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Understanding the spatial distribution of commercial sex activities (CSA) in urban environments is important in addressing the harms against sex workers and identifying sex trafficking operations. Guided by crime pattern theory, using Census data and New York Police Department (NYPD) prostitution-related arrests data (N = 29,075) from 2010 to 2019, this study examines the "hot spots" of CSA in New York City. Local Spatial Autocorrelation Analysis (LISA) identified distinctive spatial clusters of CSA, whereas logistic regression explained their significant congregation in immigrant and racial-ethnic enclaves. The heterogeneity of clusters by boroughs portrays the convergence of activity space of sex workers, patrons, and sex business opportunities reflecting the high-demand locations of CSA and human trafficking in NYC. [ABSTRACT FROM AUTHOR]

This study jointly examines the social innovation potential and spatial competitiveness of Hungarian municipalities in terms of competitive advantage and competitive disadvantage. The primary focus of our study is on the link between the factors of social innovation potential and spatial competitive advantage. Based on the social innovation complex index of municipalities and its components, we analyse the spatial pattern of social innovation potential in the country using cluster analysis and explore the possible causes of spatial disparities based on social innovation.

Explaining diversity in tropical forests remains a challenge in community ecology. Theory tells us that species differences can stabilize communities by reducing competition, while species similarities can promote diversity by reducing fitness differences and thus prolonging the time to competitive exclusion. Combined, these processes may lead to clustering of species such that species are niche differentiated across clusters and share a niche within each cluster. Here, we characterize this partial niche differentiation in a tropical forest in Panama by measuring spatial clustering of woody plants and relating these clusters to local soil conditions. We find that species were spatially clustered and the clusters were associated with specific concentrations of soil nutrients, reflecting the existence of nutrient niches. Species were almost twice as likely to recruit in their own nutrient niche. A decision tree algorithm showed that local soil conditions correctly predicted the niche of the trees with up to 85% accuracy. Iron, zinc, phosphorus, manganese, and soil pH were among the best predictors of species clusters. [ABSTRACT FROM AUTHOR]

Objective: We aimed to identify spatial clusters of high HIV prevalence in Germany. Methods: Using nationwide outpatient claims data comprising information of about 88% of the total German population (N = 72 041 683), we examined spatial variations and spatial clusters of high HIV prevalence at the district level (N = 401). People with HIV were identified using the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD‐10 codes) B20, B22, and B24 (HIV disease) documented as 'confirmed'. Results: Among 72 041 683 people with statutory health insurance in Germany in 2021, 72 636 had diagnosed HIV, which corresponds to a prevalence of 101 per 100 000 individuals (0.10%). Of these, 56 895 were males (78%). At a district level, the HIV prevalence varied by a factor of 32 between 13 in a rural district in Bavaria and 417 per 100 000 individuals in the German capital, Berlin. The spatial autocorrelation coefficient was 0.24 (p < 0.0001, Global Moran's I). Several high‐prevalence spatial clusters of different sizes were identified, mostly located in western Germany. The largest cluster comprised eight districts in the southern part of Hesse, including the city of Frankfurt and the city of Mainz in Rhineland‐Palatinate. The second cluster consisted of four districts in North Rhine‐Westphalia, including the cities of Cologne and Düsseldorf. Two districts in southern Germany (Mannheim and Ludwigshafen) formed the third cluster. Only urban districts were observed in spatial clusters of high HIV prevalence. Conclusions: The current study identified for the first time spatial clusters with high HIV prevalence in Germany. This understanding is of particular importance when planning the general and specialized medical care of patients with HIV and to support preventive measures. [ABSTRACT FROM AUTHOR]

BackgroundIntestinal infectious diseases are a global concern in terms of morbidity, and they are closely linked to socioeconomic variables such as quality of life, weather and access to healthcare services. Despite progress in spatial analysis tools and geographic information systems in epidemiology, studies in Ecuador that evaluate temporal trends, specific geographic groups, and their correlation with socioeconomic variables are lacking. The absence of such information makes it challenging to formulate public health policies. This study sought to identify the spatial and temporal patterns of these diseases in Ecuador, along with their correlation with socioeconomic variables.MethodsIn Ecuador, the study was carried out in a continental territory, focusing on data related to intestinal infectious diseases collected from the National Institute of Statistics and Census (Instituto Nacional de Estadística y Censos) during the period from 2014 to 2019. This study involved spatial and temporal analyses using tools such as the global Moran’s index and Local Indicators of Spatial Association to identify spatial clustering patterns and autocorrelation. Additionally, correlations between morbidity rates and socioeconomic variables were examined.ResultsDuring the investigated period, Ecuador registered 209,668 cases of these diseases. Notable variations in case numbers were identified, with a 9.2% increase in 2019 compared to the previous year. The most impacted group was children under 5 years old, and the highest rates were centered in the southern and southwestern regions of the country, with Limón Indanza and Chunchi being the cantons with the highest rates, notably showing a significant increase in Limón Indanza. Additionally, there were significant correlations between morbidity rates and socioeconomic variables, school dropout rates, low birth weight, and access to water services.ConclusionThis study emphasizes the importance of considering socioeconomic variables when addressing these diseases in Ecuador. Understanding these correlations and geospatial trends can guide the development of health policies and specific intervention programs to reduce the incidence in identified high-risk areas. More specific research is needed to understand the underlying causes of variability in morbidity and develop effective prevention strategies.

Abstract Spatial colocation has been studied in many contexts including locations of urban facilities, industry entities and businesses. However, identifying colocations among a small number of facilities and establishments holds the risk of introducing false positive in that such a spatial arrangement may have occurred by chance. To account for the association between a group of facilities that frequently colocate with each other, this study proposes a two-step approach consisting of identifying statistically significant clusters of each facility type using the False Discovery Rate (FDR) controlling procedure, and subsequently measuring the colocation of those clusters with the frequent-pattern-growth (FP-growth) algorithm. Empirical analysis of 6 million business and industrial establishments across Japan suggests that 10 out of 86 industry types form clear colocations and their colocations form a multi-layered, cascading structure. The number of layers in the multi-layered structure reflect the city size and the strength of the association between the colocated clusters of industries. These patterns illustrate the utility of detecting colocation of clusters towards understanding the agglomeration of different businesses. The proposed method can be applied to other contexts that would benefit from investigations into how different types of spatial features can be linked with each other and how they form colocations.

Objectives: Examine the degree of crime concentration at micro-places across six large cities, the spatial clustering of high and low crime micro-places within cities, the presence of outliers within those clusters, and extent to which there is stability and change in micro-place classification over time. Methods: Using crime incident data gathered from six U.S. municipal police departments (Chicago, Los Angeles, New York City, Philadelphia, San Antonio, and Seattle) and aggregated to the street segment, Local Moran's I is calculated to identify statistically significant high and low crime clusters across each city and outliers within those clusters that differ significantly from their local spatial neighbors. Results: Within cities, the proportion of segments that are like their neighbors and fall within a statistically significant high or low crime cluster are relatively stable over time. For all cities, the largest proportion of street segments fell into the same classification over time (47.5% to 69.3%); changing segments were less common (4.7% to 20.5%). Changing clusters (i.e., segments that fell into both low and high clusters during the study) were rare. Outliers in each city reveal statistically significant street-to-street variability. Conclusions: The findings revealed similarities across cities, including considerable stability over time in segment classification. There were also cross-city differences that warrant further investigation, such as varying levels of spatial clustering. Understanding stable and changing clusters and outliers offers an opportunity for future research to explore the mechanisms that shape a city's spatiotemporal crime patterns to inform strategic resource allocation at smaller spatial scales. [ABSTRACT FROM AUTHOR]

Spatial colocation has been studied in many contexts including locations of urban facilities, industry entities and businesses. However, identifying colocations among a small number of facilities and establishments holds the risk of introducing false positive in that such a spatial arrangement may have occurred by chance. To account for the association between a group of facilities that frequently colocate with each other, this study proposes a two-step approach consisting of identifying statistically significant clusters of each facility type using the False Discovery Rate (FDR) controlling procedure, and subsequently measuring the colocation of those clusters with the frequent-pattern-growth (FP-growth) algorithm. Empirical analysis of 6 million business and industrial establishments across Japan suggests that 10 out of 86 industry types form clear colocations and their colocations form a multi-layered, cascading structure. The number of layers in the multi-layered structure reflect the city size and the strength of the association between the colocated clusters of industries. These patterns illustrate the utility of detecting colocation of clusters towards understanding the agglomeration of different businesses. The proposed method can be applied to other contexts that would benefit from investigations into how different types of spatial features can be linked with each other and how they form colocations. [ABSTRACT FROM AUTHOR]

Abstract Context Studies that analyze the temporal trend and spatial clustering of medical education indicators are scarce, especially in developing countries such as Brazil. This analysis is essential to subsidize more equitable policies for the medical workforce in the states and regions of Brazil. Thus, this study aimed to analyze the temporal trend and identify spatial clusters of medical education indicators in Brazil disaggregated by public and private education, states, and regions. Methods A time-series ecological study was conducted using data from the Higher Education Census of the Ministry of Education from 2010 to 2021. The study analyzed vacancy density indicators of active and former students/100,000 population, disaggregated by public and private education, 27 states, and 5 regions in Brazil. Prais-Winsten regression was used for trend analyses of indicators. Hot Spot Analysis (Getis-Ord Gi*) was used to identify spatial clusters of indicators. Results The number of medical schools increased by 102.2% between 2010 and 2021. A total of 366 medical schools offered 54,870 vacancies at the end of 2021. Vacancy density and active and former students increased significantly in the period, but this increase was greater in private institutions. Most states and regions showed an increasing trend in the indicators, with higher increase percentages in private than in public schools. Hot spot spaces changed over time, concentrated in the southeast, center-west, and north at the end of 2021. Medical education remains uneven in Brazil, with a low provision in regions with low socioeconomic development, academic structure, and health services, represented by regions in the north and northeast. Conclusions There is a growing trend in medical education indicators in Brazil, especially in the private sector. Spatial clusters were found predominantly in the southeast, center-west, and north. These results indicate the need for more equitable medical education planning between the regions.

In addition to individual practices and access to water, sanitation, and hygiene (WASH) facilities, housing conditions may also be associated with the risk of diarrhea. Our study embraced a broad approach to health determinants by looking at housing deprivation characteristics as exposures of interest and confronting the latter's spatial distribution to that of diarrheal cases. We tested the hypothesis that the risk of diarrhea in informal settlements is not only associated with WASH services, but also with inadequate dwelling characteristics, and that their spatial distributions follow similar patterns. We designed a cross-sectional study and collected primary data through georeferenced household surveys in two informal settlements in Abidjan, Côte d'Ivoire. We used local join count statistics to assess the spatial distribution of events and multiple logistic regressions to calculate adjusted odds ratios between diarrhea and exposures. A total of 567 households were enrolled. We found that constant access to basic WASH services, non-durable building materials, cooking outdoors, and water service discontinuity were associated with higher risks of diarrhea in the general population. The spatial distribution of diarrheal cases coincided with that of dwelling deprivation characteristics. We observed significant heterogeneity within the study sites regarding the spatial distribution of diarrheal cases and deprived dwellings. Along with WASH infrastructure, communities also need dignified housing to effectively prevent diarrhea. We recommend that decision-makers acknowledge a "spectrum" of deprivation within the heterogeneous universe of informal settlements, adopting a site-specific approach based on high-resolution data to address diarrhea and improve people's well-being. [ABSTRACT FROM AUTHOR]

Street dust pollution by heavy metals has raised concerns because of its potentially harmful effects on the population. It has been suggested in the literature that the spatial distribution of heavy metals in street dust is associated with the urban environment. However, robust spatial econometric analyses have not been applied yet. The study of the spatial distribution of street dust load is also often overlooked. Thus, using previously collected data in México City, a spatial econometric approach was applied to analyze the association between the built environment and street dust heavy metals pollution. Firstly, spatial clusters of street dust load were identified. Then, bivariate plots (street dust load vs. metal content) were analyzed for a broad set of metals. Log-transformed Ordinary Least Squares regression models were tested to make statistical inferences about built environment determinants of heavy metal concentrations in street dust. Finally, the non-stationary property of these regression coefficients was analyzed using geographically weighted regression models. One cluster of high dust load in the east and another with low dust load in the southwest were found. Traffic-related metals (Cr, Cu, Pb, and Zn) were identified in the bivariate plots with low R2 and relatively low residual standard error. The Cu content in street dust had significant associations with several covariates. For example, it increased in areas with factories and high car use. For the rest of the metals (Cr, Pb, and Zn), spatial patterns of regression coefficients were found and interpreted in terms of pollution indicators. [ABSTRACT FROM AUTHOR]

Context: Studies that analyze the temporal trend and spatial clustering of medical education indicators are scarce, especially in developing countries such as Brazil. This analysis is essential to subsidize more equitable policies for the medical workforce in the states and regions of Brazil. Thus, this study aimed to analyze the temporal trend and identify spatial clusters of medical education indicators in Brazil disaggregated by public and private education, states, and regions. Methods: A time-series ecological study was conducted using data from the Higher Education Census of the Ministry of Education from 2010 to 2021. The study analyzed vacancy density indicators of active and former students/100,000 population, disaggregated by public and private education, 27 states, and 5 regions in Brazil. Prais-Winsten regression was used for trend analyses of indicators. Hot Spot Analysis (Getis-Ord Gi*) was used to identify spatial clusters of indicators. Results: The number of medical schools increased by 102.2% between 2010 and 2021. A total of 366 medical schools offered 54,870 vacancies at the end of 2021. Vacancy density and active and former students increased significantly in the period, but this increase was greater in private institutions. Most states and regions showed an increasing trend in the indicators, with higher increase percentages in private than in public schools. Hot spot spaces changed over time, concentrated in the southeast, center-west, and north at the end of 2021. Medical education remains uneven in Brazil, with a low provision in regions with low socioeconomic development, academic structure, and health services, represented by regions in the north and northeast. Conclusions: There is a growing trend in medical education indicators in Brazil, especially in the private sector. Spatial clusters were found predominantly in the southeast, center-west, and north. These results indicate the need for more equitable medical education planning between the regions. [ABSTRACT FROM AUTHOR]

In recent decades, farmland markets have risen sharply due to their attractiveness as safe investment and savings allocation instruments. This growth has occurred globally at different rates due to the uneven expected returns, and potential land uses in different regions. This paper provides evidence of the convergence of Italian farmland markets between 1990 and 2019 by using data on average land prices with a geographically detailed resolution that considers both administrative and altitude aspects of territories. The analysis combines traditional economic convergence approaches with spatial data exploration techniques. The results are consistent and show that territorial disparities in Italian farmland markets tend to increase over the study period. This proves that divergence patterns reflect the well-established North-South dualism and disparities in socio-economic characteristics.

Neighborhood accessibility improvement has been recently one of the main challenges of Yazd municipality, Iran, due to population growth. In response to this problem, the municipality established a special commission, called Transportation and Commuting Commission in 2022 and decided to expand public transportation across the city. Since neighborhood accessibility assessment can help to identify low-accessible neighborhoods to allocate financial resources more efficiently, this article investigated spatial dissimilarity between neighborhoods according to three accessibility variables of population, land use, and street network in Yazd city. Since the scale of the variables was different, they were normalized to be used in the K-mean cluster method in SPSS to classify neighborhoods into five clusters regarding the global level of accessibility. Before using the K-mean cluster method, the number of clusters was determined by the hierarchical cluster analysis in SPSS. In the next step, the local Moran index was carried out in ArcGIS to examine whether there was a statistically significant difference between neighborhoods according to their level of accessibility variables. Besides, the Pearson correlation coefficient was applied in SPSS to explore the relationship between population, land use, and street network. According to the local Moran index, there was a spatio-statistically significant difference between some neighborhoods considering the global accessibility level. However, the Pearson correlation coefficient showed only a significant but negative correlation between population and land use, implying that population and land use were more influential than the street network in determining the total accessibility of neighborhoods. [ABSTRACT FROM AUTHOR]

In environmental surveillance, cluster detection of environmental black spots is of major interest due to the adverse health effects of pollutants, as well as their known synergistic effect. Thus, this paper introduces three new spatial scan statistics for multivariate functional data, applicable for detecting clusters of abnormal air pollutants concentrations measured spatially at a very fine scale in northern France in October 2021 taking into account their correlations. Mathematically, our methodology is derived from a functional multivariate analysis of variance, an adaptation of the Hotelling T 2-test statistic, and a multivariate extension of the Wilcoxon test statistic. The approaches were evaluated in a simulation study and then applied to the air pollution dataset. [ABSTRACT FROM AUTHOR]

Abstract Background Understanding geographic disparities in Coronavirus Disease 2019 (COVID-19) testing and outcomes at the local level during the early stages of the pandemic can guide policies, inform allocation of control and prevention resources, and provide valuable baseline data to evaluate the effectiveness of interventions for mitigating health, economic and social impacts. Therefore, the objective of this study was to identify geographic disparities in COVID-19 testing, incidence, hospitalizations, and deaths during the first five months of the pandemic in Florida. Methods Florida county-level COVID-19 data for the time period March-July 2020 were used to compute various COVID-19 metrics including testing rates, positivity rates, incidence risks, percent of hospitalized cases, hospitalization risks, case-fatality rates, and mortality risks. High or low risk clusters were identified using either Kulldorff’s circular spatial scan statistics or Tango’s flexible spatial scan statistics and their locations were visually displayed using QGIS. Results Visual examination of spatial patterns showed high estimates of all COVID-19 metrics for Southern Florida. Similar to the spatial patterns, high-risk clusters for testing and positivity rates and all COVID-19 outcomes (i.e. hospitalizations and deaths) were concentrated in Southern Florida. The distributions of these metrics in the other parts of Florida were more heterogeneous. For instance, testing rates for parts of Northwest Florida were well below the state median (11,697 tests/100,000 persons) but they were above the state median for North Central Florida. The incidence risks for Northwest Florida were equal to or above the state median incidence risk (878 cases/100,000 persons), but the converse was true for parts of North Central Florida. Consequently, a cluster of high testing rates was identified in North Central Florida, while a cluster of low testing rate and 1–3 clusters of high incidence risks, percent of hospitalized cases, hospitalization risks, and case fatality rates were identified in Northwest Florida. Central Florida had low-rate clusters of testing and positivity rates but it had a high-risk cluster of percent of hospitalized cases. Conclusions Substantial disparities in the spatial distribution of COVID-19 outcomes and testing and positivity rates exist in Florida, with Southern Florida counties generally having higher testing and positivity rates and more severe outcomes (i.e. hospitalizations and deaths) compared to Northern Florida. These findings provide valuable baseline data that is useful for assessing the effectiveness of preventive interventions, such as vaccinations, in various geographic locations in the state. Future studies will need to assess changes in spatial patterns over time at lower geographical scales and determinants of any identified patterns.

Abstract The infant mortality rate (IMR) is still a key indicator in a middle-income country such as Ecuador where a slightly increase up to 11.75 deaths per thousand life births has been observed in 2019. The purpose of this study is to propose and apply a prioritization method that combines clusters detection (Local Indicators of Spatial Association, LISA) and a monotonic statistic depicting time trend over 10 years (Mann–Kendall) at municipal level. Annual national databases (2010 to 2019) of live births and general deaths are downloaded from National Institute of Statistics and Censuses (INEC). The results allow identifying a slight increase in the IMR at the national level from 9.85‰ in 2014 to 11.75‰ in 2019, neonatal mortality accounted for 60% of the IMR in the last year. The LISA analysis allowed observing that the high-high clusters are mainly concentrated in the central highlands. At the local level, Piñas, Cuenca, Ibarra and Babahoyo registered the highest growth trends (0.7,1). The combination of techniques made it possible to identify eight priority counties, half of them pertaining to the highlands region, two to the coastal region and two to the Amazon region. To keep infant mortality at a low level is necessary to prioritize critical areas where public allocation of funds should be concentrated and formulation of policies.

This research aims to evaluate whether agritourism is a sustainable activity, comparing the profiles of accommodation, farmers, and accommodation with agricultural activities. Four specific objectives drive this study: (a) examining the cultural/landscape, economic, environmental, and social indicators of sustainability; (b) comparing indicators of sustainability between groups; (c) revealing whether there is a significant cross-correlation between spatial distribution and its impact on sustainability metrics; (d) discussing the significance of these factors for tourism development policies. The present study uses various techniques to study the degree of equilibrium in the distribution of the accommodation and farms in relation to their sustainable impact. To verify this, we use the global Moran's I and G (d) tests proposed by Getis and Ord. As well as analyzing local contrasts, LISA (Local Indicators of Spatial Association) and Hot Spot analysis are used for mapping. The main results reveal different profiles of sustainability, highlighting the accommodation profile with the lowest contribution to sustainable development, while profiles where the relationship between agriculture and tourism is most visible seem to perform better. In general, the main results obtained suggest that there is no direct relationship between spatial distribution and sustainability inputs, excluding the potential of endogenous resources, and suggesting the existence of imbalances in the creation of agritourism products. [ABSTRACT FROM AUTHOR]

In recent decades, farmland markets have risen sharply due to their attractiveness as safe investment and savings allocation instruments. This growth has occurred globally at different rates due to the uneven expected returns, and potential land uses in different regions. This paper provides evidence of the convergence of Italian farmland markets between 1990 and 2019 by using data on average land prices with a geographically detailed resolution that considers both administrative and altitude aspects of territories. The analysis combines traditional economic convergence approaches with spatial data exploration techniques. The results are consistent and show that territorial disparities in Italian farmland markets tend to increase over the study period. This proves that divergence patterns reflect the well-established North-South dualism and disparities in socio-economic characteristics. [ABSTRACT FROM AUTHOR]

This research is framed in the area of biomathematics and contributes to the epidemiological surveillance entities in Colombia to clarify how breast cancer mortality rate (BCM) is spatially distributed in relation to the forest area index (FA) and circulating vehicle index (CV). In this regard, the World Health Organization has highlighted the scarce generation of knowledge that relates mortality from tumor diseases to environmental factors. Quantitative methods based on geospatial data science are used with cross-sectional information from the 2018 census; it's found that the BCM in Colombia is not spatially randomly distributed, but follows cluster aggregation patterns. Under multivariate modeling methods, the research provides sufficient statistical evidence in terms of not rejecting the hypothesis that if a spatial unit has high FA and low CV, then it has significant advantages in terms of lower BCM. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the spatial clusters of high and low COVID-19 vaccination rates among children and adolescents across Brazilian municipalities and their relationship to social determinants of health. This is a nationwide population-based ecological study. We have obtained for each of the 5570 Brazilian municipalities data on the COVID-19 vaccination rate of children and adolescents by August 16, 2022, the Gini index, the social vulnerability index and the municipal human development index. A Bayesian empirical local model was used to identify fluctuations in the COVID-19 vaccination rates. Spatial clusters were identified using scan spatial statistic tests. The relationship among COVID-19 vaccination rates and social determinants of health was explored by using multiple linear regression models. Overall, 52.1% of children aged 5–11 years and 72.8% of adolescents aged 12–17 years have been fully vaccinated against COVID-19 in Brazil by mid-August 2022. There was spatial dependence on the smoothed rates for both children (I Moran 0.66; P < 0.001) and adolescent (I Moran 0.65; P < 0.001) groups. The lowest rates occurred in municipalities in the North and Northeast regions. Municipalities with a higher Gini Index, higher social vulnerability index and lower municipal human development index were more likely to have a lower COVID-19 vaccination rate for both children and adolescent groups. COVID-19 vaccination of children and adolescents was heterogeneously distributed, with spatial clusters of the lowest vaccination rates occurring mainly in municipalities with marked socio-economic disparities and social vulnerability, especially in the North and Northeast regions. [ABSTRACT FROM AUTHOR]

Hand, foot and mouth disease (HFMD) is a common infection in the world, and its epidemics result in heavy disease burdens. Over the past decade, HFMD has been widespread among children in China, with Shanxi Province being a severely affected northern province. Located in the temperate monsoon climate, Shanxi has a GDP of over 2.5 trillion yuan. It is important to have a comprehensive understanding of the basic features of HFMD in those areas that have similar meteorological and economic backgrounds to northern China. We aimed to investigate epidemiological characteristics, identify spatial clusters and predict monthly incidence of HFMD. All reported HFMD cases were obtained from the Shanxi Center for Disease Control and Prevention. Overall HFMD incidence showed a significant downward trend from 2017 to 2020, increasing again in 2021. Children aged < 5 years were primarily affected, with a high incidence of HFMD in male patients (relative risk: 1.316). The distribution showed a seasonal trend, with major peaks in June and July and secondary peaks in October and November with the exception of 2020. Other enteroviruses were the predominant causative agents of HFMD in most years. Areas with large numbers of HFMD cases were primarily in central Shanxi, and spatial clusters in 2017 and 2018 showed a positive global spatial correlation. Local spatial autocorrelation analysis showed that hot spots and secondary hot spots were concentrated in Jinzhong and Yangquan in 2018. Based on monthly incidence from September 2021 to August 2022, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) of the long short-term memory (LSTM) and seasonal autoregressive integrated moving average (SARIMA) models were 386.58 vs. 838.25, 2.25 vs. 3.08, and 461.96 vs. 963.13, respectively, indicating that the predictive accuracy of LSTM was better than that of SARIMA. The LSTM model may be useful in predicting monthly incidences of HFMD, which may provide early warnings of HFMD epidemics. [ABSTRACT FROM AUTHOR]

This paper is being written at a time when the recent pandemic, namely COVID-19, has shaken the entire world in a manner that has never been seen in modern history. The ecology, socio-economy and weak health systems make Africa an area favorable to the occurrence of various diseases and disease outbreaks. This paper explores forty-eight (48) years of disease outbreaks in the WHO African region of the World Health Organization (WHO). Twenty-five (25) Integrated Disease Surveillance and Response priority diseases were selected, and their outbreaks were described and analyzed. Using inferential spatial statistics, spatial clusters at the health district level, specifically hot spots of those outbreaks, were produced and analyzed. The Population at risk of those hot spots was estimated. Results show a consistent report of outbreaks during the selected period, with 52 outbreaks on average per year. Poliomyelitis, cholera, yellow fever, meningococcal disease and measles were the most reported outbreaks. Democratic Republic of the Congo (DRC) and Nigeria were the countries reporting the highest number of outbreaks (5 on average per year), with high number of people living in hot spots districts (85M). Despite efforts to limit their number, some disease outbreaks, such as cholera, malaria, and measles, continue to have a burden in terms of morbidity and mortality. In contrast, others, such as poliomyelitis, yellow fever and diarrhoeal disease, have shown a declining trend, and wild polio virus transmission has been eliminated in the region. Results suggest that concerted public health action may help reduce outbreaks in the region. Results can be used to inform preparedness and prevention activities. Priority public health actions should target DRC and Nigeria and identified hot spots and areas with existing risk factors within other countries.

This research is framed in the area of biomathematics and contributes to the epidemiological surveillance entities in Colombia to clarify how breast cancer mortality rate (BCM) is spatially distributed in relation to the forest area index (FA) and circulating vehicle index (CV). In this regard, the World Health Organization has highlighted the scarce generation of knowledge that relates mortality from tumor diseases to environmental factors. Quantitative methods based on geospatial data science are used with cross-sectional information from the 2018 census; it’s found that the BCM in Colombia is not spatially randomly distributed, but follows cluster aggregation patterns. Under multivariate modeling methods, the research provides sufficient statistical evidence in terms of not rejecting the hypothesis that if a spatial unit has high FA and low CV, then it has significant advantages in terms of lower BCM.

The concept of territorial cohesion has gained importance in the context of the development of the European Union’s regional policy, but definitions of the concept vary enormously. The article uses the concept of spatial development supporting economic and social aspects of cohesion and, on this basis, constructs a multidimensional index of territorial cohesion based on five dimensions (economic, demographic, infrastructural in two approaches and environmental) of the phenomenon. The measure was applied to the total population of municipalities (2175) in rural areas in Poland in the years 2005-2017. The aim of the study was to assess the territorial cohesion of municipalities in Poland in static and dynamic terms and use econometric tools to identify the potential convergence of cohesion and determine the effect of “catching up”, according to Jeffrey G. Williamson [1965], where municipalities with a higher level of cohesion are followed by municipalities with a lower level. The results of previous studies conducted at different regional levels indicate an increase in income polarization in rural areas and the creation of islands of convergence, where the regions with the highest and lowest wealth become similar intra-group but not inter-group. The results indicate a decrease in relative territorial cohesion in rural areas in Poland in 2005-2017 at a municipality level and a tendency to the formation of spatial clusters of entities with similar levels of the characteristic. They do not allow to confirm the view of convergence of cohesion between entities with different levels of the characteristic.

The infant mortality rate (IMR) is still a key indicator in a middle-income country such as Ecuador where a slightly increase up to 11.75 deaths per thousand life births has been observed in 2019. The purpose of this study is to propose and apply a prioritization method that combines clusters detection (Local Indicators of Spatial Association, LISA) and a monotonic statistic depicting time trend over 10 years (Mann-Kendall) at municipal level. Annual national databases (2010 to 2019) of live births and general deaths are downloaded from National Institute of Statistics and Censuses (INEC). The results allow identifying a slight increase in the IMR at the national level from 9.85‰ in 2014 to 11.75‰ in 2019, neonatal mortality accounted for 60% of the IMR in the last year. The LISA analysis allowed observing that the high-high clusters are mainly concentrated in the central highlands. At the local level, Piñas, Cuenca, Ibarra and Babahoyo registered the highest growth trends (0.7,1). The combination of techniques made it possible to identify eight priority counties, half of them pertaining to the highlands region, two to the coastal region and two to the Amazon region. To keep infant mortality at a low level is necessary to prioritize critical areas where public allocation of funds should be concentrated and formulation of policies. [ABSTRACT FROM AUTHOR]

The transmission of coronavirus disease‐2019 (COVID‐19) epidemic is a global emergency, which is worsened by the genetic mutations of SARS‐CoV‐2. However, till date, few statistical studies have researched the COVID‐19 spread patterns in terms of the variant cases. Hence, this paper aims to explore the associated risk factors of Delta variant, the most contagious strain of COVID‐19. The study collected the state‐level COVID‐19 Delta variant cases in the United States during a 12‐week period and included potential environmental, socioeconomic, and public prevention factors as independent variables. Instead of regarding the covariate effects as constant, this paper proposes a flexible Bayesian hierarchical model with spatio‐temporally varying coefficients to account for data heterogeneity. The method enables us to cluster the states into distinctive groups based on the temporal trends of the coefficients and simultaneously identify significant risk factors for each cluster. The findings contribute novel insight into the dynamics of covariate effects on the COVID‐19 Delta variant over space and time, which could help the government develop targeted prevention measures for vulnerable regions based on the selected risk factors. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to estimate the crash response times in rural and urban counties in the United States, their association with county‐level crash fatalities, and identify spatial clusters of crash fatalities across the United States. Methods: We analyzed data from the Fatality Analysis Reporting System (2010‐2019). Data were aggregated at the county level across the contiguous United States. The selected counties (n = 3,108) were categorized as rural, micropolitan‐urban, or metropolitan‐urban using the 2013 rural‐urban commuting area codes. The predictor variable was crash response time, and the outcome variable was county‐level crash fatalities. Crash and county characteristics were used as potential confounders. We performed a spatial negative binomial regression analysis and reported the rate ratios of crash fatalities. We estimated the crude and adjusted fatality rates across all counties and identified clusters of crash fatalities across the United States. Findings: As one migrates from urban to rural areas, crash response times became significantly increasingly longer. The Emergency Medical Service (EMS) notification to scene arrival time was most predictive of crash fatalities. A minute increase in the EMS notification to scene arrival time was associated with a 1%, 2%, and 5% increased fatality rate ratio in rural, micropolitan‐urban, and metropolitan‐urban counties, respectively. Although crash fatalities were lower in rural counties, the crash fatality rate was 3‐fold higher in rural counties compared to metropolitan‐urban counties. Significant clusters of crash fatality rates were heterogeneously distributed across the United States. Conclusion: Reducing crash response time may contribute to reducing crash fatalities across the United States. [ABSTRACT FROM AUTHOR]

Abstract Background The over-distributed pattern of malaria transmission has led to attempts to define malaria “hotspots” that could be targeted for purposes of malaria control in Africa. However, few studies have investigated the use of routine health facility data in the more stable, endemic areas of Africa as a low-cost strategy to identify hotspots. Here the objective was to explore the spatial and temporal dynamics of fever positive rapid diagnostic test (RDT) malaria cases routinely collected along the Kenyan Coast. Methods Data on fever positive RDT cases between March 2018 and February 2019 were obtained from patients presenting to six out-patients health-facilities in a rural area of Kilifi County on the Kenyan Coast. To quantify spatial clustering, homestead level geocoded addresses were used as well as aggregated homesteads level data at enumeration zone. Data were sub-divided into quarterly intervals. Kulldorff’s spatial scan statistics using Bernoulli probability model was used to detect hotspots of fever positive RDTs across all ages, where cases were febrile individuals with a positive test and controls were individuals with a negative test. Results Across 12 months of surveillance, there were nine significant clusters that were identified using the spatial scan statistics among RDT positive fevers. These clusters included 52% of all fever positive RDT cases detected in 29% of the geocoded homesteads in the study area. When the resolution of the data was aggregated at enumeration zone (village) level the hotspots identified were located in the same areas. Only two of the nine hotspots were temporally stable accounting for 2.7% of the homesteads and included 10.8% of all fever positive RDT cases detected. Conclusion Taking together the temporal instability of spatial hotspots and the relatively modest fraction of the malaria cases that they account for; it would seem inadvisable to re-design the sub-county control strategies around targeting hotspots.

Abstract Background Women's cancers, represented by breast and gynecologic cancers, are emerging as a significant threat to women's health, while previous studies paid little attention to the spatial distribution of women's cancers. This study aims to conduct a spatio-temporal epidemiology analysis on breast, cervical and ovarian cancers in China, thus visualizing and comparing their epidemiologic trends and spatio-temporal changing patterns. Methods Data on the incidence and mortality of women’s cancers between January 2010 and December 2015 were obtained from the National Cancer Registry Annual Report. Linear tests and bar charts were used to visualize and compare the epidemiologic trends. Two complementary spatial statistics (Moran’s I statistics and Kulldorff’s space–time scan statistics) were adopted to identify the spatial–temporal clusters. Results The results showed that the incidence and mortality of breast cancer displayed slow upward trends, while that of cervical cancer increase dramatically, and the mortality of ovarian cancer also showed a fast increasing trend. Significant differences were detected in incidence and mortality of breast, cervical and ovarian cancer across east, central and west China. The average incidence of breast cancer displayed a high-high cluster feature in part of north and east China, and the opposite traits occurred in southwest China. In the meantime, the average incidence and mortality of cervical cancer in central China revealed a high-high cluster feature, and that of ovarian cancer in northern China displayed a high-high cluster feature. Besides, the anomalous clusters were also detected based on the space–time scan statistics. Conclusion Regional differences were detected in the distribution of women’s cancers in China. An effective response requires a package of coordinated actions that vary across localities regarding the spatio-temporal epidemics and local conditions.

This study aims to investigate the effects of urban and forest areas measured in three dimensions on seasonal temperature over forty years in South Korean cities. We measure the urban and forest areas at the city, neighborhood, and spatially clustered levels in four periods every ten years. Using Hot Spot Analysis (Getis-Ord Gi*), this study detects the spatially clustered urban and forest areas. We establish a multilevel regression model to explore the relationship between urban and forest areas measured in three dimensions, as well as seasonal temperatures. The study shows that while spatially clustered urban and forest areas have consistent associations with the four seasonal temperatures, urban and forest areas at the city scale have different associations with the seasonal temperature, depending on the season. When spatially clustered urban areas increase by 10 km2, four seasonal temperatures increase by about 0.0016–0.0067 Celsius degree (°C); on the other hand, when spatially clustered forest areas increase by 10 km2, four seasonal temperatures decrease by about 0.0001–0.0016 °C. At the neighborhood level, urban and forest areas are negatively associated with the four seasonal temperatures. The results of this study can be utilized by urban planners and policymakers to establish land use planning or policy by providing evidence of whether land use plans should be established and at what scales to manage regional thermal environments. To alleviate seasonal warming, we recommend increasing forest areas at the neighborhood and spatially clustered levels and controlling the size of spatially clustered urban areas. • Spatially clustered urban areas are positively associated with seasonal temperatures. • Spatially clustered forest areas are negatively associated with seasonal temperatures. • To alleviate regional warming, interconnected forested areas are recommended. • Regulating the expansion of urban areas is also recommended. [ABSTRACT FROM AUTHOR]

Rabies, a globally distributed and highly lethal zoonotic neglected tropical disease, has a significant impact in South America. In Ecuador, animal rabies cases are primarily linked to livestock, and hematophagous bats play a crucial role in disease transmission. This study aims to identify temporal trends, spatial patterns, and risk factors for animal rabies in Ecuador between 2014 and 2019. Epidemiological survey reports from the official Animal Rabies Surveillance Program of the Phyto and Zoosanitary Regulation and Control Agency of Ecuador (AGROCALIDAD) were used. The Animal Rabies Surveillance Program from AGROCALIDAD consists of an official passive surveillance program that receives reports from farmers or individuals (both trained or untrained) who have observed animals with neurological clinical signs and lesions compatible with bat bites, or who have seen or captured bats on their farms or houses. Once this report is made, AGROCALIDAD personnel is sent for field inspection, having to confirm the suspicion of rabies based on farm conditions and compatibility of signs. AGROCALIDAD personnel collect samples from all suspicious animals, which are further processed and analyzed using the Direct Fluorescent Antibody (DFA) test for rabies confirmatory diagnosis. In this case, study data comprised 846 bovine farms (with intra-farm sample sizes ranging from 1 to 16 samples) located in different ecoregions of Ecuador; out of these, 397 (46.93%) farms tested positive for animal rabies, revealing six statistically significant spatial clusters. Among these clusters, three high-risk areas were identified in the southeast of Ecuador. Seasonality was confirmed by the Ljung-Box test for both the number of cases (p < 0.001) and the positivity rate (p < 0.001). The Pacific Coastal lowlands and Sierra regions showed a lower risk of positivity compared to Amazonia (OR = 0.529; 95% CI = 0.318 – 0.883; p = 0.015 and OR = 0.633; 95% CI = 0.410 – 0.977; p = 0.039, respectively). The breeding of non-bovine animal species demonstrated a lower risk of positivity to animal rabies when compared to bovine (OR = 0.145; 95% CI = 0.062 – 0.339; p < 0.001). Similarly, older animals exhibited a lower risk (OR = 0.974; 95% CI = 0.967 – 0.981; p < 0.001). Rainfall during the rainy season was also found to decrease the risk of positivity to animal rabies (OR = 0.996; 95% CI = 0.995 – 0.998; p < 0.001). This study underscores the significance of strengthening the national surveillance program for the prevention and control of animal rabies in Ecuador and other countries facing similar epidemiological, social, and geographical circumstances. • Seasonality is confirmed in the presentation of animal rabies in Ecuador. • High-risk clusters of animal rabies were detected in the southeast of Ecuador. • The forecast of frequency measures indicates a slight increase for the period of 2020–2023. • Risk factors were detected for environmental and livestock factors. [ABSTRACT FROM AUTHOR]

This research aims to evaluate whether agritourism is a sustainable activity, comparing the profiles of accommodation, farmers, and accommodation with agricultural activities. Four specific objectives drive this study: (a) examining the cultural/landscape, economic, environmental, and social indicators of sustainability; (b) comparing indicators of sustainability between groups; (c) revealing whether there is a significant cross-correlation between spatial distribution and its impact on sustainability metrics; (d) discussing the significance of these factors for tourism development policies. The present study uses various techniques to study the degree of equilibrium in the distribution of the accommodation and farms in relation to their sustainable impact. To verify this, we use the global Moran’s I and G (d) tests proposed by Getis and Ord. As well as analyzing local contrasts, LISA (Local Indicators of Spatial Association) and Hot Spot analysis are used for mapping. The main results reveal different profiles of sustainability, highlighting the accommodation profile with the lowest contribution to sustainable development, while profiles where the relationship between agriculture and tourism is most visible seem to perform better. In general, the main results obtained suggest that there is no direct relationship between spatial distribution and sustainability inputs, excluding the potential of endogenous resources, and suggesting the existence of imbalances in the creation of agritourism products.

This study statistically identified the localised association between socioeconomic conditions and the coronavirus disease 2019 (COVID-19) incidence rate in Thailand on the basis of the 1,727,336 confirmed cases reported nationwide during the first major wave of the pandemic (March-May 2020) and the second one (July 2021-September 2021). The nighttime light (NTL) index, formulated using satellite imagery, was used as a provincial proxy of monthly socioeconomic conditions. Local indicators of spatial association statistics were applied to identify the localised bivariate association between COVID-19 incidence rate and the year-on-year change of NTL index. A statistically significant negative association was observed between the COVID-19 incidence rate and the NTL index in some central and southern provinces in both major pandemic waves. Regression analyses were also conducted using the spatial lag model (SLM) and the spatial error model (SEM). The obtained slope coefficient, for both major waves of the pandemic, revealed a statistically significant negative association between the year-on-year change of NTL index and COVID-19 incidence rate (SLM: coefficient= −0.0078 and −0.0064 with P

Background: By identifying the local foci and clusters of diseases, it can be hoped to reduce the incidence and deaths of the disease by making the necessary interventions. It was aimed to detect possible colorectal cancer incidence clusters using spatial analyses at point-level data at small census units in Arak, Iran from 2009 to 2014. Methods: In this ecologic study, recorded data on colorectal cancer in Arak have been collected from the Arak Cancer Registry. All records have been evaluated one-by-one using various methods to detect and resolve any probable error events or duplicated records. Then SaTScan software was used to explore spatial clusters. Discrete Poisson-based Probability Model was used to analyze the clusters. Results: 398 incidence cases of colorectal cancer was included. Three spatial clusters of colorectal cancer using individual geocodes were detected. The most high-risk cluster was located in the near of south highway of Arak, the highway with transit road of heavy and light vehicles(p=0.0004). The second significant high-risk cluster was a district located in the vast part of the center of Arak. The identified third high-risk cluster was an area in suburb of Arak, Proximity of Farmahin-Arak road and Northern highway. Conclusion: This study has identified three important clusters for the high incidence of colorectal cancer in Arak. Residents of these clusters need to be screened and trained to learn a healthy lifestyle. It is recommended that analytical studies at the individual level be designed and implemented to find the risk factors for colorectal cancer.

Copyright of Polish Statistician / Wiadomości Statystyczne is the property of State Treasury - Statistics Poland and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Background In malaria endemic areas, identifying spatio-temporal hotspots is becoming an important element of innovative control strategies targeting transmission bottlenecks. The aim of this work was to describe the spatio-temporal variation of malaria hotspots in central Senegal and to identify the meteorological, environmental, and preventive factors that influence this variation. Methods This study analysed the weekly incidence of malaria cases recorded from 2008 to 2012 in 575 villages of central Senegal (total population approximately 500,000) as part of a trial of seasonal malaria chemoprevention (SMC). Data on weekly rainfall and annual vegetation types were obtained for each village through remote sensing. The time series of weekly malaria incidence for the entire study area was divided into periods of high and low transmission using change-point analysis. Malaria hotspots were detected during each transmission period with the SaTScan method. The effects of rainfall, vegetation type, and SMC intervention on the spatio-temporal variation of malaria hotspots were assessed using a General Additive Mixed Model. Results The malaria incidence for the entire area varied between 0 and 115.34 cases/100,000 person weeks during the study period. During high transmission periods, the cumulative malaria incidence rate varied between 7.53 and 38.1 cases/100,000 person-weeks, and the number of hotspot villages varied between 62 and 147. During low transmission periods, the cumulative malaria incidence rate varied between 0.83 and 2.73 cases/100,000 person-weeks, and the number of hotspot villages varied between 10 and 43. Villages with SMC were less likely to be hotspots (OR = 0.48, IC95%: 0.33–0.68). The association between rainfall and hotspot status was non-linear and depended on both vegetation type and amount of rainfall. The association between village location in the study area and hotspot status was also shown. Conclusion In our study, malaria hotspots varied over space and time according to a combination of meteorological, environmental, and preventive factors. By taking into consideration the environmental and meteorological characteristics common to all hotspots, monitoring of these factors could lead targeted public health interventions at the local level. Moreover, spatial hotspots and foci of malaria persisting during LTPs need to be further addressed. Trial registration The data used in this work were obtained from a clinical trial registered on July 10, 2008 at www.clinicaltrials.gov under NCT00712374 .

Abstract Background Bacterial meningitis remains a major threat for the population of the meningitis belt. Between 2004 and 2009, in the countries of this belt, more than 200,000 people were infected with a 10% mortality rate. However, for almost 20 years, important meningitis epidemics are also reported outside this belt. Research is still very poorly developed in this part of the word like in the Democratic Republic of Congo (DRC), which experiences recurrent epidemics. This article describes for the first time the spatio-temporal patterns of meningitis cases and epidemics in DRC, in order to provide new insights for surveillance and control measures. Methods Based on weekly suspected cases of meningitis (2000–2012), we used time-series analyses to explore the spatio-temporal dynamics of the disease. We also used both geographic information systems and geostatistics to identify spatial clusters of cases. Both using conventional statistics and the Cleveland’s algorithm for decomposition into general trend, seasonal and residuals, we searched for the existence of seasonality. Results We observed a low rate of biological confirmation of cases (11%) using soluble antigens search, culture and PCR. The main strains found are Streptococcus pneumoniae, Haemophilus influenzae and Neisseria meningitidis (A and C) serogroups. We identified 8 distinct spatial clusters, located in the northeastern and southeastern part of DRC, and in the capital city province, Kinshasa. A low seasonal trend was observed with higher incidence and attack rate of meningitis during the dry season, with a high heterogeneity in seasonal patterns occurring across the different districts and regions of DRC. Conclusion Despite challenges related to completeness of data reporting, meningitis dynamics shows weak seasonality in DRC. This tends to suggest that climatic, environmental factors might be less preponderant in shaping seasonal patterns in central Africa. The characterization of 8 distinct clusters of meningitis could be used for a better sentinel meningitis surveillance and optimization of vaccine strategy in DRC. Improving biological monitoring of suspected cases should be a priority for future eco-epidemiological studies to better understand the emergence and spread of meningitis pathogens, and the potential ecological, environmental drivers of this disease.

Ning Wang,1,2 Shu Cong,1 Jing Fan,1 Heling Bao,1 Baohua Wang,1 Ting Yang,3 Yajing Feng,1 Yang Liu,4 Linhong Wang,1 Chen Wang,3,5 Wenbiao Hu,2 Liwen Fang1 1National Center for Chronic Non-Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 100050, People’s Republic of China; 2School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD 4059, Australia; 3Center of Respiratory Medicine, China–Japan Friendship Hospital, Beijing, People’s Republic of China; 4Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA; 5Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of ChinaCorrespondence: Liwen FangNational Center for Chronic Non-Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, 27 Nanwei Road, Xicheng District, Beijing 100050, People’s Republic of ChinaTel +86 135 5239 3376Fax +86 010 6304 2350Email fangliwen@ncncd.chinacdc.cnWenbiao HuSchool of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland 4059, AustraliaTel/Fax +61 7 3138 5724Email w2.hu@qut.edu.auPurpose: COPD prevalence has rapidly increased in China, but the geographical disparities in COPD prevalence remain largely unknown. This study aimed to assess city-level disparities in COPD prevalence and identify the relative importance of COPD related risk factors in mainland China.Patients and Methods: A nationwide cross-sectional study of COPD recruited 66,752 adults across the mainland China between 2014 and 2015. Patients with COPD were ascertained by a post-bronchodilator pulmonary function test. We estimated the city-specific prevalence of COPD by spatial kriging interpolation method. We detected spatial clusters with a significantly higher prevalence of COPD by spatial scan statistics. We determined the relative importance of COPD associated risk factors by a nonparametric and nonlinear classification and regression tree (CART) model.Results: The three spatial clusters with the highest prevalence of COPD were located in parts of Sichuan, Gansu, and Shaanxi, etc. (relative risks (RRs)) ranging from 1.55 (95% CI 1.55– 1.56) to 1.33 (95% CI 1.33– 1.33)). CART showed that advanced age (≥ 60 years) was the most important factor associated with COPD in the overall population, followed by smoking. We estimated that there were about 28.5 million potentially avoidable cases of COPD among people aged 40 or older if they never smoked. PM2.5 was an important associated risk factor for COPD in the north, northeast, and southwest of China. After adjusting for age and smoking, the spatial cluster with the highest prevalence shifted to most of Sichuan, Gansu, Qinghai, and Ningxia, etc. (RR 1.65 (95% CI 1.63– 1.67)).Conclusion: The spatial clusters of COPD at the city level and regionally varied important risk factors for COPD would help develop tailored interventions for COPD in China. After adjusting for the main risk factors, the spatial clusters of COPD shifted, indicating that there would be other potential risk factors for the remaining clusters which call for further studies.Keywords: COPD, spatial clusters, kriging, classification and regression tree

Background: Intestinal infectious diseases are a global concern in terms of morbidity, and they are closely linked to socioeconomic variables such as quality of life, weather and access to healthcare services. Despite progress in spatial analysis tools and geographic information systems in epidemiology, studies in Ecuador that evaluate temporal trends, specific geographic groups, and their correlation with socioeconomic variables are lacking. The absence of such information makes it challenging to formulate public health policies. This study sought to identify the spatial and temporal patterns of these diseases in Ecuador, along with their correlation with socioeconomic variables., Methods: In Ecuador, the study was carried out in a continental territory, focusing on data related to intestinal infectious diseases collected from the National Institute of Statistics and Census (Instituto Nacional de Estadística y Censos) during the period from 2014 to 2019. This study involved spatial and temporal analyses using tools such as the global Moran's index and Local Indicators of Spatial Association to identify spatial clustering patterns and autocorrelation. Additionally, correlations between morbidity rates and socioeconomic variables were examined., Results: During the investigated period, Ecuador registered 209,668 cases of these diseases. Notable variations in case numbers were identified, with a 9.2% increase in 2019 compared to the previous year. The most impacted group was children under 5 years old, and the highest rates were centered in the southern and southwestern regions of the country, with Limón Indanza and Chunchi being the cantons with the highest rates, notably showing a significant increase in Limón Indanza. Additionally, there were significant correlations between morbidity rates and socioeconomic variables, school dropout rates, low birth weight, and access to water services., Conclusion: This study emphasizes the importance of considering socioeconomic variables when addressing these diseases in Ecuador. Understanding these correlations and geospatial trends can guide the development of health policies and specific intervention programs to reduce the incidence in identified high-risk areas. More specific research is needed to understand the underlying causes of variability in morbidity and develop effective prevention strategies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Lalangui, Cotera-Mantilla, Sánchez-Murillo, Carrera-Alvarez, Duque-Cuasapaz and Quentin.)

Copyright of Annals of the Polish Association of Agricultural & Agribusiness Economists is the property of Polish Association of Agricultural & Agribusiness Economists and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Motivated by the need to analyze readily available data collected in space and time, especially in environmental sciences, we propose a parsimonious spatiotemporal model for time series data on a spatial grid. In essence, our model is a vector autoregressive model that utilizes the spatial structure to achieve parsimony of autoregressive matrices at two levels. The first level ensures the sparsity of the autoregressive matrices using a lagged-neighborhood scheme. The second level performs a spatial clustering of the nonzero autoregressive coefficients such that within some subregions, nearby locations share the same autoregressive coefficients while across different subregions the coefficients may have distinct values. The model parameters are estimated using the penalized maximum likelihood with an adaptive fused Lasso penalty. The estimation procedure can be tailored to accommodate the need and prior knowledge of a modeler. Performance of the proposed estimation algorithm is examined in a simulation study. Our method gives reliable estimation results that are interpretable and especially useful to identify geographical subregions, within each of which, the time series have similar dynamical behavior with homogeneous autoregressive coefficients. We apply our model to a wind speed time series dataset generated from a climate model over Saudi Arabia to illustrate its power in explaining the dynamics by the spatially structured coefficients. Moreover, the estimated model can be useful for building stochastic weather generators as an approximation of the computationally expensive climate model. [ABSTRACT FROM AUTHOR]

The efforts towards effective control of the COVID-19 pandemic may affect the incidence of dengue. This study aimed to investigate temporal variations and spatial clusters of dengue in Thailand before and during the COVID-19 pandemic. Reported dengue cases before (2011–2019) and during (2020–2021) the COVID-19 pandemic were obtained from the national disease surveillance datasets. The temporal variations were analyzed using graphics, a seasonal trend decomposition procedure based on Loess, and Poisson regression. A seasonal ARIMA model was used to forecast dengue cases. Spatial clusters were investigated using the local indicators of spatial associations (LISA). The cyclic pattern showed that the greatest peak of dengue cases likely changed from every other year to every two or three years. In terms of seasonality, a notable peak was observed in June before the pandemic, which was delayed by one month (July) during the pandemic. The trend for 2011–2021 was relatively stable but dengue incidence decreased dramatically by 7.05% and 157.80% on average in 2020 and 2021, respectively. The forecasted cases in 2020 were slightly lower than the reported cases (2.63% difference), whereas the forecasted cases in 2021 were much higher than the actual cases (163.19% difference). The LISA map indicated 5 to 13 risk areas or hotspots of dengue before the COVID-19 pandemic compared to only 1 risk area during the pandemic. During the COVID-19 pandemic, dengue incidence sharply decreased and was lower than forecasted, and the spatial clusters were much lower than before the pandemic.