Spatio-temporal Cluster Analysis Research Articles

Spatiotemporal mapping of rice acreage and productivity growth in Bangladesh.

Technological advancements have long played crucial roles in rice productivity and food security in Bangladesh. Seasonal variation over time and regional differences in rice production, however, pose a threat to agricultural sustainability but remain unexplored. We performed a spatial-temporal mapping of rice cultivation area, production, and yield from 2006-2007 to 2019-2020 using secondary data for disaggregating 64 districts in Bangladesh. Growth and multivariate approaches were employed to analyze time-series data. Results showed that Mymensingh had the highest rice cultivated area and production, while Bandarban had the lowest. The 14 years highest average rice yield was found in Gopalganj and Dhaka (3.63 tons/ha), while Patuakhali (1.73 tons/ha) had the lowest. For the Aus, Aman, and Boro, the rice cultivation area in 19 districts, 11 districts, and 13 districts declined significantly. The overall rice production increased significantly in most districts. For the Aus, Aman, and Boro seasons, the rice yield in 54, 50, and 37 districts demonstrated a significant upward trend, respectively. The adoption rate of modern varieties has risen dramatically. However, there are notable variances between regions and seasons. A significant increasing trend in Aus (0.007% to 0.521%), Aman (0.004% to 0.039%), and Boro (0.013% to 0.584%) were observed in 28, 34, and 36 districts, respectively, with an increase of 1% adaptation of HYV. Predictions revealed that rice cultivation area and production of Aus, Aman, and Boro seasons will be increased in most of the regions of Bangladesh by 2030. Based on spatiotemporal cluster analysis, the five identified cluster groupings illustrated that clusters lack spatial cohesion and vary greatly seasonally. This suggests increasing rice production by expanding cultivable land, adopting high-yielding varieties, and integrating faster technological advancement in research and extension. The findings will assist scientists in developing region-specific production technologies and policymakers in designing decentral region-specific policies to ensure the future sustainability of rice production.

Landslide spatial prediction using cluster analysis

Temporal clustering is an intrinsic nature of landslide occurrences, therefore it should be considered in data-driven landslide spatial prediction (i.e., susceptibility assessment). However, it remains problematic regarding how to determine landslide temporal clusters and how to integrate susceptibility maps derived from different landslide temporal clusters. In this paper, a general framework of landslide spatial prediction model considering the temporal clustering of landslides is proposed. This novel framework first assesses landslide susceptibility separately based on each landslide temporal cluster identified by spatiotemporal clustering analysis and then integrates separate assessments by weighted averaging. In a case study, this general framework is implemented using the stacking network landslide susceptibility assessment method and used in the landslide spatial prediction of the Sanming City and Wenchuan seismic areas. The results show that the proposed framework outperformed traditional susceptibility models that do not consider landslide temporal clustering, and the integration of susceptibility models based on all landslide temporal clusters will promote the performance of landslide spatial prediction because levels of knowledge in long-term spatiotemporal landslide activities are considered. This novel general framework highlights the benefit of considering landslide temporal clustering in landslide spatial prediction and can provide better support for landslide risk management.

Heterogeneity of influenza infection at precise scale in Yinchuan, Northwest China, 2012-2022: evidence from Joinpoint regression and spatiotemporal analysis.

Identifying high-risk regions and turning points of influenza with a precise spatiotemporal scale may provide effective prevention strategies. In this study, epidemiological characteristics and spatiotemporal clustering analysis at the township level were performed. A descriptive study and a Joinpoint regression analysis were used to explore the epidemiological characteristics and the time trend of influenza. Spatiotemporal autocorrelation and clustering analyses were carried out to explore the spatiotemporal distribution characteristics and aggregation. Furthermore, the hotspot regions were analyzed by spatiotemporal scan analysis. A total of 4025 influenza cases were reported in Yinchuan showing an overall increasing trend. The tendency of influenza in Yinchuan consisted of three stages: increased from 2012 to the first peak in 2019 (32.62/100,000) with a slight decrease in 2016; during 2019 and 2020, the trend was downwards; then it increased sharply again and reached another peak in 2022. The Joinpoint regression analysis found that there were three turning points from January 2012 to December 2022, namely January 2020, April 2020, and February 2022. The children under ten displayed an upward trend and were statistically significant. The trend surface analysis indicated that there was a shifting trend from northern to central and southern. A significant positive spatial auto-correlation was observed at the township level and four high-incidence clusters of influenza were detected. These results suggested that children under 10years old deserve more attention and the spatiotemporal distribution of high-risk regions of influenza in Yinchuan varies every year at the township level. Thus, more monitoring and resource allocation should be prone to the four high-incidence clusters, which may benefit the public health authorities to carry out the vaccination and health promotion timely.

Surveillance of Flea-Borne Typhus in California, 2011-2019.

Flea-borne typhus (FBT), also referred to as murine typhus, is an acute febrile disease in humans caused by the bacteria Rickettsia typhi. Currently, cases of FBT are reported for public health surveillance purposes (i.e., to detect incidence and outbreaks) in a few U.S. states. In California, healthcare providers and testing laboratories are mandated to report to their respective local public health jurisdictions whenever R. typhi or antibodies reactive to R. typhi are detected in a patient, who then report cases to state health department. In this study, we characterize the epidemiology of flea-borne typhus cases in California from 2011 to 2019. A total of 881 cases were reported during this period, with most cases reported among residents of Los Angeles and Orange Counties (97%). Demographics, animal exposures, and clinical courses for case patients were summarized. Additionally, spatiotemporal cluster analyses pointed to five areas in southern California with persistent FBT transmission.

Epidemiological features of SARS-CoV-2 Omicron infection under new control strategy: a cross-sectional study of the outbreak since December 2022 in Sichuan, China

BackgroundA major shift in the “dynamic zero-COVID” policy was announced by China’s National Health Commission on December 7, 2022, and the subsequent immediate large-scale outbreak of SARS-CoV-2 infections in the entire country has caused worldwide concern. This observational cross-sectional study aimed to describe the epidemiological characteristics of this outbreak in Sichuan, China.MethodsAll data were self-reported online by volunteers. We described the epidemic by characterizing the infection, symptoms, clinical duration, severity, spatiotemporal clustering, and dynamic features of the disease. Prevalence ratio (PR), Odds ratios (ORs) and adjusted ORs were calculated to analyze the associations between risk factors and infection and the associations of risk factors with clinical severity using log-binomial and multivariable logistic regression models; 95% confidence intervals (CIs) and Wald test results were reported. The prevalence rates and clinical severity among different subgroups were compared using the Chi-square and trend Chi-square tests.ResultsBetween January 6 and 12, 2023, 138,073 volunteers were enrolled in this survey, and 102,645 were infected with COVID-19, holding a prevalence rate of 74.34%; the proportion of asymptomatic infections was 1.58%. Log-binomial regression revealed that the risk of infection increased among those living in urban areas. Multivariable logistic regression analysis showed that female sex, chronic diseases, older age and the fewer doses of vaccine received were associated with an increased risk of severe clinical outcomes after infection. We estimated the mean reproduction number during this pandemic was 1.83. The highest time-dependent reproduction number was 4.15; this number decreased below 1 after 11 days from December 7, 2022. Temporal trends revealed a single peak curve with a plateau pattern of incidence during the outbreak, whereas spatiotemporal clustering analysis showed that the onset in 21 cities in the Sichuan province had four-wave peaks.ConclusionsThe peak of the first wave of Omicron infection in Sichuan Province had passed and could be considered a snapshot of China under the new control strategy. There were significant increases in the risk of severe clinical outcomes after infection among females, with chronic diseases, and the elderly. The vaccines have been effective in reducing poor clinical outcomes.

Evaluation of skin cancer screening in Germany – Spatiotemporal associations between skin cancer screening and skin cancer mortality based on ambulatory claims data

SummaryBackgroundThere are regional differences in skin cancer screening uptake in Germany. So far, it is unclear whether a high uptake of screening services leads to a reduction in mortality. This article presents study results on the investigation of spatiotemporal associations between skin cancer screening and mortality. The methods used are discussed regarding their suitability.Material and methodsThe basis is ambulatory claims data on the utilization of early skin cancer detection as well as data on skin cancer mortality from the cause‐of‐death statistics of the years 2011–2015 at county level in Germany. In addition to a descriptive evaluation, spatiotemporal cluster analyses and regression models were used to investigate the relationship between the uptake of early detection and mortality. In addition to age, adjustments were also made for other selected socio‐economic and socio‐graphical variables.ResultsThe descriptive results show striking spatial patterns of skin cancer screening and mortality. Cluster analyses identified regions with significantly higher and lower cases of early detection and skin cancer mortality. The spatiotemporal regression analyses show no clear association. Only early detection by a dermatologist, adjusted for age, shows an association with mortality.ConclusionsNo clear association between early skin cancer detection and mortality can be derived from the results. However, the study design used with a spatiotemporal cluster and regression analysis has shown that these methods allow in‐depth statements about the relationship between early skin cancer detection and mortality.

Investigation of Risk Factors Associated with the African Swine Fever Outbreaks in the Nizhny Novgorod Region of Russia, 2011–2022

African swine fever (ASF) is a transboundary viral disease that affects both domestic pigs and wild boar, causing enormous damage to the pig production. Numerous studies are devoted to elucidating the role of domestic and wild pigs in ASF outbreaks. The identification of the risk factors contributing to the spread of disease in previously unaffected regions is of special interest. We conducted a retrospective analysis of sporadic ASF outbreaks in the Nizhny Novgorod region of the Russian Federation from 2011 to 2022 among both domestic pig and wild boar populations. Methods of spatiotemporal cluster analysis, mathematical modeling with logistic and negative binomial regressions, as well as the cross-correlation analysis of time series, were used to identify the most significant factors associated with the ASF outbreaks’ presence and intensity. Regression analysis did not reveal a statistically significant correlation between ASF outbreaks in wild boar and their population density over the entire study period. However, the spatial and temporal coincidence between outbreaks and increased wild boar density was observed at the beginning of the ASF epidemic in the region. We identified the significant environmental and sociodemographic factors contributing to the spread of ASF in both domestic pig and wild boar populations. The number of small-scale farms (backyards) and summary road length were determined as the main factors contributing to the spread of ASF among domestic pigs in the Nizhny Novgorod region. The regression model for ASF outbreaks in the wild boar population revealed the presence of water bodies, number of hunting farms, and the occurrence of ASF outbreaks in the domestic pig population to be the main explanatory factors. A statistically significant coincidence was identified between the monthly volume of pork products imported to the region from the rest of Russia and the occurrence of ASF outbreaks in both domestic and wild populations. Thus, the identified spatiotemporal patterns may be useful to determine areas with an elevated risk of ASF outbreaks emergence for better resource management, disease control, and prevention. The importance of intensified monitoring and control measures in the pig products supply chain is strongly needed. The obtained models can be applied not only by the veterinary service of the Nizhny Novgorod region but to other regions with similar environmental and sociodemographic conditions.

Space-Time Patterns of Poultry Pathogens in the USA: A Case Study of Ornithobacterium rhinotracheale and Pasteurella multocida in Turkey Populations.

Respiratory infections caused by Ornithobacterium rhinotrachealis (ORT) and Pasteurella multocida (PM) bacteria are significant threats to the poultry industry by causing economic losses and welfare issues. Due to characterization difficulties and underutilization of epidemiological tools, description of the spatio-temporal spread of these diseases in the field is limited. The objectives of this retrospective observational cross-sectional study were to (a) investigate the existence of space-time clusters (hotspots); and (b) investigate the association between genetic similarity and spatial proximity for both pathogens using molecular typing and a recently developed Core-Genome Multilocus Sequencing Typing (cgMLST) scheme. ORT (n = 103) and PM (n = 69) isolates from confirmed disease outbreaks from one commercial company between 2013 and 2021 were obtained from a veterinary diagnostic laboratory, characterized using a cgMLST scheme and visualized using a minimum spanning tree. Spatio-temporal cluster analysis using SaTScanTM and a Spearman's rank correlation were performed to investigate clustering and any association between allelic diversity and geospatial distance. The cgMLST sequencing revealed three allelic clusters for ORT and thirteen clusters for PM. The spatio-temporal analysis revealed two significant clusters for PM, one with a 259.3 km cluster containing six cases between May and July 2018 and a 9 km cluster containing five cases between February 2019 and February 2021. No spatio-temporal clusters were found for ORT. A weak negative correlation between allelic diversity and geospatial distance was observed for ORT (r = -0.04, p < 0.01) and a weak positive correlation was observed for PM (r = 0.11, p < 0.01). This study revealed regional spatio-temporal clusters for PM in commercial turkey sites between 2018 and 2021 and provided additional insight into bacterial strain subgroups and the geographical spread of ORT and PM over time.

A decade of fuel poverty in England: A spatio-temporal analysis of needs-based targeting of domestic energy efficiency obligations

Like many forms of deprivation, fuel (or energy) poverty has been shown to concentrate spatially. However, there has been limited examination of its spatio-temporal dynamics, notwithstanding the evidence that prolonged periods without sufficient energy services in the home exacerbate negative welfare impacts. To better understand these dynamics, we evaluate how the distribution of relatively high levels of fuel poverty have changed over time across Local Authorities in England, based on government small area estimates. Common trajectories in fuel poverty estimates between 2010 and 2019 are identified using spatio-temporal quantitative methods, specifically sequence and cluster analysis. Relative energy affluence and relative energy deprivation are revealed to be spatially persistent and temporally entrenched. We use these findings to assess the targeting of the UK's energy efficiency obligation, the Energy Company Obligation (ECO). Energy efficiency obligations have become increasingly popular internationally over the last ten years as market-based policy instruments for addressing environmental and social goals. Yet, while significant concerns have been expressed about their utility in addressing fuel poverty, few recent studies have investigated these empirically. We show in England that the targeting of ECO has been disappointing: Local Authorities identified as experiencing entrenched energy deprivation typically receive less support compared to areas where fuel poverty levels fluctuate or are less severe. The paper thus (1) emphasises the importance of spatio-temporalities of fuel poverty, introducing an analytical approach suitable for understanding these dynamics in a range of national contexts; (2) highlights the limitations of energy efficiency obligations as tools for targeting interventions towards households in fuel poverty.

Spatiotemporal cluster analysis of reputable tourist accommodation in Greater Amman Municipality, Jordan

研究目的为了测试社交旅游网站 (STNS) 的用户生成内容 (UGC) 对在线声誉管理 (ORM) 的适用性, 本研究分析了知名酒店的空间聚类（基于 TripAdvisor 最佳价值指标) 和信誉良好的户外座位 (ODS) 餐厅(基于排名指标)。研究设计/方法/途径该研究使用数据挖掘技术从 TripAdvisor 获取 UGC。 基于（HDBSCAN）算法的分层基于密度的空间聚类方法用于鲁棒聚类分析。研究发现调查结果显示, 最具价值 (BV) 酒店和信誉良好的 ODS 餐厅最有可能位于人口和经济活动较为密集的城市旅游目的地的中心区及其周边地区。 BV 酒店的时空聚类分析形成了不同大小、密度和形状模式的聚类。研究原创性目前的文献扔缺乏专注于分析利用 UGC 的酒店和餐厅 (H&R) 空间聚类的研究。 因此, 本研究首次绘制并分析了知名酒店（TripAdvisor BV 指标）和餐厅（排名指标）的时空聚类模式。 因此, 本研究通过利用数据挖掘和 HDBSCAN 算法显示 H&Rs 聚类的模式变化, 为城市旅游研究做出了重要的方法论贡献。理论意义这项研究表明, 著名的 H&R 集中在历史悠久的市中心附近的地区。 这应该是对城市投资环境的应用研究的推动力。实践意义研究结果将为企业家和潜在投资者提供最具吸引力的旅游投资环境的理性指导。

Spatiotemporal clustering analysis of shared electric vehicles based on trajectory data for sustainable urban governance

Shared electric vehicles (SEVs) have emerged as a promising mode of transportation with the growth of EVs and the sharing economy. However, current challenges, such as suboptimal station planning and inflexible operation schedules, are limiting the user experience and the growth of the industry. Therefore, our study adopts the affinity propagation (AP) clustering algorithm to divide the stations into two categories: the residential station and the business office station. We then apply the Spatial-Temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN) algorithm to analyze the spatiotemporal distribution characteristics of SEVs from three aspects: hotspot area, travel volume and travel speed, based on trajectory data from the two types of stations. Results suggest that the distribution hotspot and travel volume of SEV at residential and business office stations is similar during the morning peak, concentrated in office, residential and transportation hub areas, whereas it is obviously different during the evening peak. Unlike the commuting demand of users in residential stations, users in business office stations prefer to use SEVs to travel to leisure and entertainment areas. Furthermore, we found that the driving speed of SEVs is primarily determined by actual road conditions and not significantly affected by station type. These findings provide valuable insights for SEV operators to optimize station layouts and spatial planning of vehicles during peak periods.

Spatio-temporal clustering analysis using generalized lasso with an application to reveal the spread of Covid-19 cases in Japan.

This study addressed the issue of determining multiple potential clusters with regularization approaches for the purpose of spatio-temporal clustering. The generalized lasso framework has flexibility to incorporate adjacencies between objects in the penalty matrix and to detect multiple clusters. A generalized lasso model with two penalties is proposed, which can be separated into two generalized lasso models: trend filtering of temporal effect and fused lasso of spatial effect for each time point. To select the tuning parameters, the approximate leave-one-out cross-validation (ALOCV) and generalized cross-validation (GCV) are considered. A simulation study is conducted to evaluate the proposed method compared to other approaches in different problems and structures of multiple clusters. The generalized lasso with ALOCV and GCV provided smaller MSE in estimating the temporal and spatial effect compared to unpenalized method, ridge, lasso, and generalized ridge. In temporal effects detection, the generalized lasso with ALOCV and GCV provided relatively smaller and more stable MSE than other methods, for different structure of true risk values. In spatial effects detection, the generalized lasso with ALOCV provided higher index of edges detection accuracy. The simulation also suggested using a common tuning parameter over all time points in spatial clustering. Finally, the proposed method was applied to the weekly Covid-19 data in Japan form March 21, 2020, to September 11, 2021, along with the interpretation of dynamic behavior of multiple clusters.

Epidemiological and spatiotemporal analysis of severe fever with thrombocytopenia syndrome in Eastern China, 2011–2021

BackgroundSevere fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease, which is caused by severe fever with thrombocytopenia syndrome virus (SFTSV) with high fatality. Recently, the incidence of SFTS increased obviously in Jiangsu Province. However, the systematic and complete analysis of spatiotemporal patterns and clusters coupled with epidemiological characteristics of SFTS have not been reported so far.MethodsData on SFTS cases were collected during 2011–2021. The changing epidemiological characteristics of SFTS were analyzed by adopting descriptive statistical methods. GeoDa 1.18 was applied for spatial autocorrelation analysis, and SaTScan 10.0 was used to identify spatio-temporal clustering of cases. The results were visualized in ArcMap.ResultsThe annual incidence of SFTS increased in Jiangsu Province from 2011 to 2021. Most cases (72.4%) occurred during May and August with the obvious peak months. Elderly farmers accounted for most cases, among which both males and females were susceptible. The spatial autocorrelation and spatio-temporal clustering analysis indicated that the distribution of SFTS was not random but clustered in space and time. The most likely cluster was observed in the western region of Jiangsu Province and covered one county (Xuyi county) (Relative risk = 8.18, Log likelihood ratio = 122.645, P < 0.001) located in southwestern Jiangsu Province from January 1, 2017 to December 31, 2021. The Secondary cluster also covered one county (Lishui county) (Relative risk = 7.70, Log likelihood ratio = 94.938, P < 0.001) from January 1, 2017 to December 31, 2021.ConclusionsThe annual number of SFTS cases showed an increasing tendency in Jiangsu Province from 2011 to 2021. Our study elucidated regions with SFTS clusters by means of ArcGIS in combination with spatial analysis. The results demonstrated solid evidences for the orientation of limited sanitary resources, surveillance in high-risk regions and early warning of epidemic seasons in future prevention and control of SFTS in Jiangsu Province.

Understanding Travel Behavior Change during COVID-19 Using Spatio-temporal Cluster Analysis

Understanding Travel Behavior Change during COVID-19 Using Spatio-temporal Cluster Analysis

Abstract P272: Spatial and Temporal Trends of Infective Endocarditis Hospitalizations in Kentucky, 2008-2018

Background: Infective endocarditis (IE) hospitalization rates have increased significantly in the last decade, primarily due to the opioid abuse epidemic. A North Carolina study showed an increase in intravenous drug use infective endocarditis (IDU-IE) from 0.9 to 10.9/100,000 during 2007-2017. In 2016, Kentucky ranked among the top ten states with the highest opioid-related overdose deaths. Limited data about IE trends are available for Kentucky. Objective: To examine county-level spatial and temporal trends in IE rates in Kentucky and identify significant high-rate clusters. Methods: We used the Kentucky hospital inpatient discharge data (2008-2018). We included hospitalizations for patients 18 years and older who are Kentucky residents. All IE admissions were identified using ICD-9 and ICD-10 diagnosis codes (421.0, 421.1, 421.9, 424.90, 424.91, 424.99, 112.81, I33.0, I39, I33.9, I38, A3282, and B376). IDU-IE were identified using additional ICD codes for injected illicit drug use. We used US Census data to calculate yearly hospitalization rates of IE /100,000 residents. Joinpoint regression was used to calculate annual percent change (APC). We conducted spatiotemporal cluster analysis at the county level using a Poisson model in SaTScan. Results: Throughout the study period, there were 17,787 IE hospital admissions, including 3,577 IDU-IE. Medicaid patients were more often in the IDU-IE group rather than the non-IDU-IE (71% versus 15%). The IDU-IE hospitalization rate increased from 1.2 to 27.3 / 100,000 in 2008 to 2018 (Figure). From 2013 to 2018, APC for IDU-IE hospitalizations was 71.3, (95% CI: 47.8, 98.5). The spatiotemporal scan statistics identified five statistically significant clusters. The largest cluster occurred during 2015-2018 and included 34 counties out of 120 Kentucky counties, all located in the Appalachian region of Eastern Kentucky (RR= 2.1, p&lt;0.00001). Conclusion: Our study showed increased rates of IE hospitalizations due to an increase in IDU-IE, especially in Eastern Kentucky.

Water quality assessment and pollution source apportionment using multivariate statistical techniques: a case study of the Laixi River Basin, China.

Identifying potential sources of pollution in tributaries and determining their contribution rates are critical to the treatment of water pollution in main streams. In this paper, we conducted a multivariate statistical analysis on the water quality data of 12 parameters for 3years (2018-2020) at six sampling sites in the Laixi River to qualitatively identify potential pollution sources and quantitatively calculate the contribution rates to reveal the tributaries' pollution status. Spatio-temporal cluster analysis (CA) divided 12months into two parts, corresponding to the lightly polluted season (LPS) and highly polluted season (HPS), and six sampling sites were divided into two regions, corresponding to the lightly polluted region (LPR) and highly polluted region (HPR). Principal component analysis (PCA) was used to determine the potential sources of contamination, identifying four and three potential factors in the LPS and HPS, respectively. The absolute principal component score-multiple linear regression (APCS-MLR) receptor model quantitatively analyzed the contribution rates of identified pollution sources, and the importance of the different pollution sources in LPS can be ranked as domestic sewage and industrial wastewater and breeding pollution (33.80%) > soil weathering (29.02%) > agricultural activities (20.95%) > natural influence (13.03%). HPS can be classified as agricultural cultivation (41.23%), domestic sewage and industrial wastewater and animal waste (33.19%), and natural variations (21.43%). Four potential sources were identified in LPR ranked as rural domestic sewage (31.01%) > agricultural pollution (26.82%) > industrial effluents and free-range livestock and poultry pollution (25.13%) > natural influence (14.82%). Three identified latent pollution sources in HPR were municipal sewage and industrial effluents (37.96%) > agricultural nonpoint sources and livestock and poultry wastewater (33.55%) > natural sources (25.23%). Using multivariate statistical tools to identify and quantify potential pollution sources, managers may be able to enhance water quality in tributary watersheds and develop future management plans.

Epidemiological characteristics and spatiotemporal analysis of mumps at township level in Wuhan, China, 2005-2019.

The resurgence and outbreaks of mumps occur frequently in many countries worldwide in recent years, even in countries with high vaccination coverage. In this study, a descriptive and spatiotemporal clustering analysis at the township level was conducted to explore the dynamic spatiotemporal aggregation and epidemiological characteristics of mumps in Wuhan. During 2005 and 2019, there were 40 685 cases reported in Wuhan, with an average annual morbidity of 28.11 per 100 000 populations. The morbidity showed a fluctuating tendency, and peaked in 2010 and 2018. Bimodal seasonality was found, with a large peak between May and July, and a mild peak from November to January in the following year. Male students aged 5-9-year-old were the main risk group of mumps infection. Significant global spatial auto-correlation was detected except in 2007, 2009 and 2015. The spatial and temporal scan statistics indicated that the hot-spots mainly located at the western and southern areas of Wuhan with variations almost every year. Our findings could assist the public health authorities to develop and improve targeted health strategies, and allocate health resources rationally.

Spatiotemporal clustering analysis and zonal prediction model for deformation behavior of super-high arch dams

Super-high arch dams are affected by similar environmental factors, and there is some spatial and temporal correlation among the deformation measurement points, while the deformation law varies in different parts of the arch dam. Using a single-point model for the deformation points or a single spatiotemporal model for the whole arch dam may ignore the spatiotemporal correlation of the measurement points or not consider the spatiotemporal variability between regions. In this study, a novel analytical model for multi-point deformation monitoring of super-arch dams is proposed. By constructing deformation similarity characteristics to characterize the deformation similarity degree, then using the clustering by fast search and find of density peaks method (CFSFDP) to obtain the measurement point regions with similar deformation laws, the deformation laws of different deformation regions are analyzed. Then, based on the spatiotemporal integrated autoregressive moving average model (STARIMA), a spatiotemporal deformation analysis model is constructed for the deformation of super-high arch dams. Through example analysis, the fitting and prediction performance of the model is better than that of the single measurement point model and the spatiotemporal hybrid model, which provides an efficient and convenient new method for deformation monitoring and analysis of super-high arch dams.

Spatio-Temporal Epidemiology of the Spread of African Swine Fever in Wild Boar and the Role of Environmental Factors in South Korea.

Since the first confirmation of African swine fever (ASF) in domestic pig farms in South Korea in September 2019, ASF continues to expand and most notifications have been reported in wild boar populations. In this study, we first performed a spatio-temporal cluster analysis to understand ASF spread in wild boar. Secondly, generalized linear logistic regression (GLLR) model analysis was performed to identify environmental factors contributing to cluster formation. In the meantime, the basic reproduction number (R0) for each cluster was estimated to understand the growth of the epidemic. The cluster analysis resulted in the detection of 17 spatio-temporal clusters. The GLLR model analysis identified factors influencing cluster formation and indicated the possibility of estimating ASF epidemic areas based on environmental conditions. In a scenario only considering direct transmission among wild boar, R0 ranged from 1.01 to 1.5 with an average of 1.10, while, in another scenario including indirect transmission via an infected carcass, R0 ranged from 1.03 to 4.38 with an average of 1.56. We identified factors influencing ASF expansion based on spatio-temporal clusters. The results obtained would be useful for selecting priority areas for ASF control and would greatly assist in identifying efficient vaccination areas in the future.

Epidemiological features and spatial-temporal clustering of visceral leishmaniasis in mainland China from 2019 to 2021.

BackgroundVisceral leishmaniasis (VL) is a serious vector-borne disease in central and western China. In recent years, the number of VL cases increased gradually, particularly the mountain-type zoonotic visceral leishmaniasis (MT-ZVL). This study clarified the epidemiological features and spatial-temporal clustering of VL in China between 2019 and 2021, identified the risk areas for VL transmission, and provided scientific evidence for the prevention and control of VL.Materials and methodsThe information on VL cases in 2019–2021 was collected from the Infectious Disease Reporting Information Management System of the Chinese Center for Disease Control and Prevention. The epidemiological characteristics of VL cases were analyzed. The global Moran’s I and Getis-ORD Gi* statistical data were processed for spatial autocorrelation and hotspot analysis in ESRI ArcGIS software. Also, spatial-temporal clustering analysis was conducted with the retrospective space–time permutation scan statistics.ResultsA total of 608 VL cases were reported from 2019 to 2021, with 158, 213, and 237 cases reported each year, respectively. Of the 608 cases, there were 10 cases of anthroponotic visceral leishmaniasis (AVL), 20 cases of desert-type zoonotic visceral leishmaniasis (DT-ZVL), and 578 cases of MT-ZVL. The age of VL cases was mainly distributed in the group of subjects aged ≥ 15 years. Peasants and infants were the dominant high-risk population. The incidence peak season of VL occurred between March and May. The cases were mainly distributed in Shanxi (299 cases), Shaanxi (118 cases), and Gansu (106 cases) Provinces, accounting for 86.02% (523/608) of the total reported cases in China. Spatial analysis revealed that clustering of infection is mainly located in eastern Shanxi Province and Shaanxi–Shanxi border areas, as well as southern Gansu and northern Sichuan Province. In addition, new reemergence hotspots in Shanxi, Henan, and Hebei Provinces have been detected since 2020. Spatio-temporal clustering analysis revealed an increase in the degree of infection aggregation in eastern Shanxi Province and Shaanxi–Shanxi border areas.ConclusionThe AVL and DT-ZVL were endemic at a lower level in western China, whereas MT-ZVL rebounded rapidly and showed a resurgence in historically endemic counties. The spatial-temporal clustering analysis displayed that the high-incidence areas of VL have shifted to central China, particularly in Shanxi and Shaanxi Provinces. Integrated mitigation strategies targeting high-risk populations are needed to control VL transmission in high-risk areas.