Search

Dialysis patients experience frequent hospitalizations and a higher mortality rate compared to other Medicare populations, in whom hospitalizations are a major contributor to morbidity, mortality, and healthcare costs. Patients also typically remain on dialysis for the duration of their lives or until kidney transplantation. Hence, there is growing interest in studying the spatiotemporal trends in the correlated outcomes of hospitalization and mortality among dialysis patients as a function of time starting from transition to dialysis across the United States Utilizing national data from the United States Renal Data System (USRDS), we propose a novel multivariate spatiotemporal functional principal component analysis model to study the joint spatiotemporal patterns of hospitalization and mortality rates among dialysis patients. The proposal is based on a multivariate Karhunen-Loéve expansion that describes leading directions of variation across time and induces spatial correlations among region-specific scores. An efficient estimation procedure is proposed using only univariate principal components decompositions and a Markov Chain Monte Carlo framework for targeting the spatial correlations. The finite sample performance of the proposed method is studied through simulations. Novel applications to the USRDS data highlight hot spots across the United States with higher hospitalization and/or mortality rates and time periods of elevated risk.

Fungi are among the most diverse and ecologically important kingdoms in life. However, the distributional ranges of fungi remain largely unknown as do the ecological mechanisms that shape their distributions1,2. To provide an integrated view of the spatial and seasonal dynamics of fungi, we implemented a globally distributed standardized aerial sampling of fungal spores3. The vast majority of operational taxonomic units were detected within only one climatic zone, and the spatiotemporal patterns of species richness and community composition were mostly explained by annual mean air temperature. Tropical regions hosted the highest fungal diversity except for lichenized, ericoid mycorrhizal and ectomycorrhizal fungi, which reached their peak diversity in temperate regions. The sensitivity in climatic responses was associated with phylogenetic relatedness, suggesting that large-scale distributions of some fungal groups are partially constrained by their ancestral niche. There was a strong phylogenetic signal in seasonal sensitivity, suggesting that some groups of fungi have retained their ancestral trait of sporulating for only a short period. Overall, our results show that the hyperdiverse kingdom of fungi follows globally highly predictable spatial and temporal dynamics, with seasonality in both species richness and community composition increasing with latitude. Our study reports patterns resembling those described for other major groups of organisms, thus making a major contribution to the long-standing debate on whether organisms with a microbial lifestyle follow the global biodiversity paradigms known for macroorganisms4,5.

Global biodiversity gradients are generally expected to reflect greater species replacement closer to the equator. However, empirical validation of global biodiversity gradients largely relies on vertebrates, plants, and other less diverse taxa. Here we assess the temporal and spatial dynamics of global arthropod biodiversity dynamics using a beta-diversity framework. Sampling includes 129 sampling sites whereby malaise traps are deployed to monitor temporal changes in arthropod communities. Overall, we encountered more than 150,000 unique barcode index numbers (BINs) (i.e. species proxies). We assess between site differences in community diversity using beta-diversity and the partitioned components of species replacement and richness difference. Global total beta-diversity (dissimilarity) increases with decreasing latitude, greater spatial distance and greater temporal distance. Species replacement and richness difference patterns vary across biogeographic regions. Our findings support long-standing, general expectations of global biodiversity patterns. However, we also show that the underlying processes driving patterns may be regionally linked.

In the American regions, Brazil accounts for 97% of visceral leishmaniasis (VL) cases, with a case fatality rate of approximately 10%. This study aimed to investigate the VL mortality distribution in Brazil and identify high-priority and high-risk areas for intervention strategies. This was an ecological study that analysed the spatial–temporal patterns of VL mortality in Brazilian municipalities. Age-standardised VL mortality rates from the Global Burden of Disease study from 2001 to 2018 were used. The distribution of mortality in the municipalities was assessed, and subsequently the Local Index of Spatial Autocorrelation (LISA) analysis was conducted to identify contiguous areas with high mortality rates. Scan analysis identified clusters of high spatial–temporal risks. The highest mortality rates and clusters were in municipalities located in the Northeast region and in the states of Tocantins and Roraima (North region), Mato Grosso do Sul (Central-West region), and Minas Gerais (Southeast region). According to LISA, there was an increase in the number of municipalities classified as high priority from the first 3-year period (n = 434) to the last 3-year period (n = 644). The spatio-temporal analysis identified 21 high-risk clusters for VL mortality. Areas with a high risk of VL mortality should prioritise preventing transmission, invest in early diagnosis and treatment, and promote the training of healthcare professionals. [ABSTRACT FROM AUTHOR]

Objective: To analyse hospital case fatality and mortality related to Chagas disease (CD) in Brazil, 2000–2019. Method: This is a mixed ecological study with spatial and temporal trends, based on national population data from the Brazilian Ministry of Health - hospital admissions (HA) and death certificates (DC). Records with CD as a primary or secondary cause of death in HA and/or as an underlying or associated cause of death in DC were evaluated. Temporal trends were analysed by Joinpoint regression and the spatial distribution of age- and gender-adjusted rates, spatial moving averages, and standardized morbidity ratios. Results: There were a total of 4,376 HA due to CD resulting in death in Brazil, with a hospital case fatality rate of 0.11/100,000 inhabitants. The Southeast region had the highest rate (63.9%, n = 2,796; 0.17/100,000 inhabitants). The general trend for this indicator in Brazil is upwards (average annual percentage change [AAPC] 7.5; 95% confidence interval [CI] 5.3 to 9.9), with increases in the North, Northeast and Southeast regions. During the same period 122,275 deaths from CD were registered in DC, with a mortality rate of 3.14/100,000 inhabitants. The highest risk of CD-related death was found among men (relative risk [RR] 1.27) and Afro-Brazilians (RR 1.63). There was a downward trend in CD mortality in the country (AAPC − 0.7%, 95%CI -0.9 to -0.5), with an increase in the Northeast region (AAPC 1.1%, 95%CI 0.6 to 1.6). Municipalities with a very high Brazilian Deprivation Index tended to show an increase in mortality (AAPC 2.1%, 95%CI 1.6 to 2.7), while the others showed a decrease. Conclusion: Hospital case fatality and mortality due to CD are a relevant public health problem in Brazil. Differences related to gender, ethnicity, and social vulnerability reinforce the need for comprehensive care, and to ensure equity in access to health in the country. Municipalities, states, and regions with indicators that reveal higher morbidity and mortality need to be prioritized. [ABSTRACT FROM AUTHOR]

Wildfires are an intrinsic and vital driving factor in the Miombo ecosystem. Understanding fire regimes in Miombo is crucial for its ecological sustainability. Miombo is dominant in Central Mozambique, having one of the highest fire incidences in the country. This study evaluated the spatio-temporal patterns of fire regimes (intensity, seasonality, frequency and fire return interval) in the LevasFlor Forest Concession (LFC), Central Mozambique using remotely sensed data from 2001 to 2022. We conducted hotspot spatial statistics using the Getis-Ord Gi* method to assess fire distribution and patterns. The results revealed that 88% of the study area was burnt at least once from 2001 to 2022, with an average burned area of 9733 ha/year (21% of LFC's total area). Fires were more likely to occur (74.4%) in open and deciduous Miombo types. A total of 84% of the studied area, burned in a range of 4 to 22 years of fire return interval (FRI) over the 21 assessed. Only 16% of the area was affected by high to very high FRI (1 to 4 years), with an average FRI of 4.43 years. Generally, fires are more frequent and intense in September and October. These results highlight the usefulness of remote sensing in evaluating long-term spatiotemporal fire trends for effective fire management strategies and control measures in African savanna ecosystems. [ABSTRACT FROM AUTHOR]

Background: Breastfeeding offers numerous benefits for infants, mothers, and the community, making it the best intervention for reducing infant mortality and morbidity. The World Health Organization (WHO) recommends initiating breastfeeding within one hour after birth and exclusively breastfeeding for the first six months. This study investigated the trend, spatio-temporal variation, and determinants of spatial clustering of early initiation of breastfeeding (EIBF) and exclusive breastfeeding (EBF) in Ethiopia from 2011 to 2019. Methods: Data from the Ethiopian Demographic and Health Survey (EDHS), which was conducted in 2011, 2016, and 2019, were analyzed utilizing a weighted sample of 10,616 children aged 0–23 years for EIBF and 2,881 children aged 0–5 months for EBF. Spatial autocorrelation analysis was used to measure whether EIBF and EBF were dispersed, clustered, or randomly distributed and Kriging interpolation was employed to predict the outcome variables in the unmeasured areas. Spatial scan statistics were used to identify spatial clusters with a high prevalence of cases. Both global and local regression modeling techniques were employed to examine the spatial relationships between the explanatory variables and the dependent variables. Results: The trend analysis revealed a notable increase in the prevalence of EIBF from 51.8% in 2011 to 71.9% in 2019. Similarly, the prevalence of EBF increased from 52.7% in 2011 to 58.9% in 2019. Spatial analysis demonstrated significant spatial variation in both EIBF and EBF throughout the country. Cold spots or clusters with a low prevalence of EIBF were observed consistently in the Tigray and Amhara regions, and significant cold spot areas of EBF were observed consistently in the Afar and Somali regions. Multiscale geographically weighted regression analysis revealed significant predictors of spatial variations in EIBF, including the religious affiliation of being a follower of the orthodox religion, parity of 1–2, absence of antenatal care visits, and delivery via cesarean section. Conclusions: Despite the increase in both EIBF and EBF rates over time in Ethiopia, these rates still fall below the national target. To address this issue, the government should prioritize public health programs aimed at improving maternal healthcare service utilization and maternal education. It is essential to integrate facility-level services with community-level services to achieve optimal breastfeeding practices. Specifically, efforts should be made to promote breastfeeding among mothers who have delivered via cesarean section. Additionally, there should be a focus on encouraging antenatal care service utilization and adapting maternal healthcare services to accommodate the mobile lifestyle of pastoralist communities. These steps will contribute to enhancing breastfeeding practices and achieving better outcomes for maternal and child health. [ABSTRACT FROM AUTHOR]

Accurate identification of the spatio-temporal planting structure and analysis of its driving factors in an irrigation district are the important bases for scientific and reasonable utilization of irrigation water resources. In pursuit of this goal, the training sample migration method combined with the random forest algorithm were used to classify land use and planting structure over 2001–2022 in the lower Yellow River Basin. Moreover, an econometric regression model was applied to quantify the driving factors of the change in the crop-planted area. The results illustrated that the identification method has relatively high accuracy in identifying historical periods of land use and planting structures, with the average kappa coefficient equating to 0.953. From 2001 to 2022, the area of cultivated land was the largest, with the proportion of the total area increasing from 45.72% to 58.12%. The planted area of winter wheat–summer maize rotation increased from 74.84% to 88.11% of the cultivated land. While the planted area of cotton declined by 96.36%, about 50% of cotton planting was converted to the winter wheat–summer maize rotation planting. The government policies about grain purchase and storage were the dominant factors for the change in the crop-planted area. This resulted in an increase of 63.32 × 103 ha and 63.98 × 103 ha in the planted area of winter wheat and summer maize, respectively. The findings are of great significance to the allocation of water resources in irrigation districts of the lower Yellow River Basin. [ABSTRACT FROM AUTHOR]

Knowledge regarding soil wetness dynamics is relevant for its application in hydrological and climatological studies. It is also a parameter that allows the analysis of processes related to land cover and land use. Due to this importance, the spatio-temporal dynamics of the surface soil humidity (SSH) in the southern of the Argentinean Pampean Plains have been analyzed, based on data corresponding to six Argentine localities and in relation to a climatological index that incorporates rainfall and evapotranspiration variables. The methodology includes statistical correlation and multivariate analysis based on modeled and reanalysis data. The results obtained reveal a different response of the SSH at a spatial level, with maximum values in the highest rainfall region, diminishing towards the most arid zone. This allows us to infer the influence of the topographic homogeneity of the area and the predominance of vertical processes in the hydrological balance. At both a spatial and temporal scale, the SSH dynamics concur with the climatological index pattern, confirmed by a Pearson coefficient. It has also been shown that the SSH responds to the climatological parameters and that the highest correlation occurs at a three-month temporal scale. This response may be observed at an interannual and annual level, with maximum and minimum values during the wet and dry years, respectively and a decrease in the SSH during the warmest seasons (summer). The SSH displays the same tendency as the climatological index, from NE to SW, reaching a maximum in the rainiest area and minimum in the arid region. The manuscript, based on diverse sources analysis, pluviometric periods classification and statistical methods application, is a contribution related to the edaphic variable dynamics in a region in which farming activity constitutes the main land use and where the droughts and floods alternance has effects on the flooding risks and the economic losses as a consequence of intensive droughts, respectively. [ABSTRACT FROM AUTHOR]

This study conducts a spatio-temporal analysis to identify trends and clusters of child sexual abuse in Mexico City before, during, and after the COVID-19 pandemic. Sexual abuses of children were analyzed considering various crime theories. Trends and patterns were identified using time series decomposition and spatial autocorrelation techniques. Time series considered three relevant periods. Anselin's Local Moran's I identified the spatial distribution of significant clusters. The child sexual abuse rate presented similar values following school closures. The resumption of classes entailed a decrease of −1.5% (children under 15) and an increase of 29% (children over 15). Particular locations in Mexico City experienced significant clusters among those over 15. There were eight noteworthy clusters displaying recidivism patterns with lower poverty rates and a high level of education. Efforts to combat child sexual abuse should prioritize specific areas in Mexico City where female children over 15 are at high risk of becoming victims of sexual abuse. [ABSTRACT FROM AUTHOR]

Droughts can affect forest ecosystems and lead to soil degradation, biodiversity loss, and desertification. Not all regions of Mexico are affected in the same way, as some areas are naturally more prone to drought due to their geographical location. Therefore, the objective of this work was to carry out a spatio-temporal analysis of the occurrence of droughts (severe and extreme) in Mexican forest systems, covering the period 2000–2021, and to study the area covered by these events in Mexican forest systems. This analysis was divided into three stages: the classification of land use and vegetation, spatial mapping and the classification of drought intensity, and an analysis of drought frequency and probability in forest systems. The results show that more than 46% of Mexico's forest area experienced severe and extreme droughts during the 21-year period studied. Broadleaved forests were most affected by severe and extreme droughts, with a frequency of 6 years. The increasing frequency of droughts poses a major challenge to the resilience of forest ecosystems in Mexico, highlighting the need to implement climate change adaptation and forest management measures to protect the country's biodiversity and natural resources. [ABSTRACT FROM AUTHOR]

Abstract Objective To analyse hospital case fatality and mortality related to Chagas disease (CD) in Brazil, 2000–2019. Method This is a mixed ecological study with spatial and temporal trends, based on national population data from the Brazilian Ministry of Health - hospital admissions (HA) and death certificates (DC). Records with CD as a primary or secondary cause of death in HA and/or as an underlying or associated cause of death in DC were evaluated. Temporal trends were analysed by Joinpoint regression and the spatial distribution of age- and gender-adjusted rates, spatial moving averages, and standardized morbidity ratios. Results There were a total of 4,376 HA due to CD resulting in death in Brazil, with a hospital case fatality rate of 0.11/100,000 inhabitants. The Southeast region had the highest rate (63.9%, n = 2,796; 0.17/100,000 inhabitants). The general trend for this indicator in Brazil is upwards (average annual percentage change [AAPC] 7.5; 95% confidence interval [CI] 5.3 to 9.9), with increases in the North, Northeast and Southeast regions. During the same period 122,275 deaths from CD were registered in DC, with a mortality rate of 3.14/100,000 inhabitants. The highest risk of CD-related death was found among men (relative risk [RR] 1.27) and Afro-Brazilians (RR 1.63). There was a downward trend in CD mortality in the country (AAPC − 0.7%, 95%CI -0.9 to -0.5), with an increase in the Northeast region (AAPC 1.1%, 95%CI 0.6 to 1.6). Municipalities with a very high Brazilian Deprivation Index tended to show an increase in mortality (AAPC 2.1%, 95%CI 1.6 to 2.7), while the others showed a decrease. Conclusion Hospital case fatality and mortality due to CD are a relevant public health problem in Brazil. Differences related to gender, ethnicity, and social vulnerability reinforce the need for comprehensive care, and to ensure equity in access to health in the country. Municipalities, states, and regions with indicators that reveal higher morbidity and mortality need to be prioritized.

Abstract Background Breastfeeding offers numerous benefits for infants, mothers, and the community, making it the best intervention for reducing infant mortality and morbidity. The World Health Organization (WHO) recommends initiating breastfeeding within one hour after birth and exclusively breastfeeding for the first six months. This study investigated the trend, spatio-temporal variation, and determinants of spatial clustering of early initiation of breastfeeding (EIBF) and exclusive breastfeeding (EBF) in Ethiopia from 2011 to 2019. Methods Data from the Ethiopian Demographic and Health Survey (EDHS), which was conducted in 2011, 2016, and 2019, were analyzed utilizing a weighted sample of 10,616 children aged 0–23 years for EIBF and 2,881 children aged 0–5 months for EBF. Spatial autocorrelation analysis was used to measure whether EIBF and EBF were dispersed, clustered, or randomly distributed and Kriging interpolation was employed to predict the outcome variables in the unmeasured areas. Spatial scan statistics were used to identify spatial clusters with a high prevalence of cases. Both global and local regression modeling techniques were employed to examine the spatial relationships between the explanatory variables and the dependent variables. Results The trend analysis revealed a notable increase in the prevalence of EIBF from 51.8% in 2011 to 71.9% in 2019. Similarly, the prevalence of EBF increased from 52.7% in 2011 to 58.9% in 2019. Spatial analysis demonstrated significant spatial variation in both EIBF and EBF throughout the country. Cold spots or clusters with a low prevalence of EIBF were observed consistently in the Tigray and Amhara regions, and significant cold spot areas of EBF were observed consistently in the Afar and Somali regions. Multiscale geographically weighted regression analysis revealed significant predictors of spatial variations in EIBF, including the religious affiliation of being a follower of the orthodox religion, parity of 1–2, absence of antenatal care visits, and delivery via cesarean section. Conclusions Despite the increase in both EIBF and EBF rates over time in Ethiopia, these rates still fall below the national target. To address this issue, the government should prioritize public health programs aimed at improving maternal healthcare service utilization and maternal education. It is essential to integrate facility-level services with community-level services to achieve optimal breastfeeding practices. Specifically, efforts should be made to promote breastfeeding among mothers who have delivered via cesarean section. Additionally, there should be a focus on encouraging antenatal care service utilization and adapting maternal healthcare services to accommodate the mobile lifestyle of pastoralist communities. These steps will contribute to enhancing breastfeeding practices and achieving better outcomes for maternal and child health.

Introduction. The rapid spread of African swine fever in the Kaliningrad region makes it necessary to use the methods of molecular epidemiology to determine the dynamics and direction of ASF spread in this region of Russia. The aim of the study was to determine single nucleotide polymorphisms within molecular markers K145R, O174L and MGF 505-5R of ASFVs isolated in Kaliningrad region and to study the circulating of the pathogen in European countries by subgenotyping and spatio-temporal clustering analysis. Materials and methods. Blood samples from living domestic pigs and organs from dead domestic pigs and wild boars, collected in the Kaliningrad region between 2017 and 2022 were used. Virus isolation was carried out in porcine bone-marrow primary cell culture. Amplicons of genome markers were amplified by PCR with electrophoretic detection and subsequent extraction of fragments from agarose gel. Sequencing was performed using the Sanger method. Results. The circulation of two genetic clusters of ASFV isolates on the territory of the Kaliningrad has been established: epidemic (K145R-III, MGF 505-5R-II, O174L-I – 94.3% of the studied isolates) and sporadic (K145R-II, MGF 505-5R-II, O174L-I – 5.7%). Conclusion. The broaden molecular genetic surveillance of ASFV isolates based on sequencing of genome markers is necessary in the countries of the Eurasian continent to perform a more detailed analysis of ASF spread between countries and within regions.

In Canada, Gonorrhea infection ranks as the second most prevalent sexually transmitted infection. In 2018, Manitoba reported an incidence rate three times greater than the national average. This study aims to investigate the spatial, temporal, and spatio-temporal patterns of Gonorrhea infection in Manitoba, using individual-level laboratory-confirmed administrative data provided by Manitoba Health from 2000 to 2016. Age and sex patterns indicate that females are affected by infections at younger ages compared to males. Moreover, there is an increase in repeated infections in 2016, accounting for 16% of the total infections. Spatial analysis at the 96 Manitoba regional health authority districts highlights significant positive spatial autocorrelation, demonstrating a clustered distribution of the infection. Northern districts of Manitoba and central Winnipeg were identified as significant clusters. Temporal analysis shows seasonal patterns, with higher infections in late summer and fall. Additionally, spatio-temporal analysis reveals clusters during high-risk periods, with the most likely cluster in the northern districts of Manitoba from January 2006 to June 2014, and a secondary cluster in central Winnipeg from June 2004 to November 2012. This study identifies that Gonorrhea infection transmission in Manitoba has temporal, spatial, and spatio-temporal variations. The findings provide vital insights for public health and Manitoba Health by revealing high-risk clusters and emphasizing the need for focused and localized prevention, control measures, and resource allocation.

This study investigates the prevalence, distribution, and ecological consequences of 21 heavy metals (Ag, Al, As, B, Ba, Ca, Cd, Co, Cr, Cu, Fe, Hg, K, Li, Mg, Mn, Na, Ni, Pb, Sr, and Zn) in the soils between the Mahi and Dhadhar rivers in Gujarat, India. It aims to assess the seasonal variations in soil contamination and the potential human health risk associated with the heavy metal exposure through ingestion, inhalation, and dermal contact pathways. Soil samples were collected from 57 sites across three distinct seasons and analyzed using indices such as the geo-accumulation index, contamination factor, pollution load index, ecological risk factor, and global potential ecological risk. Human health risks were also evaluated for carcinogenic and non-carcinogenic effects. The findings reveal significant soil contamination, especially during the Monsoon and Post-Monsoon seasons, with heavy metals like Ag, Cd, Cr, Cu, Co, Ni, and Pb posing considerable ecological threats. Cr and Ni were identified as presenting the highest carcinogenic risks, while Fe and Cr posed major non-carcinogenic challenges. Seasonal variations significantly influenced heavy metal concentrations and distribution patterns. The study highlights the urgent need for comprehensive policies and sustainable practices to mitigate soil contamination and protect environmental health. It emphasizes the critical role of human activities, such as industrial, agricultural, and mining operations, in soil degradation and calls for increased community awareness and action to address these challenges. [ABSTRACT FROM AUTHOR]

Summary Background In Haiti, reported incidence and mortality rates for COVID-19 were lower than expected. We aimed to analyze factors at communal and individual level that might lead to an underestimation of the true burden of the COVID-19 epidemic in Haiti during its first two years. Methods We analyzed national COVID-19 surveillance data from March 2020 to December 2021, to describe the epidemic using cluster detection, time series, and cartographic approach. We performed multivariate Quasi-Poisson regression models to determine socioeconomic factors associated with incidence and mortality. We performed a mixed-effect logistic regression model to determine individual factors associated with the infection. Results Among the 140 communes of Haiti, 57 (40.7%) had a COVID-19 screening center, and the incidence was six times higher in these than in those without. Only 22 (15.7%) communes had a COVID-19 care center, and the mortality was five times higher in these than in those without. All the richest communes had a COVID-19 screening center while only 30.8% of the poorest had one. And 75% of the richest communes had a COVID-19 care center while only 15.4% of the poorest had one. Having more than three healthcare workers per 1000 population in the commune was positively associated with the incidence (SIR: 3.31; IC95%: 2.50, 3.93) and the mortality (SMR: 2.73; IC95%: 2.03, 3.66). At the individual level, male gender (adjusted OR: 1.11; IC95%: 1.01, 1.22), age with a progressive increase of the risk compared to youngers, and having Haitian nationality only (adjusted OR:2.07; IC95%: 1.53, 2.82) were associated with the infection. Conclusions This study highlights the weakness of SARS-CoV-2 screening and care system in Haiti, particularly in the poorest communes, suggesting that the number of COVID-19 cases and deaths were probably greatly underestimated.

Objective: The aim of this study was to conduct a comprehensive spatio-temporal analysis of suicide-related emergency calls in the city of Valencia (Spain) over a six-year period. To this end we first examined age and gender patterns and, second, the influence of neighborhood characteristics on general and gender-specific spatio-temporal patterns of suicide-related emergency calls. Method: Geocoded data on suicide-related emergency calls between 2017 and 2022 (N = 10,030) were collected from the 112 emergency service in Valencia. Data were aggregated at the census block group level, used as a proxy for neighborhoods, and trimesters were considered as the temporal unit. Two set of analyses were performed: (1) demographic (age and gender) and temporal descriptive analyses and (2) general and gender-specific Bayesian spatio-temporal autoregressive models. Results: Descriptive analyses revealed a higher incidence of suicide-related emergency calls among females and an increase in calls among the 18-23 age group from 2020 onwards. The general spatio-temporal model showed higher levels of suicide-related emergency calls in neighborhoods characterized by lower education levels and population density, and higher residential mobility, aging population, and immigrant concentration. Relevant gender differences were also observed. A seasonal effect was noted, with a peak in calls during spring for females and summer for males. Conclusions: These findings highlight the need for comprehensive mental health targeted interventions and preventive strategies that account for gender-specific disparities, age-related vulnerabilities, and the specific characteristics of neighborhoods.

Summary: Background: In Haiti, reported incidence and mortality rates for COVID-19 were lower than expected. We aimed to analyze factors at communal and individual level that might lead to an underestimation of the true burden of the COVID-19 epidemic in Haiti during its first two years. Methods: We analyzed national COVID-19 surveillance data from March 2020 to December 2021, to describe the epidemic using cluster detection, time series, and cartographic approach. We performed multivariate Quasi-Poisson regression models to determine socioeconomic factors associated with incidence and mortality. We performed a mixed-effect logistic regression model to determine individual factors associated with the infection. Results: Among the 140 communes of Haiti, 57 (40.7%) had a COVID-19 screening center, and the incidence was six times higher in these than in those without. Only 22 (15.7%) communes had a COVID-19 care center, and the mortality was five times higher in these than in those without. All the richest communes had a COVID-19 screening center while only 30.8% of the poorest had one. And 75% of the richest communes had a COVID-19 care center while only 15.4% of the poorest had one. Having more than three healthcare workers per 1000 population in the commune was positively associated with the incidence (SIR: 3.31; IC95%: 2.50, 3.93) and the mortality (SMR: 2.73; IC95%: 2.03, 3.66). At the individual level, male gender (adjusted OR: 1.11; IC95%: 1.01, 1.22), age with a progressive increase of the risk compared to youngers, and having Haitian nationality only (adjusted OR:2.07; IC95%: 1.53, 2.82) were associated with the infection. Conclusions: This study highlights the weakness of SARS-CoV-2 screening and care system in Haiti, particularly in the poorest communes, suggesting that the number of COVID-19 cases and deaths were probably greatly underestimated. [ABSTRACT FROM AUTHOR]

Our study examines how dengue fever incidence is associated with spatial (demographic and socioeconomic) alongside temporal (environmental) factors at multiple scales in the city of Ibagué, located in the Andean region of Colombia. We used the dengue incidence in Ibagué from 2013 to 2018 to examine the associations with climate, socioeconomic, and demographic factors from the national census and satellite imagery at four levels of local spatial aggregation. We used geographically weighted regression (GWR) to identify the relevant socioeconomic and demographic predictors, and we then integrated them with environmental variables into hierarchical models using integrated nested Laplace approximation (INLA) to analyze the spatio-temporal interactions. Our findings show a significant effect of spatial variables across the different levels of aggregation, including human population density, gas and sewage connection, percentage of woman and children, and percentage of population with a higher education degree. Lagged temporal variables displayed consistent patterns across all levels of spatial aggregation, with higher temperatures and lower precipitation at short lags showing an increase in the relative risk (RR). A comparative evaluation of the models at different levels of aggregation revealed that, while higher aggregation levels often yield a better overall model fit, finer levels offer more detailed insights into the localized impacts of socioeconomic and demographic variables on dengue incidence. Our results underscore the importance of considering macro and micro-level factors in epidemiological modeling, and they highlight the potential for targeted public health interventions based on localized risk factor analyses. Notably, the intermediate levels emerged as the most informative, thereby balancing spatial heterogeneity and case distribution density, as well as providing a robust framework for understanding the spatial determinants of dengue. [ABSTRACT FROM AUTHOR]

Noncommunicable diseases (NCDs) accounted for approximately 74% of all deaths globally in 2022. Among the deaths from NCDs, the leading causes is cardiovascular disease (CVD), which represents approximately 32% of all global deaths. Furthermore, estimates indicate that over three-quarters of CVD deaths occur in low- and middle-income countries such as Mexico. Therefore, the goal of this study is to analyze the spatio-temporal patterns of mortality from cardiovascular diseases across Mexican municipalities from 2010 to 2019. We used a spatial Bayesian hierarchical regression model based on the Integrated Nested Laplace Approximation (INLA) and implemented in the R-INLA package to study the spatial pattern of mortality from cardiovascular diseases in Mexican municipalities. The modeling process revealed that the best model for both populations under and over 60 years old was the spatio-temporal model with space-time interaction. Overall, the purely spatial results suggest that the relative risks for both age groups (under and over 60 years old) do not have a consistent spatial pattern in 2019. On the other hand, the spatio-temporal results show that the interactions are stronger for the population over 60 years of age. This paper demonstrates the importance of assessing not only the spatial pattern of deaths, but also simultaneously incorporating temporal trends. With the understanding that this relationship (space-time) cannot be neglected, first the results of a purely spatial model are presented and, soon after, this model is expanded and the spatio-temporal results of mortality from cardiovascular diseases across Mexican municipalities from 2010 to 2019 are shown. [ABSTRACT FROM AUTHOR]

The study aims to identify the spatial and spatio-temporal patterns of rape in India from 2011 to 2020 using crime data aggregated at the district level. The study also tries to understand persistent patterns in the spatial variation of rape incidence across the country during the study period. The annual rape data from 2011 to 2020 were extracted from the National Crime Records Bureau. Retrospective spatial and temporal cluster analyses were performed using the purely spatial (Kulldorff’s and Mann–Whitney scan statistic) and space–time scan statistic (Kulldorff’s based on the discrete Poisson model). Cluster frequency analysis was performed. Results showed the spatial heterogeneity in the pattern of rape crime across the country as well as its localization among geographically contiguous regions across time and space. The detected primary spatial clusters (P < 0.05) persisted mainly in the Central, North, and North Eastern zones of the country over the ten year study period. The primary spatio-temporal cluster (LLR = 5560.09, P < 0.001) appeared between the time frame 2014 to 2016 and was concentrated mainly in the Northern zone. Thirty-seven districts of India (5.78%) contributed to spatial clusters in all years of the study period. The heterogeneous distribution of rape across India could be due to the spatio-temporal variations in the determinants of rape (such as socioeconomic status, demographics, urbanisation, and gender equality) across the country. Identification of the localized rape clusters could result in understanding the contextual factors of rape and, thereby be beneficial to national and regional rape control strategies. [ABSTRACT FROM AUTHOR]

Lake Balkhash is Asia's third-largest lake and an endorheic basin. The lake and its contributing tributaries provide essential water and ecosystem services to the surrounding population, particularly in the Kazakh region. With approximately 2.5 million people living in the areas such as Almaty oblast, Zhetisu oblast, several districts of Karagandy oblast, and Abay province, monitoring and maintaining the lake's health and water quality is essential for the sustainable management of water resources. The hydrology of Lake Balkhash has been significantly impacted in recent decades by a warming climate, landuse landcover changes, and waterconsuming economic activities, the latter of which are driven by population growth and expansion. Turbidity--the measurement of water clarity--serves as a major indicator of water health. Here, we analyze spatial and temporal variability in turbidity across Lake Balkhash by mapping the normalized difference turbidity index (NDTI) based on Landsat data for 1991-2022. We consider major exploratory variables such as precipitation, near-surface temperature, wind speed and direction, water level, and landuse landcover (LULC) within the catchment. We find an overall decrease in turbidity over interannual and seasonal timescales. We observe significant negative correlations between NDTI, near-surface temperature, and water level at both scales but no clear relationship between turbidity and precipitation or wind variables. Among the LULC variables, grassland and bareland near Lake Balkhash showed a positive correlation with NDTI but have spatially decreased over time. Conversely, shrubland and wetland exhibit a negative correlation with NDTI; however, this has spatially increased with time. Our results highlight the significant impact of rising temperatures, anthropogenically influenced water levels, and the LULC variables on turbidity. The turbidity dynamics, in turn, influence the circulation, oxidation, and overall health of Lake Balkhash's water. Therefore, the study emphasizes that the warming climate and alterations in the lake's hydrology have a considerable impact on water quality. This suggests that monitoring water health alone may not suffice to mitigate the impacts of climate change and human activities. However, a more comprehensive approach is needed to sustainably manage and conserve dryland water resources. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to conduct a comprehensive spatio-temporal analysis of suicide-related emergency calls in the city of Valencia (Spain) over a six-year period. To this end we first examined age and gender patterns and, second, the influence of neighborhood characteristics on general and gender-specific spatio-temporal patterns of suicide-related emergency calls. Method: Geocoded data on suicide-related emergency calls between 2017 and 2022 (N = 10,030) were collected from the 112 emergency service in Valencia. Data were aggregated at the census block group level, used as a proxy for neighborhoods, and trimesters were considered as the temporal unit. Two set of analyses were performed: (1) demographic (age and gender) and temporal descriptive analyses and (2) general and gender-specific Bayesian spatio-temporal autoregressive models. Results: Descriptive analyses revealed a higher incidence of suicide-related emergency calls among females and an increase in calls among the 18-23 age group from 2020 onwards. The general spatio-temporal model showed higher levels of suicide-related emergency calls in neighborhoods characterized by lower education levels and population density, and higher residential mobility, aging population, and immigrant concentration. Relevant gender differences were also observed. A seasonal effect was noted, with a peak in calls during spring for females and summer for males. Conclusions: These findings highlight the need for comprehensive mental health targeted interventions and preventive strategies that account for gender-specific disparities, age-related vulnerabilities, and the specific characteristics of neighborhoods. [ABSTRACT FROM AUTHOR]

Background: Chemical control of insect pests in oilseed rape (OSR) is becoming increasingly difficult due to the development of resistance and restrictive insecticide approvals in Europe. At the same time, there is a lack of preventive and alternative control measures. Crop rotation mostly fails to control insects due to their mobility; however, changing regional cropping densities can dilute or concentrate pest pressure. In this study, we investigated whether the local occurrence of Psylliodes chrysocephala and Delia radicum, serious insect pests in winter OSR, is influenced by distance from the previous year's OSR fields and how changes in OSR rape cropping density at a regional scale (up to 10 km radius) affect pest pressure. Results: Abundance of P. chrysocephala in yellow water traps decreased with increasing distance to previous year's OSR. Estimated catches in the first 3 weeks of migration were about 68–76% lower at 10 km distance compared to 1 km in autumn 2019 and 2020. However, in both seasons P. chrysocephala was able to disperse over distances of 10 km. Probability of root damage by D. radicum was affected by changes of OSR cropping area at a spatial scale of 2.5 km radius; it increased if acreage of OSR decreased. Furthermore, aphid infestation was lower when OSR was distant in the previous year. Conclusion: This study could enable field‐specific risk assessment and prediction of pest pressure. To decide about the effectiveness of cropping breaks at a regional level as a preventive crop protection measure, more knowledge on other pest species and antagonists is needed. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

In this study, we analysed the novel coronavirus disease (COVID-19) cases data to investigate the regional infection trends in Japan. There had been seven outbreaks by October 2022 in Japan. In each outbreak, the number of COVID-19 cases has increased at different rates in different regions. The prefectural infection ratio is defined using COVID-19 cases data. We calculate the prefectural infection ratio and study the characteristic of each pandemic wave. The prefectural order of infection progression is estimated in each past wave of the COVID-19 pandemic. This study shows that the infection spread from the Kanto region in the fourth pandemic wave and the infection spread simultaneously from four regions in the sixth wave. It is also found that the infection situation trend in Okinawa differs from that in the other regions. [ABSTRACT FROM AUTHOR]

In order to further understand the ecological status of the northern slope of Qinling Mountains, high-resolution remote sensing data and fuzzy analytic hierarchy process method were used to build an assessment model for the evolution of ecological security. With the analysis of spatio temporal variation and its driving factors, the ecological security status of the mountains was deeply explored. The findings reveal an overall increasing trend in the ecological security of the region, with areas rated as moderately safe or above increasing from 67.5% to 71.9%. Ecological security is generally higher in the southern than in the northern regions, displaying a clear zonal distribution pattern. The evolution of ecological security first declined and then grew, with land use, normalized vegetation index, and PM2.5 identified as the primary driving factors, with correlation indices of 0.90, 0.86, and 0.79, respectively. The results indicate that anthropogenic activities from urbanization and ecological conservation policies significantly influence the ecological security of the northern Qinling region. This study not only offers a scientific assessment of the ecological security in the mountains, but also provides robust decision-making support for regional ecological conservation and sustainable development strategies. [ABSTRACT FROM AUTHOR]

This study is interdisciplinary in nature and brings in its fold dissemination of social science research on health. In the light of the recent Covid 19 crisis, there has been an increasing interest towards epidemiology and to understand the concept of epidemics or pandemics. Intermittent outbreaks of infectious diseases have had profound effect on societies throughout history. Historical perspective helps in understanding the extent to which panic, connected with social stigma, threat, prejudice, frustrated public health efforts can control the spread of disease. The intensity of the spread of a pandemic and the number of people affected in country and specific regions depend a lot on the measures of state control at the local and centre-level. However, the severity of an epidemic which slowly pervades into a pandemic depends on the spatio-temporal frame of a region. The research poses a basic question, how do members of the society respond to the threat of pandemic and to the hygiene, social isolation and other measures proposed by public health, over time and selects the case of Bengal which has witnessed three pandemics since the 18thcentury.. The study follows a mixed-method approach and the discussion provided a few general observations which however are not exhaustive to pandemic reaction, for the study area like threat Perception, emergence of leadership, science communication etc. [ABSTRACT FROM AUTHOR]

Terrestrial ecosystems have taken up about 32% of the total anthropogenic CO2 emissions in the past six decades1. Large uncertainties in terrestrial carbon-climate feedbacks, however, make it difficult to predict how the land carbon sink will respond to future climate change2. Interannual variations in the atmospheric CO2 growth rate (CGR) are dominated by land-atmosphere carbon fluxes in the tropics, providing an opportunity to explore land carbon-climate interactions3-6. It is thought that variations in CGR are largely controlled by temperature7-10 but there is also evidence for a tight coupling between water availability and CGR11. Here, we use a record of global atmospheric CO2, terrestrial water storage and precipitation data to investigate changes in the interannual relationship between tropical land climate conditions and CGR under a changing climate. We find that the interannual relationship between tropical water availability and CGR became increasingly negative during 1989-2018 compared to 1960-1989. This could be related to spatiotemporal changes in tropical water availability anomalies driven by shifts in El Niño/Southern Oscillation teleconnections, including declining spatial compensatory water effects9. We also demonstrate that most state-of-the-art coupled Earth System and Land Surface models do not reproduce the intensifying water-carbon coupling. Our results indicate that tropical water availability is increasingly controlling the interannual variability of the terrestrial carbon cycle and modulating tropical terrestrial carbon-climate feedbacks.

ABSTRACT BACKGROUND: The risk of death due to tuberculosis (TB) in Brazil is high and strongly related to living conditions (LC). However, epidemiological studies investigating changes in LC and their impact on TB are lacking. OBJECTIVES: To evaluate the impact of LC on TB mortality in Brazil. DESIGN AND SETTING: This ecological study, using panel data on spatial and temporal aggregates, was conducted in 1,614 municipalities between 2002 and 2015. METHODS: Data were collected from the Mortality Information System and the Brazilian Institute of Geography and Statistics. The proxy variable used for LC was the Urban Health Index (UHI). Negative binomial regression models were used to estimate the effect of the UHI on TB mortality rate. Attributable risk (AR) was used as an impact measure. RESULTS: From 2002 to 2015, TB mortality rate decreased by 23.5%, and LC improved. The continuous model analysis resulted in an RR = 0.89 (95%CI = 0.82–0.96), so the AR was −12.3%. The categorized model showed an effect of 0.92 (95%CI = 0.83–0.95) in municipalities with intermediate LC and of 0.83 (95%CI = 0.82–0.91) in those with low LC, representing an AR for TB mortality of −8.7% and −20.5%, respectively. CONCLUSIONS: Improved LC impacted TB mortality, even when adjusted for other determinants. This impact was greater in the strata of low-LC municipalities.

Since the new century, the Association of Southeast Asian Nations (ASEAN) has managed to sustain stable economic growth, drawing global attention due to its robust performance. Nighttime light (NTL) observations have been recognized as a useful tool for monitoring the long-term socioeconomic development trends over large areas. Therefore, this study focused on analyzing the spatial and temporal distribution of NTL data in ASEAN countries from 2000 to 2022. The results reveal that NTL data are closely correlated with four key socioeconomic factors, including population, gross domestic product, industrial output, and service industry output. This demonstrates the viability of using NTL data as a proxy for assessing socioeconomic development levels in ASEAN countries. The study also observes significant variations in the spatial distribution of NTL within the ASEAN. Since 2000, there has been a continuous increase in NTL data across ASEAN countries, with a particularly rapid growth phase commencing post-2010. Notably, Laos, Cambodia, and Vietnam exhibited the fastest growth rates, with annual averages of 15.79%, 14.95%, and 14.81%, respectively. Furthermore, the relative changes in NTL values in ASEAN countries significantly surpassed those observed in South Korea, Japan, and the United States. The main reasons may be the implementation of open policies, optimized industrial structure, enhanced infrastructure development, and abundant labor resources in ASEAN countries. The insights from this study provide local governments and policymakers with a scientific basis to inform regional sustainable development planning in ASEAN countries, highlighting the importance of integrating NTL data analysis in socioeconomic development strategies.

The purpose of this research was to investigate the intricate connections among land use change, land surface temperature, and the distribution of partridges (Alectoris Barbara), employing a comprehensive analysis of various environmental factors. Indeed, a variety of geospatial techniques have been used to analyze the spatio-temporal trends in temperature as a function of different classes of vegetation cover, and the geographic distribution of ecological niches for this species in Meknes province was modeled using Maxent 3.2 (Maximum Entropy) software. The study spanned a 22-year timeframe, from 2000 to 2021, during which alterations in each land use category were identified through the utilization of various sensors, incorporating Landsat 7 ETM+ and Landsat 8 OLI/TIRS in the analysis. The results induced a significant change in the land surface temperature (LTS) with a range of 15.85-36.20 °C, 12.76-38.24 °C and 25.73-47.79 °C for the years 2000, 2010 and 2020, respectively. However, this change was negatively correlated with the normalized difference vegetation index (NDVI). This decline in vegetation, in turn, manifests as a significant factor contributing to the diminution of partridge distribution. By empirically establishing these connections, the research not only underscores the impact of temperature-induced vegetation changes on partridge habitat but also enhances our comprehension of the intricate ecological dynamics governing species distribution in the context of evolving land use patterns.

Nightlife in urban areas is gaining interest due to its role as a stimulus of the local economy, tourism, safety, and public services. However, most studies analyse nighttime activity from a qualitative perspective and at a general urban scale, without going into a detailed spatial analysis. In this paper, we have used the spatio-temporal details of Google Places data, and the activity trends at a variety of facilities that provide Google Popular Times. We studied and mapped the recovery of nighttime activity in the city of Madrid after the pandemic restrictions. The results reveal that there was a significant decrease of over 80% in the activity levels of open premises, which also led to a decrease in their average occupancy rate. These reductions were particularly noticeable in the city center and during the late-night hours.

BackgroundThere is growing evidence to suggest that exposure to a high concentration of nitrogen dioxide (NO2) can lead to a higher incidence of Acute Respiratory Infections (ARIs) in children; however, such an association remains understudied in Sub-Saharan Africa due to the limited availability of exposure data. This study explored this association by using the satellite-detected tropospheric NO2 concentrations measured by Sentinel-5 Precursor and ARI symptoms in children under age five collected in the Demographic and Health Survey (DHS) in Senegal.MethodsWe matched the daily tropospheric NO2 exposure with the individual ARI symptoms according to the DHS survey clusters spatially and temporally and conducted a logistic regression analysis to estimate the association of exposure to NO2 with ARI symptoms in two preceding weeks.ResultsWe observed a positive association between exposure to continuous levels of NO2 and ARI symptoms after adjusting for confounders (OR 1.27 per 10 mol/m2, 95% CI: 1.06 - 1.52). When the association was further examined by quartile exposure categories, the 4th quartile category was positively associated with symptoms of ARI after adjusting for confounders (OR 1.71, 95% CI: 1.08-2.69). This suggests that exposure to certain high levels of NO2 is associated with the increased risk of children having symptoms of ARI in Senegal.ConclusionsThis study highlights the need for increased research on the effects of ambient NO2 exposure in Africa as well as the need for more robust, ground-based air monitoring in the region. For a country like Senegal, where more than 90% of the population lives in areas that do not meet the national air quality standards, it is urgently required to implement air pollution prevention efforts to protect children from the health hazards of air pollution.

Macroautophagy is a homeostatic process required to clear cellular waste. Neuronal autophagosomes form constitutively in the distal tip of the axon and are actively transported toward the soma, with cargo degradation initiated en route. Cargo turnover requires autophagosomes to fuse with lysosomes to acquire degradative enzymes; however, directly imaging these fusion events in the axon is impractical. Here we use a quantitative model, parameterized and validated using data from primary hippocampal neurons, to explore the autophagosome maturation process. We demonstrate that retrograde autophagosome motility is independent of fusion and that most autophagosomes fuse with only a few lysosomes during axonal transport. Our results indicate that breakdown of the inner autophagosomal membrane is much slower in neurons than in nonneuronal cell types, highlighting the importance of this late maturation step. Together, rigorous quantitative measurements and mathematical modeling elucidate the dynamics of autophagosome-lysosome interaction and autophagosomal maturation in the axon.

In the wake of the COVID-19 pandemic, global efforts to mitigate the spread of the virus have led to widespread lockdowns and movement restrictions. Earlier studies have reported a notable positive correlation between nitrogen dioxide (NO2) levels and mobility during the initial 2020 lockdowns. However, our explorative investigation in Southeast Asia observed that, despite their similar spatial distribution, NO2 does not consistently align with mobility patterns. This observation indicates the existence of additional influential factors apart from mobility contributing to NO2 variation, necessitating a more comprehensive examination. Subsequently, we developed a trained Multi-Layer Perceptron (MLP) model and leveraged SHapley Additive exPlanations (SHAP) values. Our analysis extends beyond mobility to encompass diverse potential factors such as travel modes and meteorological factors. The model results suggest that, while as expected mobility has a strong impact on NO2 column density, a more accurate prediction requires considering different travel modes (i.e. driving and walking). Furthermore, the study reveals that spatio-temporal heterogeneity and meteorological factors also play roles in shaping NO2 level. These findings emphasize the importance of integrating such multifaceted considerations in future NO2 study for a more accurate and holistic understanding. [ABSTRACT FROM AUTHOR]

Exploratory data analysis tools designed to measure global and local spatial autocorrelation (e.g. Moran's $I$ I statistic) have become standard in modern GIS software. However, there has been little development in amending these tools for visualization and analysis of patterns captured in spatio-temporal data. We design and implement an exploratory mapping tool, VASA (Visual Analysis for Spatial Association), that streamlines analytical pipelines in assessing spatio-temporal structure of data and enables enhanced visual display of the patterns captured in data. Specifically, VASA applies a set of cartographic visual variables to map local measures of spatial autocorrelation and helps delineate micro and macro trends in space-time processes. Two visual displays are presented: recency and consistency map and line-scatter plots. The former combines spatial and temporal data view of local clusters, while the latter drills down on the temporal trends of the phenomena. As a case study, we demonstrate the usability of VASA for the investigation of mobility patterns in response to the COVID-19 pandemic throughout 2020 in the United States. Using daily county-level and grid-level mobility metrics obtained from three different sources (SafeGraph, Cuebiq, and Mapbox), we demonstrate cartographic functionality of VASA for a swift exploratory analysis and comparison of mobility trends at different regional scales. [ABSTRACT FROM AUTHOR]

Achieving the synergistic reduction of CO2 and air pollution emissions (SRCAPEs) holds great significance in promoting the green transformation. However, limited research has been conducted on the spatio-temporal impact of digital inclusive finance (DIF) on the synergy between CO2 and air pollution emissions (SCAPEs). To address this gap, we comprehensively employ the linear regression model, geographically and the temporally weighted regression (GTWR) model, and the ordered probit model to empirically analyze the influence of DIF on SCAPE. Our research reveals the following: (1) The linear regression model demonstrates that, on average, DIF can achieve a weak synergistic emission reduction effect. This result remains robust after a battery of robustness tests. (2) The GTWR model reveals that the impact of DIF on both emissions exhibits evident spatio-temporal characteristics. Its emission reduction effect gradually increases, especially after 2014. (3) On the basis of the estimates from the GTWR model, we can identify four distinct synergy types driven by DIF. The number of cities with the preferred type (i.e., achieving SRCAPE) increases the most, from 59 in 2011 to 233 in 2019. (4) On the basis of the built ordered probit models, green technology innovation is an important path for DIF to achieve synergistic emission reduction. The synergistic emission reduction effect is also significantly moderated by the regional economic level and environmental regulation intensity. Our findings have policy implications for central and local governments in achieving SRCAPE and support efforts to achieve sustainable development. [ABSTRACT FROM AUTHOR]

The North-Western Himalayas are becoming the hotspots for hydro-meteorological catastrophes due to climate change implications. Present study aims to analyse spatio-temporal dynamics of hailstorms, posing a threat to livelihood security in the Valley of Kashmir on account of significant land use shift from agriculture to horticulture which is highly susceptible to hail hazard. The mean maximum and mean minimum temperature and annual rainfall for Qazigund, Kupwara and Srinagar weather stations were analysed through statistical treatments like Mann Kendall and Sen's slope estimator. To establish the land use change scenario, area under horticulture and under Apple was calculated and demonstrated with the help of regression analysis. Hailstorm incidents were calculated through the archival newspapers from 2007 to 2022 and mapped spatio-temporally in GIS environment. Additionally, to forecast the near-term hail scenario, a trendline was established by using Linear Regression Equation for a period of 2022–2040. Furthermore, a 4 Point Likert scale survey for evaluating the perception of people regarding the changing climatic scenarios and intensification of hailstorm activity was carried out throughout the valley. A total of 203 hailstorm events have occurred from year 2007 to 2022. The findings reveal that the hail storm intensity and frequency has increased with the corresponding increase in temperature and decline in rainfall on account of changing Climatic scenarios across the Kashmir Valley. There has been an increase in hailstorms from 2 events in the year 2007 to 27 events in 2022. North-Kashmir districts, namely Baramulla and Kupwara are emerging as the hail storm hot spots during the spring seasons due to their location around the entrance corridor of western disturbance. The southern districts of Pulwama, Shopian and Anantnag have witnessed enhanced summer hailstorm activity probably under the influence of south-west monsoons. The forecast indicated a significant increase in the hailstorm occurrence with an R2 value of 0.83. The study also concludes that hailstorms are responsible for (30–70%) loss in the productivity of horticulture in the affected areas, thereby threatening livelihood of millions of farmers. Furthermore, the study indicates that the region does not have adequate adaptation and mitigation strategies in place as only 0.06% of the horticulturists are having Anti-hail net protection while as the crop insurance-cover is almost non-existent. The study shall be helpful in developing effective mitigation and adaptation strategies to combat the hail hazard for securing livelihoods by promoting sustainable horticulture in the region. [ABSTRACT FROM AUTHOR]

The open-pit mining area is highly affected by human activities, which aggravate soil erosion and disturb surface ecology, bringing many problems and challenges to its environmental management and restoration, which has received widespread attention. The establishment of an objective, timely and quantitative remote sensing monitoring, and evaluation system for the spatio-temporal evolution of the surface ecological environment in the open-pit mining area is of great significance for its environmental protection, management decisions, and sustainable social development. Based on the Google Earth Engine (GEE) platform, this paper uses Landsat images to construct and calculate the remote sensing ecological index (RSEI) of the Pingshuo open-cast mine area (POMA) from 1990 to 2020 and monitor and evaluate its surface ecological environment. Combined with the Theil-Sen median, Mann–Kendall test, and Hurst index, the spatio-temporal process was analyzed. The results showed that the ecological environmental quality of the mining area first decreased and then increased from 1990 to 2020. 1990–2000 was a period of serious ecological degradation, followed by improvement. The overall improvement area reached 87.03%, and the degradation was concentrated in the coal mining area. Between 1990 and 2020, the Hurst index of the mining area was 0.452, indicating that the region has a fragile ecological environment and has difficult maintaining its stability. The global Moran's I mean value of the RSEI of the study area is 0.92, which combined with Moran's scatter plot to indicate that there is a strong positive spatial correlation rather than a random distribution of its ecological environment. During the study period, the impact on the climate of the ecological environmental change of POMA was weak, and human factors such as coal mining, land reclamation, and social construction were the main driving forces for the change in ecological quality. The results of this study reveal the changing trend of surface ecology in the mining area over the past 30 years, which is helpful for understanding its impact mechanism on ecological quality and provides support for the management of the region. [ABSTRACT FROM AUTHOR]

Predicting the number of infections in the anti-epidemic process is extremely beneficial to the government in developing anti-epidemic strategies, especially in fine-grained geographic units. Previous works focus on low spatial resolution prediction, e.g., county-level, and preprocess data to the same geographic level, which loses some useful information. In this paper, we propose a fine-grained population mobility data-based model (FGC-COVID) utilizing data of two geographic levels for community-level COVID-19 prediction. We use the population mobility data between Census Block Groups (CBGs), which is a finer-grained geographic level than community, to build the graph and capture the dependencies between CBGs using graph neural networks (GNNs). To mine as finer-grained patterns as possible for prediction, a spatial weighted aggregation module is introduced to aggregate the embeddings of CBGs to community level based on their geographic affiliation and spatial autocorrelation. Extensive experiments on 300 days LA city COVID-19 data indicate our model outperforms existing forecasting models on community-level COVID-19 prediction. [ABSTRACT FROM AUTHOR]

Drought is a lengthened dry period in natural climate pattern which is one of the top global concerns. Drought in Thailand has been a long occurring issue affecting serious impact on health, economy, energy, and especially agricultural sector. Less precipitation caused long term water levels by lowering river volume and underground water. Therefore, the drought problem in Thailand is becoming more frequent and more severe. Kamphaeng Phet province located at the upper central part of Thailand demonstrated as the most drought-prone area. The objective of this study was to develop an approach for drought monitoring in provincial scale. MOD13Q1 dataset retrieved from MODIS satellite passing in different periods of Kamphaeng Phet was employed to develop the approach. Normalized Vegetation Index (NDVI) was extracted from the MOD13Q1 dataset so as to detect the vegetation condition of the study area. Afterwards NDVI was essentially standardized into Standardized Vegetation Index (SVI) in order to examine the area where NDVI was different from average value (Deviation). The probability of SVI acquired from NDVI standard scores reflected vegetation states explaining density and drought of vegetation in each period of time and season. Time series analysis of drought was implemented using SVI variances in different seasons. Additionally, precipitation variable was also included to find statistical consistency between SVI and precipitation. The lowest average of SVI was referred as drought, and nearly 0 value described severe drought. It was found that the most severe drought of Kamphaeng Phet occurred in 2020. In 2021, 2019, and 2018 drought showed at severe, moderate, and low level, respectively. Using both NDVI and SVI approach can be applied for drought monitoring and mitigation. Preparedness measures and public awareness of drought mitigation in this area can be enhanced. [ABSTRACT FROM AUTHOR]

Recent advances in deep learning, increased availability of large-scale datasets, and improvement of accelerated graphics processing units facilitated creation of an unprecedented amount of synthetically generated media content with impressive visual quality. Although such technology is used predominantly for entertainment, there is widespread practice of using deepfake technology for malevolent ends. This potential for malicious use necessitates the creation of detection methods capable of reliably distinguishing manipulated video content. In this work we aim to create a learning-based detection method for synthetically generated videos. To this end, we attempt to detect spatiotemporal inconsistencies by leveraging a learning-based magnification-inspired feature manipulation unit. Although there is existing literature on the use of motion magnification as a preprocessing step for deepfake detection, in our work, we aim to utilize learning-based magnification elements to develop an end-to-end deepfake detection model. In this research, we investigate different variations of feature manipulation networks, both with spatially constant and spatially varying amplification. To clarify, although the proposed model draws from existing literature on motion magnification, we do not perform motion magnification in our experiments but instead use the underlying architecture of such networks for feature enhancement. Our objective with this work is to take a step towards applying learnable motion manipulation in improving the target accuracy of a task at hand. [ABSTRACT FROM AUTHOR]

Agriculture is the lifeblood of the Indian economy. Agriculture production in India has increased dramatically, albeit with certain adverse effects. Land for agriculture is limited but the population is increasing rapidly, necessitating more food. In order to feed the world's rising population, agricultural intensification must be increased as cropping intensity is an important indication of agricultural production and sustainability, and variations in cropping intensity can have large economic, social and environmental consequences. This study examines the Spatio-temporal patterns of cropping intensity over the last decade and identifies the mechanisms causing these changes using data from government records, Statistical Abstract and field observations. Cropping intensity has grown in some areas of Haryana while dropping in others, according to the findings. The research also demonstrates the impact of factors such as irrigation access and government regulations on cropping intensity. The study has significant significance for Haryana's agricultural development and policy, and also for other regions facing similar challenges. [ABSTRACT FROM AUTHOR]

African swine fever (ASF) is a highly contagious, notifiable, and fatal hemorrhagic viral disease affecting domestic and wild pigs. The disease was reported for the first time in India during 2020, resulted in serious outbreaks and economic loss in North-Eastern (NE) parts, since 47% of the Indian pig population is distributed in the NE region. The present study focused on analyzing the spatial autocorrelation, spatio-temporal patterns, and directional trend of the disease in NE India during 2020–2021. The ASF outbreak data (2020–2021) were collected from the offices of the Department of Animal Husbandry and Veterinary Services in seven NE states of India to identify the potential clusters, spatio-temporal aggregation, temporal distribution, disease spread, density maps, and risk zones. Between 2020 and 2021, a total of 321 ASF outbreaks were recorded, resulting in 59,377 deaths. The spatial pattern analysis of the outbreak data (2020–2021) revealed that ASF outbreaks were clustered in 2020 (z score = 2.20, p <.01) and 2021 (z score = 4.89, p <.01). Spatial autocorrelation and Moran’s I value (0.05–0.06 in 2020 and 2021) revealed the spatial clustering and spatial relationship between the outbreaks. The hotspot analysis identified districts of Arunachal Pradesh, Assam and districts of Mizoram, Tripura as significant hotspots in 2020 and 2021, respectively. The spatial-scan statistics with a purely spatial and purely temporal analysis revealed six and one significant clusters, respectively. Retrospective unadjusted, temporal, and spatially adjusted space–time analysis detected five, five, and two statistically significant (p <.01) clusters, respectively. The directional trend analysis identified the direction of disease distribution as northeast-southwest (2020) and north–south (2021), indicate the possibility of ASF introduction to India from China. The high-risk zones and spatio-temporal pattern of ASF outbreaks identified in the present study can be used as a guide for deploying proper prevention, optimizing resource allocation and disease control measures in NE Indian states. [ABSTRACT FROM AUTHOR]