Spatial analysis of dengue fever in Guangdong Province, China, 2001-2006

A Privacy-Preserved Internet-of-Medical-Things Scheme for Eradication and Control of Dengue Using UAV

Dengue is a mosquito-borne viral infection, found in tropical and sub-tropical climates worldwide, mostly in urban and semi-urban areas. Countries like Pakistan receive heavy rains annually resulting in floods in urban cities due to poor drainage systems. Currently, different cities of Pakistan are at high risk of dengue outbreaks, as multiple dengue cases have been reported due to poor flood control and drainage systems. After heavy rain in urban areas, mosquitoes are provided with a favorable environment for their breeding and transmission through stagnant water due to poor maintenance of the drainage system. The history of the dengue virus in Pakistan shows that there is a closed relationship between dengue outbreaks and a rainfall. There is no specific treatment for dengue; however, the outbreak can be controlled through internet of medical things (IoMT). In this paper, we propose a novel privacy-preserved IoMT model to control dengue virus outbreaks by tracking dengue virus-infected patients based on bedding location extracted using call data record analysis (CDRA). Once the bedding location of the patient is identified, then the actual infected spot can be easily located by using geographic information system mapping. Once the targeted spots are identified, then it is very easy to eliminate the dengue by spraying the affected areas with the help of unmanned aerial vehicles (UAVs). The proposed model identifies the targeted spots up to 100%, based on the bedding location of the patient using CDRA.

Dengue and Dengue Virus in Guangdong, China, 1978-2017: Epidemiology, Seroprevalence, Evolution, and Policies

Background

Guangdong is a hyperepidemic area of dengue, which has over 0.72 million cumulative cases within the last four decades, accounting for more than 90% of cases in China. The local epidemic of dengue in Guangdong is suspected to be triggered by imported cases and results in consequent seasonal transmission. However, the comprehensive epidemiological characteristics of dengue in Guangdong are still unclear.

Methods

The epidemiology, seroprevalence, molecular evolution of dengue virus, and the development of policies and strategies on the prevention and control of dengue were analyzed in Guangdong, China from 1978 to 2017.

Findings

Seasonal transmission of dengue virus in Guangdong, China was mainly sustained from July to October of each year. August to September was the highest risk period of local dengue outbreaks. Most of the dengue cases in Guangdong were young and middle-aged adults. Five hundred and three fatal cases were recorded, which declined within the last two decades (n = 10). The serological test of healthy donors' serum samples showed a positive rate of 5.77%. Dengue virus 1-4 (DENV 1-4) was detected in Guangdong from 1978 to 2017. DENV 1 was the dominant serotype of dengue outbreaks from 1978 to 2017, with an increasing tendency of DENV 2 since 2010. Local outbreaks of DENV 3 were rare. DENV 4 was only encountered in imported cases in Guangdong, China. The imported cases were the main source of outbreaks of DENV 1-2. Early detection, management of dengue cases, and precise vector control were the key strategies for local dengue prevention and control in Guangdong, China.

Interpretation

Dengue has not become an endemic arboviral disease in Guangdong, China. Early detection, case management, and implementation of precise control strategies are key findings for preventing local dengue transmission, which may serve for countries still struggling to combat imported dengue in the west pacific areas.

Estimating the Threshold Effects of Climate on Dengue: A Case Study of Taiwan

Climate change is regarded as one of the major factors enhancing the transmission intensity of dengue fever. In this study, we estimated the threshold effects of temperature on Aedes mosquito larval index as an early warning tool for dengue prevention. We also investigated the relationship between dengue vector index and dengue epidemics in Taiwan using weekly panel data for 17 counties from January 2012 to May 2019. To achieve our goals, we first applied the panel threshold regression technique to test for threshold effects and determine critical temperature values. Data were then further decomposed into different sets corresponding to different temperature regimes. Finally, negative binomial regression models were applied to assess the non-linear relationship between meteorological factors and Breteau index (BI). At the national level, we found that a 1°C temperature increase caused the expected value of BI to increase by 0.09 units when the temperature is less than 27.21 °C, and by 0.26 units when the temperature is greater than 27.21 °C. At the regional level, the dengue vector index was more sensitive to temperature changes because double threshold effects were found in the southern Taiwan model. For southern Taiwan, as the temperature increased by 1°C, the expected value of BI increased by 0.29, 0.63, and 1.49 units when the average temperature was less than 27.27 °C, between 27.27 and 30.17 °C, and higher than 30.17 °C, respectively. In addition, the effects of precipitation and relative humidity on BI became stronger when the average temperature exceeded the thresholds. Regarding the impacts of climate change on BI, our results showed that the potential effects on BI range from 3.5 to 54.42% under alternative temperature scenarios. By combining threshold regression techniques with count data regression models, this study provides evidence of threshold effects between climate factors and the dengue vector index. The proposed threshold of temperature could be incorporated into the implementation of public health measures and risk prediction to prevent and control dengue fever in the future.

Exploring Epidemiological Characteristics of Domestic Imported Dengue Fever in Mainland China, 2014-2018

Epidemiological characteristics of domestic imported dengue fever in mainland China, 2014-2018, including time-series, spatial mobility and crowd features, were analyzed. There existed seasonal characteristics from August to November. The 872 domestic imported cases from 8 provinces, located in the southeastern, southwestern and southern coastal or border areas, were imported to 267 counties in 20 provinces of mainland China, located in the outer areas along the southwest-northeast line. The 628 domestic imported cases were still imported to the adjacent counties in the provinces themselves, 234 domestic imported cases were imported to 12 other provinces except the 8 original exported provinces, 493 cases in 2014 reached the peak, and 816 domestic imported cases were from Guangdong (675) and Yunnan (141). Domestic imported cases from Guangdong were imported to 218 counties, and 475 cases from Guangdong were imported to the adjacent counties in Guangdong itself. There were more male cases than female cases except in 2016. Domestic imported cases were clustered from 21 to 50 years old. The top three cases were from farmer, worker and housework or unemployed. The findings are helpful to formulate targeted, strategic plans and implement effective public health prevention and control measures.

Epidemiological and Evolutionary Analysis of Dengue-1 Virus Detected in Guangdong during 2014: Recycling of Old and Formation of New Lineages

The incidence of dengue is increasing in Guangdong, China, with the largest outbreak to date in 2014. Widespread awareness of epidemiological and molecular characteristics of the dengue virus (DENV) is required. In 2014, we isolated the virus from patients and sequenced its genome. The sequences of DENV isolated from Guangdong and other countries screened since 2005 were studied to establish molecular evolutionary databases along with epidemiological data to explore its epidemiological, phylogenetic, and molecular characteristics. Causes underlying the occurrence of the dengue epidemic included importation and localization of the virus. The number of indigenous cases significantly exceeded that of imported cases. Dengue virus 1 is the most important serotype and caused the long-term epidemic locally. Based on the data available since 2005, DENV1 was divided into three genotypes (I, IV, and V). Only genotypes I and V were detected in 2014. In 2014, an epidemic involving old lineages of DENV1 genotype V occurred after 2 years of silence. The genotype was previously detected from 2009 to 2011. Genotype I, which caused recent epidemics, demonstrated a continuation of new lineages, and a predictive pattern of molecular evolution since 2005 among the four lineages was present. The DENV isolated from Guangdong was closely related to those causing large-scale epidemics in neighboring countries, suggesting the possibility of its import from these countries. The lack of sufficient epidemiological data and evidence on the local mosquito-borne DENV emphasizes the importance of studying the molecular evolutionary features and establishing a well-established phylogenetic tree for dengue prevention and control in Guangdong.

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.

Epidemiological dynamics of dengue fever in mainland China, 2014-2018

OBJECTIVE

To explore the epidemiological dynamics of dengue fever.

METHODS

Epidemiological dynamics of imported and indigenous dengue cases during 2014-2018, including demographic, time-series, spatial and spatio-temporal features, were analyzed.

RESULTS

There were 5 458 imported dengue cases and 59 183 indigenous dengue cases during 2014-2018. Both imported and indigenous dengue cases show seasonal patterns from August to November. 12.9% (12.9/100) of dengue cases were from businessmen. 58.2% (58.2/100) of dengue cases were from individuals between 21-50 years old. Imported dengue cases, mainly from Southeastern Asia, had doubled, and were distributed in 734 counties, 29 provinces, with 50% (50/100) in Yunnan. Except in 2014, indigenous dengue cases were under 5 000 every year, but the number in counties increased dramatically from 51 to 127. The total cases were distributed in 314 districts, 13 provinces. They were clustered in Yunnan border and southern Guangdong. They emerged gradually from southwestern and southern provinces to southeastern coastal provinces, and then to central and northern provinces every year. They spread from the southern regions to the central and northern regions in 2014-2018.

CONCLUSIONS

The findings of epidemiological dynamics of dengue fever are helpful to formulate targeted, strategic plans and implement effective public health prevention and control measures.

Paediatric dengue infection in Cirebon, Indonesia: a temporal and spatial analysis of notified dengue incidence to inform surveillance

Background

The recent situation of dengue infection in Cirebon district is concerning due to an upsurge trend since the year 2010. The largest dengue outbreak was reported in 2016 which has affected more than 1600 children. A study was conducted to explore the temporal variability of dengue outbreak in Cirebon’s child population in during 2011–2017, and to assess the short-term effects of climatic and environmental factor on dengue incidence. In addition, the spatial pattern of dengue incidence in children and high-risk villages were investigated.

Methods

A total of 4597 confirmed dengue cases in children notified from January 2011 to December 2017 were analysed. Seasonal decomposition analysis was carried out to examine the annual seasonality. A generalized linear model (GLM) was applied to assess the short-term effect of climate and normalized difference vegetation index (NDVI) on dengue incidence. The incidence rate ratio (IRR) of the final model was reported. Spatial analyses were conducted by using Moran’s I and local indicator of spatial association (LISA) analyses to explore geographical clustering in incidence and to identify high-risk villages for dengue, respectively.

Results

An annual dengue epidemic period was observed with peaks occurring every January/February. Based on the GLM, temperature at a lag 4 months (IRR = 1.27; 95% confidence interval, 95% CI: 1.22–1.31, P < 0.001), rainfall at a lag 2 months (IRR = 0.99, 95% CI: 0.99–0.99, P < 0.001), humidity at lag 0 month (IRR = 1.05, 95% CI: 1.04–1.06, P < 0.001) and NDVI at a lag 1 month (IRR = 3.07, 95% CI: 1.94–4.86, P < 0.001) were associated with dengue incidence in children. The dengue incidence in children was spatially varied and clustered at the village level across Cirebon. During 2011–2017, a total of 38 high-risk villages for dengue were identified, which were mainly located in the northern part of Cirebon.

Conclusions

Seasonal patterns of dengue incidence in children in Cirebon were strongly associated with rainfall, temperature, humidity and NDVI variability, suggesting that climatic and environmental data could be used to help predict dengue outbreaks. Our spatial analysis revealed a clustered pattern in dengue incidence and high-risk villages for dengue across Cirebon, suggesting that effective interventions such as vector surveillance and school-based campaigns should be prioritized around the identified high-risk villages. Temporal and spatial analytical tools could be utilized to support local health authorities to apply timely and targeted public health interventions and help better planning and decision-making in order to minimize the impact of dengue outbreaks.

Electronic supplementary material

The online version of this article (10.1186/s13071-019-3446-3) contains supplementary material, which is available to authorized users.

Spatiotemporal characteristics and primary influencing factors of typical dengue fever epidemics in China

BACKGROUND

Dengue fever (DF) is a common mosquito-borne viral infectious disease in the world, and increasingly severe DF epidemics in China have seriously affected people's health in recent years. Thus, investigating spatiotemporal patterns and potential influencing factors of DF epidemics in typical regions is critical to consolidate effective prevention and control measures for these regional epidemics.

METHODS

A generalized additive model (GAM) was used to identify potential contributing factors that influence spatiotemporal epidemic patterns in typical DF epidemic regions of China (e.g., the Pearl River Delta [PRD] and the Border of Yunnan and Myanmar [BYM]). In terms of influencing factors, environmental factors including the normalized difference vegetation index (NDVI), temperature, precipitation, and humidity, in conjunction with socioeconomic factors, such as population density (Pop), road density, land-use, and gross domestic product, were employed.

RESULTS

DF epidemics in the PRD and BYM exhibit prominent spatial variations at 4 km and 3 km grid scales, characterized by significant spatial clustering over the Guangzhou-Foshan, Dehong, and Xishuangbanna areas. The GAM that integrated the Pop-urban land ratio (ULR)-NDVI-humidity-temperature factors for the PRD and the ULR-Road density-NDVI-temperature-water land ratio-precipitation factors for the BYM performed well in terms of overall accuracy, with Akaike Information Criterion values of 61 859.89 and 826.65, explaining a total variance of 83.4 and 97.3%, respectively. As indicated, socioeconomic factors have a stronger influence on DF epidemics than environmental factors in the study area. Among these factors, Pop (PRD) and ULR (BYM) were the socioeconomic factors explaining the largest variance in regional epidemics, whereas NDVI was the environmental factor explaining the largest variance in both regions. In addition, the common factors (ULR, NDVI, and temperature) in these two regions exhibited different effects on regional epidemics.

CONCLUSIONS

The spatiotemporal patterns of DF in the PRD and BYM are influenced by environmental and socioeconomic factors, the socioeconomic factors may play a significant role in DF epidemics in cases where environmental factors are suitable and differ only slightly throughout an area. Thus, prevention and control resources should be fully allocated by referring to the spatial patterns of primary influencing factors to better consolidate the prevention and control measures for DF epidemics.

Risk Factors Associated with Dengue Virus Infection in Guangdong Province: A Community-Based Case-Control Study

Dengue fever (DF) is a mosquito-borne infectious disease that is now an epidemic in China, Guangdong Province, in particular and presents high incidence rates of DF. Effective preventive measures are critical for controlling DF in China given the absence of a licensed vaccination program in the country. This study aimed to explore the individual risk factors for the dengue virus infection in Guangdong Province and to provide a scientific basis for the future prevention and control of DF. A case-control study including 237 cases and 237 controls was performed. Cases were defined for samples who were IgG-antibody positive or IgM-antibody positive, and willing to participate in the questionnaire survey. Additionally, the controls were selected through frequency matching by age, gender and community information from individuals who tested negative for IgG and IgM and volunteered to become part of the samples. Data were collected from epidemiological questionnaires. Univariate analysis was performed for the preliminary screening of 28 variables that were potentially related to dengue virus infection, and multivariate analysis was performed through unconditioned logistic regression analysis to analyze statistically significant variables. Multivariate analysis revealed two independent risk factors: Participation in outdoor sports (odds ratio (OR) = 1.80, 95% confidence interval (CI) 1.17 to 2.78), and poor indoor daylight quality (OR = 2.27, 95% CI 1.03 to 5.03). Two protective factors were identified through multivariate analysis: 2 occupants per room (OR = 0.43, 95% CI 0.28 to 0.65) or ≥3 occupants per room (OR = 0.45, 95% CI 0.23 to 0.89) and air-conditioner use (OR = 0.46, 95% CI 0.22 to 0.97). The results of this study were conducive for investigating the risk factors for dengue virus infection in Guangdong Province. Effective and efficient strategies for improving environmental protection and anti-mosquito measures must be provided. In addition, additional systematic studies are needed to explore other potential risk factors for DF.

Dengue control in the context of climate change: Views from health professionals in different geographic regions of China

BACKGROUND

Dengue is a significant climate-sensitive disease. Public health professionals play an important role in prevention and control of the disease. This study aimed to explore dengue control and prevention in the context of climate change in China.

METHODS

A cross-sectional survey was conducted among 630 public health professionals in 2015. Descriptive analysis and logistic regression were performed.

RESULTS

More than 80% of participants from southwest and central China believed climate change would affect dengue. However, participants from northeast China were less likely to believe so (65%). Sixty-nine percent of participants in Yunnan perceived that dengue had emerged/re-emerged in recent years, compared with 40.6% in Henan and 23.8% in Liaoning. Less than 60% of participants thought current prevention and control programs had been effective. Participants believed mosquitoes in high abundance, imported cases and climate change were main risk factors for dengue in China.

CONCLUSION

There were varying views of dengue in China. Professionals in areas susceptible to dengue were more likely to be concerned about climate change and dengue. Current prevention and control strategies need to be improved. Providing more information for staff in lower levels of Centers for Disease Control and Prevention may help in containing a possible increase of dengue.

Spatial analysis of dengue fever and exploration of its environmental and socio-economic risk factors using ordinary least squares: A case study in five districts of Guangzhou City, China, 2014

OBJECTIVE

Spatial patterns and environmental and socio-economic risk factors of dengue fever have been studied widely on a coarse scale; however, there are few such quantitative studies on a fine scale. There is a need to investigate these factors on a fine scale for dengue fever.

METHODS

In this study, a dataset of dengue fever cases and environmental and socio-economic factors was constructed at 1-km spatial resolution, in particular 'land types' (LT), obtained from the first high resolution remote sensing satellite launched from China (GF-1 satellite), and 'land surface temperature', obtained from moderate resolution imaging spectroradiometer (MODIS) images. Spatial analysis methods, including point density, average nearest neighbor, spatial autocorrelation, and hot spot analysis, were used to analyze spatial patterns of dengue fever. Spearman rank correlation and ordinary least squares (OLS) were used to explore associated environmental and socio-economic risk factors of dengue fever in five districts of Guangzhou City, China in 2014.

RESULTS

A total of 30553 dengue fever cases were reported in the districts of Baiyun, Haizhu, Yuexiu, Liwan, and Tianhe of Guangzhou, China in 2014. Dengue fever cases showed strong seasonal variation. The cases from August to October accounted for 96.3% of the total cases in 2014. The top three districts for dengue fever morbidity were Baiyun (1.32%), Liwan (0.62%), and Haizhu (0.60%). Strong spatial clusters of dengue fever cases were observed. Areas of high density for dengue fever were located at the district junctions. The dengue fever outbreak was significantly correlated with LT, normalized difference water index (NDWI), land surface temperature of daytime (LSTD), land surface temperature of nighttime (LSTN), population density (PD), and gross domestic product (GDP) (correlation coefficients of 0.483, 0.456, 0.612, 0.699, 0.705, and 0.205, respectively). The OLS equation was built with dengue fever cases as the dependent variable and LT, LSTN, and PD as explanatory variables. The residuals were not spatially autocorrelated. The adjusted R-squared was 0.320.

CONCLUSIONS

The findings of spatio-temporal patterns and risk factors of dengue fever can provide scientific information for public health practitioners to formulate targeted, strategic plans and implement effective public health prevention and control measures.

Photoperiodic diapause in a subtropical population of Aedes albopictus in Guangzhou, China: optimized field-laboratory-based study and statistical models for comprehensive characterization

BACKGROUND

Aedes albopictus is among the 100 most invasive species worldwide and poses a major risk to public health. Photoperiodic diapause provides a crucial ecological basis for the adaptation of this species to adverse environments. Ae. albopictus is the vital vector transmitting dengue virus in Guangzhou, but its diapause activities herein remain obscure.

METHODS

In the laboratory, yeast powder and food slurry were compared for a proper diapause determination method, and the critical photoperiod (CPP) was tested at illumination times of 11, 11.5, 12, 12.5, 13, and 13.5 h. A 4-parameter logistic (4PL) regression model was selected to estimate the CPP. In the field, the seasonal dynamics of the Ae. albopictus population, egg diapause, and hatching of overwintering eggs were investigated monthly, weekly, and daily, respectively. A distributed lag non-linear model (DLNM) was used to assess the associations of diapause with meteorological factors.

RESULTS

In the laboratory, both the wild population and the Foshan strain of Ae. albopictus were induced to diapause at an incidence greater than 80%, and no significant difference (P > 0.1) was observed between the two methods for identifying diapause. The CPP of this population was estimated to be 12.312 h of light. In the field, all of the indexes of the wild population were at the lowest levels from December to February, and the Route Index was the first to increase in March. Diapause incidence displayed pronounced seasonal dynamics. It was estimated that the day lengths of 12.111 h at week_{2016, 43} and 12.373 h at week_{2017, 41} contributed to diapause in 50% of the eggs. Day length was estimated to be the main meteorological factor related to diapause.

CONCLUSIONS

Photoperiodic diapause of Ae. albopictus in Guangzhou of China was confirmed and comprehensively elucidated in both the laboratory and the field. Diapause eggs are the main form for overwintering and begin to hatch in large quantities in March in Guangzhou. Furthermore, this study also established an optimized investigation system and statistical models for the study of Ae. albopictus diapause. These findings will contribute to the prevention and control of Ae. albopictus and mosquito-borne diseases.

Non-linear effects of mean temperature and relative humidity on dengue incidence in Guangzhou, China

BACKGROUND

Dengue fever is an important infectious disease in Guangzhou, China; previous studies on the effects of weather factors on the incidence of dengue fever did not consider the linearity of the associations.

METHODS

This study evaluated the effects of daily mean temperature, relative humidity and rainfall on the incidence of dengue fever. A generalized additive model with splines smoothing function was performed to examine the effects of daily mean, minimum and maximum temperatures, relative humidity and rainfall on incidence of dengue fever during 2006-2014.

RESULTS

Our analysis detected a non-linear effect of mean, minimum and maximum temperatures and relative humidity on dengue fever with the thresholds at 28°C, 23°C and 32°C for daily mean, minimum and maximum temperatures, 76% for relative humidity, respectively. Below the thresholds, there was a significant positive effect, the excess risk in dengue fever for each 1°C in the mean temperature at lag7-14days was 10.21%, (95% CI: 6.62% to 13.92%), 7.10% (95% CI: 4.99%, 9.26%) for 1°C increase in daily minimum temperature in lag 11days, and 2.27% (95% CI: 0.84%, 3.72%) for 1°C increase in daily maximum temperature in lag 10days; and each 1% increase in relative humidity of lag7-14days was associated with 1.95% (95% CI: 1.21% to 2.69%) in risk of dengue fever.

CONCLUSIONS

Future prevention and control measures and epidemiology studies on dengue fever should consider these weather factors based on their exposure-response relationship.

Complete Genome Characterization of the 2017 Dengue Outbreak in Xishuangbanna, a Border City of China, Burma and Laos

A dengue outbreak abruptly occurred at the border of China, Myanmar, and Laos in June 2017. By November 3rd 2017, 1184 infected individuals were confirmed as NS1-positivein Xishuangbanna, a city located at the border. To verify the causative agent, complete genome information was obtained through PCR and sequencing based on the viral RNAs extracted from patient samples. Phylogenetic trees were constructed by the maximum likelihood method (MEGA 6.0). Nucleotide and amino acid substitutions were analyzed by BioEdit, followed by RNA secondary structure prediction of untranslated regions (UTRs) and protein secondary structure prediction in coding sequences (CDSs). Strains YN2, YN17741, and YN176272 were isolated from local residents. Stains MY21 and MY22 were isolated from Burmese travelers. The complete genome sequences of the five isolates were 10,735 nucleotides in length. Phylogenetic analysis classified all five isolates as genotype I of DENV-1, while isolates of local residents and Burmese travelers belonged to different branches. The three locally isolates were most similar to the Dongguan strain in 2011, and the other two isolates from Burmese travelers were most similar to the Laos strain in 2008. Twenty-four amino acid substitutions were important in eight evolutionary tree branches. Comparison with DENV-1SS revealed 658 base substitutions in the local isolates, except for two mutations exclusive to YN17741, resulting in 87 synonymous mutations. Compared with the local isolates, 52 amino acid mutations occurred in the CDS of two isolates from Burmese travelers. Comparing MY21 with MY22, 17 amino acid mutations were observed, all these mutations occurred in the CDS of non-structured proteins (two in NS1, 10 in NS2, two in NS3, three in NS5). Secondary structure prediction revealed 46 changes in the potential nucleotide and protein binding sites of the CDSs in local isolates. RNA secondary structure prediction also showed base changes in the 3′UTR of local isolates, leading to two significant changes in the RNA secondary structure. To our knowledge, this study is the first complete genome analysis of isolates from the 2017 dengue outbreak that occurred at the border areas of China, Burma, and Laos.

Spatiotemporal Distribution and Geospatial Diffusion Patterns of 2013 Dengue Outbreak in Jhapa District, Nepal

This study describes spatiotemporal distribution and geospatial diffusion patterns of dengue outbreak of 2013 in Jhapa district, Nepal. Laboratory-confirmed dengue cases were collected from the District Public Health Office, Government of Nepal. Choropleth mapping technique, Global Moran's Index, SaTScan, and standard deviational ellipse were used to map and quantify the outbreak dynamics. The results revealed heterogeneous distribution and globally autocorrelated patterns. Local clusters were observed in 3 major urban centers. The standard deviational ellipse demonstrated the outbreak occurred from the east and diffused to the west along the east-west highway in different weeks. The results of this study could be useful to public health authorities to plan and execute dengue control strategies.

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.

Border analysis for spatial clusters

Background

The spatial scan statistic is widely used by public health professionals in the detection of spatial clusters in inhomogeneous point process. The most popular version of the spatial scan statistic uses a circular-shaped scanning window. Several other variants, using other parametric or non-parametric shapes, are also available. However, none of them offer information about the uncertainty on the borders of the detected clusters.

Method

We propose a new method to evaluate uncertainty on the boundaries of spatial clusters identified through the spatial scan statistic for Poisson data. For each spatial data location i, a function F(i) is calculated. While not a probability, this function takes values in the [0, 1] interval, with a higher value indicating more evidence that the location belongs to the true cluster.

Results

Through a set of simulation studies, we show that the F function provides a way to define, measure and visualize the certainty or uncertainty of each specific location belonging to the true cluster. The method can be applied whether there are one or multiple detected clusters on the map. We illustrate the new method on a data set concerning Chagas disease in Minas Gerais, Brazil.

Conclusions

The higher the intensity given to an area, the higher the plausibility of that particular area to belong to the true cluster in case it exists. This way, the F function provides information from which the public health practitioner can perform a border analysis of the detected spatial scan statistic clusters. We have implemented and illustrated the border analysis F function in the context of the circular spatial scan statistic for spatially aggregated Poisson data. The definition is clearly independent of both the shape of the scanning window and the probability model under which the data is generated. To make the new method widely available to users, it has been implemented in the freely available SaTScan\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^\mathrm{TM}$$\end{document}TM software www.satscan.org.

Surveillance of intensity level and geographical spreading of dengue outbreak among males and females in Punjab, Pakistan: A case study of 2011

BACKGROUND

Dengue fever is viral disease which spreads due to the bite of the Aedes aegypti mosquito. In recent years, it has affected around 40% population of the world. Its endemic flow has led to a large disease burden, in terms of human and financial resources.

METHODS

Geographical Information Systems (GIS) are normally used to develop epidemiological thematic maps. This study explores the patterns and hotspots, associated with the catastrophic outbreak of dengue, in Punjab, in 2011. The ArcView software was used to analyze the data reported by the district hospitals of Punjab. Twenty-one-thousand cases were reported from March to December 2011, with 300 causalities.

RESULTS AND CONCLUSION

This research reveals that from among the total 37 epidemiological weeks, the maximum impact was observed between weeks 22 and 27. The geographical flow and hotspots associated with dengue have been shown through thematic maps. A positive correlation between the risk for dengue and age was observed. The findings of this research can help health officials and decision-makers alert the public about future outbreaks and take preventive measures to considerably reduce the mortality and morbidity associated with the disease.

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.

Epidemiological trends of dengue in mainland China, 2005-2015

OBJECTIVE

To explore the epidemiological trend of dengue in mainland China.

METHODS

Data on dengue cases reported from 2005 to 2015 were collected, and epidemiological trends, including average age, occupation, seasonal distribution, and interval from illness onset to confirmation, were analyzed using SPSS 19.0 and R 3.1.1.

RESULTS

A total of 59 334 dengue cases were recorded in China during the years 2005-2015. Most dengue cases occurred in individuals aged between 21 years and 50 years. Of note, the median age of dengue cases did not show a trend towards becoming younger; the median age was significantly older than that of cases in dengue endemic areas. The proportion of cases occurring during September and October was higher in 2012-2015 than in 2005-2011. The number of affected provinces ranged between 10 and 27 and the number of affected counties ranged between 42 and 415. The median time from illness onset to confirmation of dengue decreased sharply in 2015, indicating that comprehensive measures have been taken in mainland China.

CONCLUSIONS

Although the number of dengue cases has increased and the affected areas have expanded in recent years, dengue is still an imported disease and does not present an endemic trend in mainland China.

Application of the analytic hierarchy approach to the risk assessment of Zika virus disease transmission in Guangdong Province, China

BACKGROUND

An international spread of Zika virus (ZIKV) infection has attracted global attention in 2015. The infection also affected Guangdong province, which is located in southern China. Multiple factors, including frequent communication with South America and Southeast Asia, suitable climate (sub-tropical) for the habitat of Aedes species, may increase the risk of ZIKV disease transmission in this region.

METHODS

An analytic hierarchy process (AHP) method was used to develop a semi-quantitative ZIKV risk assessment model. After selecting indicators, we invited experts in related professions to identify the index weight and based on that a hierarchical structure was generated. Then a series of pairwise comparisons were used to determine the relative importance of the criteria. Finally, the optimal model was established to estimate the spatial and seasonal transmission risk of ZIKV.

RESULTS

A total of 15 factors that potentially influenced the risk of ZIKV transmission were identified. The factor that received the largest weight was epidemic of ZIKV in Guangdong province (combined weight [CW] =0.37), followed by the mosquito density (CW = 0.18) and the epidemic of DENV in Guangdong province (CW = 0.14). The distribution of 123 districts/counties' RIs of ZIKV in Guangdong through different seasons were presented, respectively.

CONCLUSIONS

Higher risk was observed within Pearl River Delta including Guangzhou, Shenzhen and Jiangmen, and the risk is greater in summer and autumn compared to spring and winter.

VERTICAL TRANSMISSION OF DENGUE INFECTION: THE FIRST PUTATIVE CASE REPORTED IN CHINA

Dengue is a systemic viral infection that is commonly transmitted between humans via mosquitoes. Other modes of transmission such as the vertical one are rare and have been infrequently reported in the literature. This report investigates one case of vertical transmission of dengue in Guangzhou, China. A G1P1 lady at 39 weeks of gestation was referred to the Huzhong Hospital presenting a fever for two days. She subsequently developed a skin rash on the back and lower limb and at that time she had already experienced five days of fever. She subsequently went into labor and delivered a female neonate weighting 3,500 g at birth. The neonate developed fever on the third day of life which was associated with a systemic erythematous skin rash. There was no report or evidence of mosquito bites after birth. A complete blood count showed leucopenia, thrombocytopenia and anemia and the liver function test showed elevated AST, GGT and bilirubin. Dengue was diagnosed in the mother and the neonate by the ELISA dengue virus NS1 antigen test (Wantai, Beijing, China) and dengue virus fluorogenic quantitative PCR test (Liferiver, Shanghai, China).The case report illustrates the possibility of the vertical transmission of dengue. Clinicians should be alert to this possibility and institute early treatment. Further direct evidence and research are required.

Spatiotemporal analysis of dengue fever in Nepal from 2010 to 2014

Background

Due to recent emergence, dengue is becoming one of the major public health problems in Nepal. The numbers of reported dengue cases in general and the area with reported dengue cases are both continuously increasing in recent years. However, spatiotemporal patterns and clusters of dengue have not been investigated yet. This study aims to fill this gap by analyzing spatiotemporal patterns based on monthly surveillance data aggregated at district.

Methods

Dengue cases from 2010 to 2014 at district level were collected from the Nepal government’s health and mapping agencies respectively. GeoDa software was used to map crude incidence, excess hazard and spatially smoothed incidence. Cluster analysis was performed in SaTScan software to explore spatiotemporal clusters of dengue during the above-mentioned time period.

Results

Spatiotemporal distribution of dengue fever in Nepal from 2010 to 2014 was mapped at district level in terms of crude incidence, excess risk and spatially smoothed incidence. Results show that the distribution of dengue fever was not random but clustered in space and time. Chitwan district was identified as the most likely cluster and Jhapa district was the first secondary cluster in both spatial and spatiotemporal scan. July to September of 2010 was identified as a significant temporal cluster.

Conclusion

This study assessed and mapped for the first time the spatiotemporal pattern of dengue fever in Nepal. Two districts namely Chitwan and Jhapa were found highly affected by dengue fever. The current study also demonstrated the importance of geospatial approach in epidemiological research. The initial result on dengue patterns and risk of this study may assist institutions and policy makers to develop better preventive strategies.

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.

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.

Identification of Aedes albopictus larval index thresholds in the transmission of dengue in Guangzhou, China

Entomological indices have been used to quantitatively express vector density, but the threshold of larval indices of Aedes albopictus in dengue epidemics is still undefined. We conducted a case-control study to identify the thresholds of Aedes albopictus larval indices in dengue epidemics. Two unit levels of analysis were used: district and street. The discriminative power of the indices was assessed by receiver operating characteristic (ROC) curves. The association between the entomologic indices and dengue transmission was further explored by a logistic regression model. At the district level, there was no significant difference in the Breteau index (BI) between districts that reported cases and those did not (t=0.164, p>0.05), but the Container index (CI) did show a significant difference (t=2.028, p<0.01). The AUC (Area Under the Curve) of BI, CI, and prediction value were 0.540, 0.630, and 0.533, respectively. Predicting at the street level, the AUC of BI, CI, and prediction values were 0.684, 0.660, and 0.685, respectively, and 0.861, 0.827, and 0.867 for outbreaks. BI=5.1, CI=5.4, or prediction value =0.491were suggested to control the epidemic efficiently with the fewest resources, where BI=4.0, CI=5.1, or PRE =0.483 were suggested to achieve effectiveness.

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.