Spatiotemporal heterogeneity of schistosomiasis in China's Mainland:Evidence from a multi-stage continuous downscaling sentinel monitoring

Objective:To determine the spatiotemporal distribution of Schistosoma(S.)japonicum infections in humans,livestock,and Oncomelania(O.)hupensis across the endemic foci of China.Methods:Based on multi-stage continuous downscaling of sentinel monitoring,county-based schistosomiasis surveillance data were captured from the national schistosomiasis surveillance sites of China from 2005 to 2019.The data included S.japonicum infections in humans,livestock,and O.hupensis.The spatiotemporal trends for schistosomiasis were detected using a Joinpoint regression model,with a standard deviational ellipse(SDE)tool,which determined the central tendency and dispersion in the spatial distribution of schistosomiasis.Further,more spatiotemporal clusters of S.japonicum infections in humans,livestock,and O.hupensis were evaluated by the Poisson model.Results:The prevalence of S.japonicum human infections decreased from 2.06%to zero based on data of the national schistosomiasis surveillance sites of China from 2005 to 2019,with a reduction from 9.42%to zero for the prevalence of S.japonicum infections in livestock,and from 0.26%to zero for the prevalence of S.japonicum infections in O.hupensis.Analysis using an SDE tool showed that schistosomiasis-affected regions were reduced yearly from 2005 to 2014 in the endemic provinces of Hunan,Hubei,Jiangxi,and Anhui,as well as in the Poyang and Dongting Lake regions.Poisson model revealed 11 clusters of S.japonicum human infections,six clusters of S.japonicum infections in livestock,and nine clusters of S.japonicum infections in O.hupensis.The clusters of human infection were highly consistent with clusters of S.japonicum infections in livestock and O.hupensis.They were in the 5 provinces of Hunan,Hubei,Jiangxi,Anhui,and Jiangsu,as well as along the middle and lower reaches of the Yangtze River.Humans,livestock,and O.hupensis infections with S.japonicum were mainly concentrated in the north of the Hunan Province,south of the Hubei Province,north of the Jiangxi Province,and southwestern portion of Anhui Province.In the 2 mountainous provinces of Sichuan and Yunnan,human,livestock,and O.hupensis infections with S.japonicum were mainly concentrated in the northwestern portion of the Yunnan Province,the Daliangshan area in the south of Sichuan Province,and the hilly regions in the middle of Sichuan Province.Conclusions:A remarkable decline in the disease prevalence of S.japonicum infection was observed in endemic schistosomiasis in China between 2005 and 2019.However,there remains a long-term risk of transmission in local areas,with the highest-risk areas primarily in Poyang Lake and Dongting Lake regions,requiring to focus on vigilance against the rebound of the epidemic.Development of high-sensitivity detection methods and integrating the transmission links such as human and livestock infection,wild animal infection,and O.hupensis into the surveillance-response system will ensure the elimination of schistosomiasis in China by 2030.

The Importance of Spatiotemporal Heterogeneity for Biodiversity in Forest—Heathland Mosaics and Implications for Heathland Conservation

In biodiversity management, spatio-temporal heterogeneity is important to consider conserving high levels of habitat diversity and ecosystems. In this study, we investigated the relationship between landscape spatio-temporal heterogeneity and biodiversity in a mosaic-landscape, located in the Fontainebleau forest (France). The diversity of successional stages along a gradient from heathland to forest as well as the persistence of Calluna vulgaris (L.) Hull in different forest stands was examined in order to find how the numerous patches of European Heathland habitat embedded in this area should be maintained. The results indicated that in the areas of high spatio-temporal heterogeneity, a general increase is observed in species richness, in particular for vascular plants, bryophytes and carabids. C. vulgaris persisted in coniferous stands and young mixed stand but decreased under deciduous trees and old mixed stands. The Ellenberg’s values for light, nutrients and acidity, show the persistence of favorable enviromental conditions for heathland vegetation under coniferous stands and young mixed stands. These results enable us to offer recommendations to better manage mosaic-landscape biodiversity, and in particular, the heathland semi-natural habitats in the Fontainebleau forest and elsewhere in Europe.

Epidemiological Characteristics and Spatiotemporal Distribution Patterns of Human Norovirus Outbreaks in China, 2012–2018

Objective To clarify the epidemiological characteristics and spatial distribution patterns of human norovirus outbreaks in China, identify high-risk areas, and provide guidance for epidemic prevention and control.Methods This study analyzed 964 human norovirus outbreaks involving 50,548 cases in 26 provinces reported from 2012 to 2018. Epidemiological analysis and spatiotemporal scanning analysis were conducted to analyze the distribution of norovirus outbreaks in China.Results The outbreaks showed typical seasonality, with more outbreaks in winter and fewer in summer, and the total number of infected cases increased over time. Schools, especially middle schools and primary schools, are the most common settings of norovirus outbreaks, with the major transmission route being life contact. More outbreaks occurred in southeast coastal areas in China and showed significant spatial aggregation. The highly clustered areas of norovirus outbreaks have expanded northeast over time.Conclusion By identifying the epidemiological characteristics and high-risk areas of norovirus outbreaks, this study provides important scientific support for the development of preventive and control measures for norovirus outbreaks, which is conducive to the administrative management of high-risk settings and reduction of disease burden in susceptible areas.

A Spatio-Temporal Heterogeneity Data Accuracy Detection Method Fused by GCN and TCN

Spatio-temporal heterogeneous data is the database for decisionmaking in many fields,and checking its accuracy can provide data support for making decisions.Due to the randomness,complexity,global and local correlation of spatiotemporal heterogeneous data in the temporal and spatial dimensions,traditional detection methods can not guarantee both detection speed and accuracy.Therefore,this article proposes a method for detecting the accuracy of spatiotemporal heterogeneous data by fusing graph convolution and temporal convolution networks.Firstly,the geographic weighting function is introduced and improved to quantify the degree of association between nodes and calculate the weighted adjacency value to simplify the complex topology.Secondly,design spatiotemporal convolutional units based on graph convolutional neural networks and temporal convolutional networks to improve detection speed and accuracy.Finally,the proposed method is compared with three methods,ARIMA,T-GCN,and STGCN,in real scenarios to verify its effectiveness in terms of detection speed,detection accuracy and stability.The experimental results show that the RMSE,MAE,and MAPE of this method are the smallest in the cases of simple connectivity and complex connectivity degree,which are 13.82/12.08,2.77/2.41,and 16.70/14.73,respectively.Also,it detects the shortest time of 672.31/887.36,respectively.In addition,the evaluation results are the same under different time periods of processing and complex topology environment,which indicates that the detection accuracy of this method is the highest and has good research value and application prospects.

Seasonal and regional diff erences in long-term changes in large mesozooplankton(>505μm)biomass and abundance in a semi-enclosed subtropical bay

Obvious spatiotemporal heterogeneity is a distinct characteristic of ecosystems in subtropical bays.To aid targeted management and ecological restoration in long and narrow semi-enclosed subtropical bays,we analyzed seasonal and regional differences in long-term changes(1980-2019)in the biomass and abundance of large mesozooplankton(LMZ;>505μm)in Xiangshan Bay,Zhejiang,China.We found spatiotemporal heterogeneity in the historical changes of LMZ.Significant negative trends in LMZ biomass were found in the inner and middle bay during the warm season(summer and autumn),when the nutrient concentration(especially dissolved inorganic nitrogen)and temperature increased simultaneously.Nutrient changes in Xiangshan Bay began in the late 1980s or early 1990s,coinciding with large-scale fish cage development.A rapid decline in LMZ biomass occurred after 2005 when power plants commenced operation,accelerating the warming trend.Therefore,the joint stress of eutrophication and warming likely precipitated the decline in LMZ biomass.Conversely,a significant increase in LMZ biomass was found in the outer bay in spring.This trend was consistent with the trend of LMZ biomass near the Changjiang(Yangtze)River estuary,which indicates that the pelagic ecosystem in the outer bay was aff ected by water from the Changjiang River estuary during spring.Based on our results,ecosystem management and restoration in semi-enclosed subtropical bays should focus on internal waters,which have a poor capacity for water exchange.For Xiangshan Bay,the changes in the Changjiang River estuary ecosystem during the cold season(winter and spring)should also be considered.

Spatiotemporal analysis of the impact of urban landscape forms on PM_(2.5) in China from 2001 to 2020

Urban landscape forms can be effective in reducing increasing PM_(2.5) concentrations due to urbanization in China,making it crucially important to accurately quantify the spatiotemporal impact of urban landscape forms on PM_(2.5) variations.Three landscape indices and six control variables were selected to assess these impacts in 362 Chinese cities during different time scales from 2001 to 2020,using a spatiotemporal geographically weighted regression model,random forest models and partial dependence plots.The results show that there are spatiotemporal differences in the impacts of landscape indices on PM_(2.5).the proportion of urban green infrastructure(PLAND-UGI)and the fractal dimension of urban green infrastructure(FRACT-UGI)exacerbate PM_(2.5) concentrations in the northwest,the proportion of impervious surfaces(PLAND-Impervious)mitigates air pollution in northwest and southwest China,and shannon’s diversity index(SHDI)has seasonal differences in the northwest.PLAND-UGI is the landscape index with the largest contribution(30%)and interpretable range.The relationship between FRACT and PM_(2.5) was more complex than for other landscape indices.The results of this study contribute to a deeper understanding of the spatial and temporal differences in the impact of urban landscape patterns on PM_(2.5),contributing to clean urban development and sustainable development.

TransCode:Uncovering COVID-19 transmission patterns via deep learning

Background The heterogeneity of COVID-19 spread dynamics is determined by complex spatiotemporal transmission patterns at a fine scale,especially in densely populated regions.In this study,we aim to discover such fine-scale transmission patterns via deep learning.Methods We introduce the notion of TransCode to characterize fine-scale spatiotemporal transmission patterns of COVID-19 caused by metapopulation mobility and contact behaviors.First,in Hong Kong,China,we construct the mobility trajectories of confirmed cases using their visiting records.Then we estimate the transmissibility of individual cases in different locations based on their temporal infectiousness distribution.Integrating the spatial and temporal information,we represent the TransCode via spatiotemporal transmission networks.Further,we propose a deep transfer learning model to adapt the TransCode of Hong Kong,China to achieve fine-scale transmission characterization and risk prediction in six densely populated metropolises:New York City,San Francisco,Toronto,London,Berlin,and Tokyo,where fine-scale data are limited.All the data used in this study are publicly available.Results The TransCode of Hong Kong,China derived from the spatial transmission information and temporal infectiousness distribution of individual cases reveals the transmission patterns(e.g.,the imported and exported transmission intensities)at the district and constituency levels during different COVID-19 outbreaks waves.By adapting the TransCode of Hong Kong,China to other data-limited densely populated metropolises,the proposed method outperforms other representative methods by more than 10%in terms of the prediction accuracy of the disease dynamics(i.e.,the trend of case numbers),and the fine-scale spatiotemporal transmission patterns in these metropolises could also be well captured due to some shared intrinsically common patterns of human mobility and contact behaviors at the metapopulation level.Conclusions The fine-scale transmission patterns due to the metapopulation level mobility(e.g.,travel across different districts)and contact behaviors(e.g.,gathering in social-economic centers)are one of the main contributors to the rapid spread of the virus.Characterization of the fine-scale transmission patterns using the TransCode will facilitate the development of tailor-made intervention strategies to effectively contain disease transmission in the targeted regions.

地理大数据挖掘——目标、内涵与研究问题

The objective,connotations and research issues of big geodata mining were discussed to address its significance to geographical research in this paper.Big geodata may be categorized into two domains:big earth observation data and big human behavior data.A description of big geodata includes,in addition to the“5Vs”(volume,velocity,value,variety and veracity),a further five features,that is,granularity,scope,density,skewness and precision.Based on this approach,the essence of mining big geodata includes four aspects.First,flow space,where flow replaces points in traditional space,will become the new presentation form for big human behavior data.Second,the objectives for mining big geodata are the spatial patterns and the spatial relationships.Third,the spatiotemporal distributions of big geodata can be viewed as overlays of multiple geographic patterns and the characteristics of the data,namely heterogeneity and homogeneity,may change with scale.Fourth,data mining can be seen as a tool for discovery of geographic patterns and the patterns revealed may be attributed to human-land relationships.The big geodata mining methods may be categorized into two types in view of the mining objective,i.e.,classification mining and relationship mining.Future research will be faced by a number of issues,including the aggregation and connection of big geodata,the effective evaluation of the mining results and the challenge for mining to reveal“non-trivial”knowledge.