 (p = 0.024) regions and the NS2B (p = 0.016) and NS3 (p = 0.042) regions were found in DENV-1 and DENV-2. Recombination analysis showed no inter-serotype recombination events between the DENV-1 and DENV-2, while six and seven breakpoints were found in DENV-1 and DENV-2. Conclusively, the individual genes might not be suitable to analyze the evolution and selection pressure isolated in mainland China; the mutations in the amino acid residues in the E, NS1 and NS3 regions may play important roles in DENV-1 and DENV-2 epidemics.

Spatiotemporal patterns of Japanese encephalitis in China, 2002-2010

Objective

The aim of the study is to examine the spatiotemporal pattern of Japanese Encephalitis (JE) in mainland China during 2002–2010. Specific objectives of the study were to quantify the temporal variation in incidence of JE cases, to determine if clustering of JE cases exists, to detect high risk spatiotemporal clusters of JE cases and to provide evidence-based preventive suggestions to relevant stakeholders.

Methods

Monthly JE cases at the county level in mainland China during 2002–2010 were obtained from the China Information System for Diseases Control and Prevention (CISDCP). For the purpose of the analysis, JE case counts for nine years were aggregated into four temporal periods (2002; 2003–2005; 2006; and 2007–2010). Local Indicators of Spatial Association and spatial scan statistics were performed to detect and evaluate local high risk space-time clusters.

Results

JE incidence showed a decreasing trend from 2002 to 2005 but peaked in 2006, then fluctuated over the study period. Spatial cluster analysis detected high value clusters, mainly located in Southwestern China. Similarly, we identified a primary spatiotemporal cluster of JE in Southwestern China between July and August, with the geographical range of JE transmission increasing over the past years.

Conclusion

JE in China is geographically clustered and its spatial extent dynamically changed during the last nine years in mainland China. This indicates that risk factors for JE infection are likely to be spatially heterogeneous. The results may assist national and local health authorities in the development/refinement of a better preventive strategy and increase the effectiveness of public health interventions against JE transmission.

Japanese encephalitis (JE) is a mosquito-borne disease, which primarily occurs in rural and suburban areas of Southeast Asia and the Western Pacific region. JE still remains a significant public health problem in mainland China, with approximately 50% of global cases annually. Few studies have explored the spatiotemporal patterns of JE cases in China. Here we reported the results of Local Indicators of Spatial Association and spatial scan statistics of JE cases in mainland China at the county level during the four periods: 2002; 2003–2005; 2006; 2007–2010. The primary spatiotemporal cluster of JE was detected in Southwestern China between July and August, with the geographical range of JE transmission increasing over the past years. The results of LISA and spatial scan statistics were consistent which indicates that these methods are reliable and could have wider applications in the fields of disease surveillance and management in China, particularly in the surveillance and monitoring of other vector-borne diseases. These findings may assist in informing prevention and control strategies and increase the effectiveness of public health interventions against JE transmission.