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![Temporal and spatial distribution of dengue cases in human from 2005-2017 in mainland China. (A) The year-month temporal change of dengue fever (log [Dengue cases]) in China from 2005-2017. (B) The spatial distribution of dengue cases distinguished by color from high to low in every county in China from 2005-2017.](profile/Ruiyun-Li-2/publication/327607827/figure/fig2/AS:704240137826304@1544915341788/Temporal-and-spatial-distribution-of-dengue-cases-in-human-from-2005-2017-in-mainland.png)
Temporal and spatial distribution of dengue cases in human from 2005-2017 in mainland China. (A) The year-month temporal change of dengue fever (log [Dengue cases]) in China from 2005-2017. (B) The spatial distribution of dengue cases distinguished by color from high to low in every county in China from 2005-2017.
Source publication
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 influen...
Contexts in source publication
Context 1
... total of 60,448 indigenous dengue cases were reported in mainland China with a generally increasing trend from 2005 to 2017 (Figure 1). The annual dengue incidence ranged from 0 to 1400 per 100,000 people across 768 counties, with a particularly higher incidence in Guangdong and Yunnan Provinces (Figure 2). ...
Context 2
... model prediction was further validated by the corresponding AUC value, which ranges from 0.89-0.96 depending on the year ( Figure S1). pre, annul average precipitation; ndvi_max, the annual maximum normalized differential vegetation index; ndvi_mean, the annual average normalized differential vegetation index; ndvi_min, the annual minimum normalized differential vegetation index. ...
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Citations
... The study set a maximum spatial cluster size of 1,000 km and a maximum temporal cluster size of 20%. The p value was calculated using Monte Carlo random sampling to generate the simulation dataset (22). Any spatiotemporal cluster with a p value <0.05 was considered statistically significant. ...
Equine influenza (EI) is a severe infectious disease that causes huge economic losses to the horse industry. Spatial epidemiology technology can explore the spatiotemporal distribution characteristics and occurrence risks of infectious diseases, it has played an important role in the prevention and control of major infectious diseases in humans and animals. For the first time, this study conducted a systematic analysis of the spatiotemporal distribution of EI using SaTScan software and investigated the important environmental variables and suitable areas for EI occurrence using the Maxent model. A total of 517 occurrences of EI from 2005 to 2022 were evaluated, and 14 significant spatiotemporal clusters were identified. Furthermore, a Maxent model was successfully established with high prediction accuracy (AUC = 0.920 ± 0.008). The results indicated that annual average ultraviolet radiation, horse density, and precipitation of the coldest quarter were the three most important environmental variables affecting EI occurrence. The suitable areas for EI occurrence are widely distributed across all continents, especially in Asia (India, Mongolia, and China) and the Americas (Brazil, Uruguay, USA, and Mexico). In the future, these suitable areas will expand and move eastward. The largest expansion is predicted under SSP126 scenarios, while the opposite trend will be observed under SSP585 scenarios. This study presents the spatial epidemiological characteristics of EI for the first time. The results could provide valuable scientific insights that can effectively inform prevention and control strategies in regions at risk of EI worldwide.
... The incidence of DHF showed a linear increase along with the increase in rainfall [22]. The influence of rainfall on the risk of dengue fever depends on local socio-ecological conditions [23] [24]. Many studies say that rainfall is the main climate that affects of dengue incidence [25] [26]. ...
... MaxEnt is one of the most commonly used ecological niche models. Previous study showed that DENV outbreaks in China are significantly shaped by environmental factors, such as temperature, precipitation and land cover through MaxEnt model [38]. The MaxEnt model could correctly describe the breeding sites distribution of Ae. ...
Background and Aim
Dengue fever, transmitted by Aedes mosquitoes, is a significant public health concern in tropical and subtropical regions. With the end of the COVID-19 pandemic and the reopening of the borders, dengue fever remains a threat to mainland China, Zhejiang province of China is facing a huge risk of importing the dengue virus. This study aims to analyze and predict the current and future potential risk regions for Aedes vectors distribution and dengue prevalence in Zhejiang province of China.
Method
We collected occurrence records of DENV and DENV vectors globally from 2010 to 2022, along with historical and future climate data and human population density data. In order to predict the probability of DENV distribution in Zhejiang province of China under future conditions, the ecological niche of Ae. aegypti and Ae. albopictus was first performed with historical climate data based on MaxEnt. Then, predicted results along with a set of bioclimatic variables, elevation and human population density were included in MaxEnt model to analyze the risk region of DENV in Zhejiang province. Finally, the established model was utilized to predict the spatial pattern of DENV risk in the current and future scenarios in Zhejiang province of China.
Results
Our findings indicated that approximately 89.2% (90,805.6 KM²) of Zhejiang province of China is under risk, within about 8.0% (8,144 KM²) classified as high risk area for DENV prevalence. Ae. albopictus were identified as the primary factor influencing the distribution of DENV. Future predictions suggest that sustainable and “green” development pathways may increase the risk of DENV prevalence in Zhejiang province of China. Conversely, Fossil-fueled development pathways may reduce the risk due to the unsuitable environment for vectors.
Conclusions
The implications of this research highlight the need for effective vector control measures, community engagement, health education, and environmental initiatives to mitigate the potential spread of dengue fever in high-risk regions of Zhejiang province of China.
... Since Liu et al. first reported the detection of DENV in Yunnan Province in 2006, large-scale dengue outbreaks have occurred in Yunnan every 2 years since 2013, involving different DENV serotypes (20). More specifically, DENV1 was the main serotype that caused dengue epidemics in 2013-2016, DENV2 was mainly involved in 2015, DENV3 was mainly concentrated in 2013 and 2015, while DENV4 was only scattered sporadically (21)(22)(23). Notably, the dengue outbreak areas, such as Jinghong, Mengla, Ruili, Jiangcheng, and Menglian, were mainly focused on border port cities with Myanmar, Vietnam, and Laos. In other words, Yunnan was a province that borders dengueendemic Southeast Asian countries. ...
Since 2013, a dengue epidemic has broken out in Yunnan, China and neighboring countries. However, after the COVID-19 pandemic in 2019, the number of dengue cases decreased significantly. In this retrospective study, epidemiological and genetic diversity characterizations of dengue viruses (DENV) isolated in Yunnan between 2017 and 2018 were performed. The results showed that the dengue outbreak in Yunnan from 2017 to 2018 was mainly caused by DENV1 (genotype I and genotype V) and DENV2 (Asia I, Asia II, and Cosmopolitan). Furthermore, correlation analysis indicated a significant positive correlation between the number of imported and local cases (correlation coefficient = 0.936). Multiple sequence alignment and phylogenetic divergence analysis revealed that the local isolates are closely related to the isolates from Myanmar and Laos. Interestingly, recombination analysis found that the DENV1 and DENV2 isolates in this study had widespread intra-serotype recombination. Taken together, the results of the epidemiological investigation imply that the dengue outbreak in Yunnan was primarily due to imported cases. This study provides a new reference for further investigations on the prevalence and molecular epidemiology of DENV in Yunnan, China.
... Spatial disparities exist in the distribution of infectious diseases owing to differences in economic development levels, population density, meteorological factors, and more [7,8]. There is tremendous interest from policymakers, public health practitioners, and researchers in understanding the spatiotemporal epidemiology of notifiable infectious diseases in China. ...
... The clustering time of SS + tuberculosis was concentrated before 2010, whereas that of SS − tuberculosis was mainly concentrated after 2010. It is worth noting that [7,8,18,19,20,22,24,25,26,28,29,30,31,35,36,37,38,39,41,42,44,45,47,48,49,50,51,52,53,54,55,57,58,59,60,61,62,63,64,65,66,67,68,70,71,72,73,74,75,76,77,78,79,80,84,85,86] Kernel density map 4 [23,45,53,66] Excess hazard map 2 [18,58] Spatially smoothed percentile map 1 [18] Continuous distribution map 1 [18] Relative risk map 1 [67] Cluster (Hotspot) Detection 54 ...
... Moran's I statistic 41 [7,8,18,21,22,25,26,27,29,31,33,34,36,38,39,40,41,45,47,49,50,54,55,56,59,62,65,66,67,68,69,70,71,73,75,79,80,82,83,85,86] Kulldorff space-time scan statistic 26 [7,18,26,27,31,32,33,35,40,42,43,45,46,49,50,55,61,65,67,69,73,74,75,76,79,83] LISA cluster map 24 [7,8,18,21,22,26,29,33,34,38,39,40,41,45,47,49,55,56,59,69,70,73,74,75] Getis-Ord Gi* statistic 18 [21,33,34,36,38,39,49,53,56,62,65,66,68,73,74,75,77,80] K-nearest neighbor test 2 [18,21] Standard deviation elliptical analysis 2 [66,81] Optimized/emerging hot spot analysis 2 [83,84] Average nearest neighbor distance method 1 [23] Density-based spatial clustering of applications with noise 1 [23] Spatial exploration 10 Hierarchical cluster analysis 3 [52,82,86] Bayesian hierarchical model 2 [54,58] Spatial Markov chain model 2 [83,84] Spearman rank correlation analysis method 1 [75] Empirical orthogonal function analysis 1 [30] Fréchet distance approach 1 [25] Spatial/Spatio-temporal modelling 29 GWR 7 [20,46,62,68,72,73,78] Poisson regression 6 [24,42,46,61,63,72] Geographical detector method 4 [8,25,29,85] Bayesian spatial model 3 [22,51,63] Linear Logistic regression 1 [37] Granger causality analysis 1 [24] Cochran-Armitage trend test 1 [55] Kruskal-Wallis test 1 [42] Ecological niche model 1 [7] GMM 1 [60] Zheng et al. BMC Infectious Diseases (2022) 22:723 the primary clusters of fetal syphilis decreased by more than 65% in 2015 compared to 2010. ...
Background
The prevalence of infectious diseases remains one of the major challenges faced by the Chinese health sector. Policymakers have a tremendous interest in investigating the spatiotemporal epidemiology of infectious diseases. We aimed to review the small-scale (city level, county level, or below) spatiotemporal epidemiology of notifiable infectious diseases in China through a systematic review, thus summarizing the evidence to facilitate more effective prevention and control of the diseases.
Methods
We searched four English language databases (PubMed, EMBASE, Cochrane Library, and Web of Science) and three Chinese databases (CNKI, WanFang, and SinoMed), for studies published between January 1, 2004 (the year in which China’s Internet-based disease reporting system was established) and December 31, 2021. Eligible works were small-scale spatial or spatiotemporal studies focusing on at least one notifiable infectious disease, with the entire territory of mainland China as the study area. Two independent reviewers completed the review process based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.
Results
A total of 18,195 articles were identified, with 71 eligible for inclusion, focusing on 22 diseases. Thirty-one studies (43.66%) were analyzed using city-level data, 34 (47.89%) were analyzed using county-level data, and six (8.45%) used community or individual data. Approximately four-fifths (80.28%) of the studies visualized incidence using rate maps. Of these, 76.06% employed various spatial clustering methods to explore the spatial variations in the burden, with Moran’s I statistic being the most common. Of the studies, 40.85% explored risk factors, in which the geographically weighted regression model was the most commonly used method. Climate, socioeconomic factors, and population density were the three most considered factors.
Conclusions
Small-scale spatiotemporal epidemiology has been applied in studies on notifiable infectious diseases in China, involving spatiotemporal distribution and risk factors. Health authorities should improve prevention strategies and clarify the direction of future work in the field of infectious disease research in China.
... In the history of Guangzhou, there were three occasions of which the number of cases in 1 year exceeded a thousand (2006, 2013, and 2014). One outbreak happened in 2014 and affected 20 cities, with over 42,335 cases reported (Liu et al., 2018;Chen et al., 2019). Supplementary Figure S1 shows the geographic distribution of dengue cases in 2014, which is presented as an orange-red area denoting the concentration of a large number of cases. ...
Background
Dengue has become an increasing public health threat around the world, and climate conditions have been identified as important factors affecting the transmission of dengue, so this study was aimed to establish a prediction model of dengue epidemic by meteorological methods.
Methods
The dengue case information and meteorological data were collected from Guangdong Provincial Center for Disease Prevention and Control and Guangdong Meteorological Bureau, respectively. We used spatio-temporal analysis to characterize dengue epidemics. Spearman correlation analysis was used to analyze the correlation between lagged meteorological factors and dengue fever cases and determine the maximum lagged correlation coefficient of different meteorological factors. Then, Generalized Additive Models were used to analyze the non-linear influence of lagged meteorological factors on local dengue cases and to predict the number of local dengue cases under different weather conditions.
Results
We described the temporal and spatial distribution characteristics of dengue fever cases and found that sporadic single or a small number of imported cases had a very slight influence on the dengue epidemic around. We further created a forecast model based on the comprehensive consideration of influence of lagged 42-day meteorological factors on local dengue cases, and the results showed that the forecast model has a forecast effect of 98.8%, which was verified by the actual incidence of dengue from 2005 to 2016 in Guangzhou.
Conclusion
A forecast model for dengue epidemic was established with good forecast effects and may have a potential application in global dengue endemic areas after modification according to local meteorological conditions. High attention should be paid on sites with concentrated patients for the control of a dengue epidemic.
... Con respecto al tema de esta tesis, tanto el cambio climático como la variabilidad climática juega un papel importante en la distribución espacial y temporal del dengue (50,(218)(219)(220). Las asociaciones entre la estacionalidad del dengue y variables climáticas como la temperatura, las precipitaciones, las humedad, sol y El Niño-Oscilación del Sur (ENSO) se han documentado en anteriores estudios (200); estas relaciones según Zhang y colaboradores en un estudio en China en el 2019, haciendo uso de modelos no lineales muestran que la variación en la temperatura media diaria impulsa en parte la dinámica del dengue, y este factor climático está estrechamente relacionado con la fluctuación estacional de la incidencia del dengue (221). ...
... Estudios en otras latitudes como Australia, China, Pakistán, Tailandia y Sri Lanka hablan del efecto de las desigualdades sociales, fenómenos de pobreza e inequidad en la presencia de dengue en sus poblaciones (6,219,(281)(282)(283)(284)(285)(286). Estos investigadores hacen un llamado a aunar esfuerzos en un enfoque integrado y que debe ir más allá de las estrategias convencionales de control de vectores, con el objetivo de controlar la amenaza del dengue a través de mejoras en las condiciones socioeconómicas, de ingresos y educación (6,219,(281)(282)(283)(284)(285)(286). ...
... Estudios en otras latitudes como Australia, China, Pakistán, Tailandia y Sri Lanka hablan del efecto de las desigualdades sociales, fenómenos de pobreza e inequidad en la presencia de dengue en sus poblaciones (6,219,(281)(282)(283)(284)(285)(286). Estos investigadores hacen un llamado a aunar esfuerzos en un enfoque integrado y que debe ir más allá de las estrategias convencionales de control de vectores, con el objetivo de controlar la amenaza del dengue a través de mejoras en las condiciones socioeconómicas, de ingresos y educación (6,219,(281)(282)(283)(284)(285)(286). ...
RESUMEN DEL PROYECTO
Una de las enfermedades con mayor extensión en cuanto al número de casos en los últimos años el mundo es el dengue, siendo catalogada como una de las patologías transmisibles con mayor magnitud e importancia a nivel mundial tanto por la carga en salud como por su impacto económico (1). Esta enfermedad presenta un estimado de infecciones de aproximadamente 390 millones de personas al año; de los 194 países que hay actualmente en el mundo, 128 son catalogados como lugares endémicos de la enfermedad (2). La literatura actual habla del efecto de diversas vulnerabilidades sociales, climáticas y gubernamentales en la dinámica del dengue, exacerbando la presencia de la enfermedad en muchos territorios a nivel mundial (3–6).
El presente estudio tiene como objetivo evaluar el posible impacto de estos factores en el territorio colombiano en la frecuencia de esta enfermedad, todo desde un marco de vulnerabilidades presentes en la población colombiana, con fines de exponer el comportamiento complejo del dengue y proponer herramientas que permitan un manejo del dengue más particularizado, especifico y efectivo.
Diseño y objetivo
El siguiente estudio tiene como objetivo principal establecer la influencia de las vulnerabilidades sociales, climáticas, gubernamentales y de equidad a nivel departamental y municipal la evolución del dengue en el periodo de enero de 2015 a diciembre de 2020. Con esto se busca exponer la influencia de estas vulnerabilidades y establecer cuáles de estas son modificables con fines de reducir la frecuencia de dengue en el país.
Metodología
Estudio observacional, de medidas repetidas basado en los datos recolectados de manera mensual en todo el territorio a lo largo del periodo de análisis establecido, analítico, de tipo multinivel basado en la recolección de información de distintas fuentes nacionales.
El análisis principal se centrará en el número de casos de dengue presentados en el país en el periodo de enero de 2015 a diciembre de 2020. Se estimará la correlación espacial del número de casos de dengue presentados. Por otro lado se realizará un Gráfico Acíclico Dirigido (DAG por sus siglas en inglés) con fines de estudiar las interrelaciones de las distintas vulnerabilidades estudiadas con la presencia de dengue en el país y se estimará el posible impacto de dichos factores de vulnerabilidad con el número de casos de dengue por medio de un Modelo Aditivo Generalizado de tipo Poisson multinivel.
Por otro lado, se tiene presupuestado realiza un Análisis de Componentes Principales con fines de construir un índice multidimensional de vulnerabilidad que permita cuantificar el nivel de propensión a nivel municipal en cuanto al número de casos de dengue en el país.
Resultados esperados
De acuerdo con la información que se obtenga de los análisis propuestos, se espera describir el impacto de distintas vulnerabilidades objeto del estudio, así como las tendencias tanto temporales como espaciales del fenómeno del dengue, con el propósito de conocer más sobre la afectación de estas vulnerabilidades en la frecuencia de dengue en el periodo establecido con fines de ofrecer mejor información sobre los probables resultados obtenidos con este tratamiento.
... This has resulted in very high population mobility in the border areas of Yunnan, which facilitated the spread of mosquito-borne viruses such as dengue, Japanese encephalitis, chikungunya and Zika virus, etc. [18,19]. [21][22][23]. Specially, the outbreak area of dengue is mainly border port cities (such as Jinghong, Mengla, Ruili, Jiangcheng and Menglian, etc.) with Myanmar, Vietnam and Laos. In the past, it was generally believed that the dengue outbreak in Yunnan was caused by local cases spreading through mosquito bites or mosquitoes spreading across borders. ...
Dengue has become a worldwide public health problem. In recent years, Dengue has broken out in multiple countries and regions. From 2017 to 2018, a dengue epidemic broke out in Yunnan of China and neighboring Southeast Asian countries (Laos and Myanmar). In this study, retrospective detection and genetic characterization of Dengue virus (DENV) strains were carried out between 2017 and 2018 in Yunnan, China. The dengue outbreak in Yunnan, China in 2017 was mainly caused by DENV1, while the dengue outbreak in 2018 was caused by DENV1 and DENV2. Among them, DENV2 plays a dominant role. Then, three complete sequence and ten envelope (E) gene sequences were obtained through PCR. YN/324 and YN/017 were isolated from Myanmar travelers, and YN/117 isolate from Laos travelers. In addition, YN/007 isolate has 13 nucleotides (10270 nt -10282 nt) deletions in the 3′ UTRs, which leads to significant changes in the RNA secondary structure. Multiple sequence alignment E gene sequences revealed that these locally isolates share high homology (> 99.5%) with the isolates from Myanmar and Laos. The phylogenetic divergence analysis also revealed that most of the local isolates were closely related to the isolates from Myanmar and Laos. YN/033 strain belong to genotype V of DENV1, and it was detected in Yunnan after 2013. Other isolates of DENV1 in this study were clustered in a branch representing genotype I. These isolates of DENV2 in this study belonged to Asia I, Asia II and Cosmopolitan. Further analyzed the relationship between the evolutionary tree branching and amino acid substitution of genotype revealed that multiple amino acid substitutions are related to the genetic evolution of DENV1 or DENV2, respectively. Similarly, genotype 1 of DENV1 can be divided into 4 subgroups based on the amino acid substitutions. In addition, twelve amino acid mutations are unique to the isolate in this study and have never been reported. Interestingly, recombination analysis found that both DENV1 and DENV2 isolates in this study had widespread intra-serotype recombination. In summary, the results of the epidemiological investigation implies that the dengue outbreak in Yunnan of China was mainly caused by imported cases. This research provides new reference for further research on the prevalence and the molecular epidemiology of DENV in Yunnan, China, as well as the variation characteristics of these novel isolates.
... Precipitation can provide more habitats for mosquitoes, contributing to their survival and reproduction [80,81]. However, extreme precipitation can destroy vector habitats, disrupt the growth of insects, and wash eggs out of breeding grounds, further decreasing vector density and disease transmission [81][82][83]. ...
... Precipitation can provide more habitats for mosquitoes, contributing to their survival and reproduction [80,81]. However, extreme precipitation can destroy vector habitats, disrupt the growth of insects, and wash eggs out of breeding grounds, further decreasing vector density and disease transmission [81][82][83]. Some scholars also believe that although heavy precipitation takes away vector organisms, the rest of the rain will become a potential breeding ground for adult mosquitoes [84]. ...
Vector-borne diseases have posed a heavy threat to public health, especially in the context of climate change. Currently, there is no comprehensive review of the impact of meteorological factors on all types of vector-borne diseases in China. Through a systematic review of literature between 2000 and 2021, this study summarizes the relationship between climate factors and vector-borne diseases and potential mechanisms of climate change affecting vector-borne diseases. It further examines the regional differences of climate impact. A total of 131 studies in both Chinese and English on 10 vector-borne diseases were included. The number of publications on mosquito-borne diseases is the largest and is increasing, while the number of studies on rodent-borne diseases has been decreasing in the past two decades. Temperature, precipitation, and humidity are the main parameters contributing to the transmission of vector-borne diseases. Both the association and mechanism show vast differences between northern and southern China resulting from nature and social factors. We recommend that more future research should focus on the effect of meteorological factors on mosquito-borne diseases in the era of climate change. Such information will be crucial in facilitating a multi-sectorial response to climate-sensitive diseases in China.
... The severity of the disease varied according to the socioeconomic level of the patient [13,14]. Another similar study of China found that socioeconomic factors had a stronger infl uence on DF epidemics than environmental factors in the study area [15,16]. ...
With the objectives of determining the association between risk factors and Dengue Fever (DF), a case-control study with a random sample size of 102:102 in each was conducted at Hetauda and Kamalamai Municipalities, Nepal. The hypothesis of risk factors for DF like low level of knowledge about DF, discarded receptacles, old tires, containers, etc. nearby house, and use of stagnant AC/Coolers was used to conduct the study. Traveling to DF affected areas nearly 2 weeks before the onset of disease was significantly associated with dengue fever (OR= 6.10, 95% cl: 1.31-28.34, p<0.021). Waste disposal of old containers, receptacles, tires during the rainy season were significantly associated with the incidence of DF (AOR= 6.308, 96% cl: 2-751-14.462, p<0.000). The frequency of DF was associated with the middle social class level (p<0.05, d. f. 2). Uncovered water tanks of the household were significantly associated with DF (AOR= 3.78, 95% cl: 1.51-9.45, p<0.0043). As the number of families increases in the household, the number of cases increases with a positive correlation (r = +0.62). Crowded households with more than 2 occupants in one room were at risk of dengue infection. The study concluded that DF was associated with the risk factors of traveling to endemic areas, discarded waste containers, receptacles, tires, and uncovered water tanks, middle social class, and crowded households. Public health managers should prioritize these risk factors while planning for DF control and prevention.
