Risk factors for persistent fatal opioid-involved overdose clusters in Massachusetts 2011-2021: a spatial statistical analysis with socio-economic, accessibility, and prescription factors.

BACKGROUND: Fatal opioid-involved overdose rates increased precipitously from 5.0 per 100,000 population to 33.5 in Massachusetts between 1999 and 2022. METHODS: We used spatial rate smoothing techniques to identify persistent opioid overdose-involved fatality clusters at the ZIP Code Tabulation Area (ZCTA) level. Rate smoothing techniques were employed to identify locations of high fatal opioid overdose rates where population counts were low. In Massachusetts, this included areas with both sparse data and low population density. We used Local Indicators of Spatial Association (LISA) cluster analyses with the raw incidence rates, and the Empirical Bayes smoothed rates to identify clusters from 2011 to 2021. We also estimated Empirical Bayes LISA cluster estimates to identify clusters during the same period. We constructed measures of the socio-built environment and potentially inappropriate prescribing using principal components analysis. The resulting measures were used as covariates in Conditional Autoregressive Bayesian models that acknowledge spatial autocorrelation to predict both, if a ZCTA was part of an opioid-involved cluster for fatal overdose rates, as well as the number of times that it was part of a cluster of high incidence rates. RESULTS: LISA clusters for smoothed data were able to identify whether a ZCTA was part of a opioid involved fatality incidence cluster earlier in the study period, when compared to LISA clusters based on raw rates. PCA helped in identifying unique socio-environmental factors, such as minoritized populations and poverty, potentially inappropriate prescribing, access to amenities, and rurality by combining socioeconomic, built environment and prescription variables that were highly correlated with each other. In all models except for those that used raw rates to estimate whether a ZCTA was part of a high fatality cluster, opioid overdose fatality clusters in Massachusetts had high percentages of Black and Hispanic residents, and households experiencing poverty. The models that were fitted on Empirical Bayes LISA identified this phenomenon earlier in the study period than the raw rate LISA. However, all the models identified minoritized populations and poverty as significant factors in predicting the persistence of a ZCTA being part of a high opioid overdose cluster during this time period. CONCLUSION: Conducting spatially robust analyses may help inform policies to identify community-level risks for opioid-involved overdose deaths sooner than depending on raw incidence rates alone. The results can help inform policy makers and planners about locations of persistent risk.

Anaemia prevalence and socio-demographic factors among women of reproductive age (WRA): A geospatial analysis of empowered action group (EAG) states in India.

Anaemia remains a major nutritional-related health concern for women under reproductive age (WRA) in developing nations like India as well as the Indian EAG states. According to NFHS round-5, EAG states constitute 57% of WRA having any form of anaemia, higher than many other states of India and other developed and developing nations. This study aimed to assess the frequency of anaemia among the WRA in India's eight EAG states. Also, it attempts to analyse the causes associated with anaemia by the women's background characteristics with spatial correlation with its co-variates across 291 districts of the EAG states. One of the most current Demographic and Health Survey's (DHS) cross-sectional data is the NFHS-5th (2019-21) round taken, conducted by the IIPS under the administration of MoHFW, India. This study only included 315,069 women under reproductive age (WRA). The variables related to anaemia among women's (WRA) background socio-demographic characteristics were assessed using bivariate statistics and multinominal logistic regression analysis to comprehend the spatial correlation between women and their determinant factors. Among the EAG states, the overall prevalence of anaemia was 57%, varying from 42.6% in Uttarakhand to 65.3% in Jharkhand. Multinominal logistic regression analyses reveal that the chances of anaemia are remarkably more prevalent in younger women (15-19 years of age), women living in rural areas, no educated and primary level educated women, women belonging to the middle to poorest wealth quintile, women no longer living together, women of the Christian religion, women who are not exposed to reading newspapers, underweight BMI women, and scheduled tribe women. Mainly, the prevalence is observed in the North-eastern and southeastern states of Bihar, Jharkhand, Odisha, Chhattisgarh, some parts of Madhya Pradesh, Uttar Pradesh, and Rajasthan, which is shown by the hotspot map. According to the findings of this study, numerous factors like family, socioeconomic, educational, awareness, and individual characteristics such as caste and domicile all lead to a risk of anaemia. The WRA suffers from anaemia as a result of their socioeconomic background and awareness, which leads to a lack of nourishment, and they seek nutrient deficiencies. To overcome this anaemia, multiple discipline policies and initiatives need to be taken targeting women's wellness and nutritional status by increasing women's education and socioeconomic status.

Measurement and spatial correlation analysis of high-quality development Level: A case study of the Yangtze River Delta urban agglomeration in China.

Against the backdrop of slowing economic growth and increasing environmental pressure, the Yangtze River Delta city cluster, as one of the largest city clusters in the world, has become more driven in its pursuit of high-quality development. We constructed a system of 24 evaluation indexes and used entropy-weighted TOPSIS to calculate and study the high-quality development index of urban agglomerations in the region. First, the level of high quality development (HQD) of the Yangtze River Delta city cluster generally improved from 2010 to 2021, with 2017 was the best year, while 2010 was the worst year. Second, in the multidimensional evaluation of HQD, Jiangsu excels in innovation and people's livelihood with 0.524 and 0.534, respectively; Shanghai (0.531) excels in coordinated development; Zhejiang excels in green and economic development with 0.557 and 0.484, respectively; and Anhui lags behind in all aspects. Third, the development process of HQD in the Yangtze River Delta region is uneven, and the level of HQD development varies greatly among the city clusters in the province. The measurement results show that Shanghai (0.511) has the highest score, followed by Zhejiang (0.484), Jiangsu (0.440) and Anhui (0.435). Fourth, spatial correlation analysis shows that Shanghai and Jiangsu are mainly distributed in the double-high region, Zhejiang is distributed in the high-low region, while Anhui is concentrated in the low-low region. The results of this study help us understand more deeply the characteristics and challenges of high-quality development in the Yangtze River Delta urban agglomerations and provide a scientific basis for more precise urban development policies.

Mother of all bonds: Influences on spatial association across the lifespan in capuchins.

In humans, being more socially integrated is associated with better physical and mental health and/or with lower mortality. This link between sociality and health may have ancient roots: sociality also predicts survival or reproduction in other mammals, such as rats, dolphins, and non-human primates. A key question, therefore, is which factors influence the degree of sociality over the life course. Longitudinal data can provide valuable insight into how environmental variability drives individual differences in sociality and associated outcomes. The first year of life-when long-lived mammals are the most reliant on others for nourishment and protection-is likely to play an important role in how individuals learn to integrate into groups. Using behavioral, demographic, and pedigree information on 376 wild capuchin monkeys (Cebus imitator) across 20 years, we address how changes in group composition influence spatial association. We further try to determine the extent to which early maternal social environments have downstream effects on sociality across the juvenile and (sub)adult stages. We find a positive effect of early maternal spatial association, where female infants whose mothers spent more time around others also later spent more time around others as juveniles and subadults. Our results also highlight the importance of kin availability and other aspects of group composition (e.g., group size) in dynamically influencing spatial association across developmental stages. We bring attention to the importance of-and difficulty in-determining the social versus genetic influences that parents have on offspring phenotypes. RESEARCH HIGHLIGHTS: Having more maternal kin (mother and siblings) is associated with spending more time near others across developmental stages in both male and female capuchins. Having more offspring as a subadult or adult female is additionally associated with spending more time near others. A mother's average sociality (time near others) is predictive of how social her daughters (but not sons) become as juveniles and subadults (a between-mother effect). Additional variation within sibling sets in this same maternal phenotype is not predictive of how social they become later relative to each other (no within-mother effect).

Mining significant local spatial association rules for multi-category point data.

Spatial association rule mining can reveal the inherent laws of spatial object interdependence and is an important part of spatial data mining. Most of the existing algorithms for mining local spatial association rules are oriented towards the spatial association between two categories of points and cannot fully reflect the spatial heterogeneity of complex spatial relations among multiple categories of points. In addition, the interactions between points in different categories are often asymmetrical. However, the existing algorithms ignore this asymmetry. To address the above problems, an algorithm for mining local spatial association rules for point data of multiple categories based on position quotients is proposed. First, the proximity relationship between points is determined by an adaptive filter, and the spatial weight value is given according to Gaussian kernel function. Then, the multivariate local colocation quotient of each point is calculated to measure the strength of the local regional spatial association rule. Finally, the Monte Carlo simulation function is used to generate a random sample distribution to test the significance of the results. The algorithm is verified on artificial simulation data and real Point of Interest (POI) data. The experimental results show that the algorithm can identify significant association regions of different spatial association rules for point sets.

Epidemiological features of seasonal influenza transmission among 11 climate zones in Chinese Mainland.

BACKGROUND: Previous studies provided some evidence of meteorological factors influence seasonal influenza transmission patterns varying across regions and latitudes. However, research on seasonal influenza activities based on climate zones are still in lack. This study aims to utilize the ecological-based Köppen Geiger climate zones classification system to compare the spatial and temporal epidemiological characteristics of seasonal influenza in Chinese Mainland and assess the feasibility of developing an early warning system. METHODS: Weekly influenza cases number from 2014 to 2019 at the county and city level were sourced from China National Notifiable Infectious Disease Report Information System. Epidemic temporal indices, time series seasonality decomposition, spatial modelling theories including Moran's I and local indicators of spatial association were applied to identify the spatial and temporal patterns of influenza transmission. RESULTS: All climate zones had peaks in Winter-Spring season. Arid, desert, cold (BWk) showed up the first peak. Only Tropical, savannah (Aw) and Temperate, dry winter with hot summer (Cwa) zones had unique summer peak. Temperate, no dry season and hot summer (Cfa) zone had highest average incidence rate (IR) at 1.047/100,000. The Global Moran's I showed that average IR had significant clustered trend (z = 53.69, P < 0.001), with local Moran's I identified high-high cluster in Cfa and Cwa. IR differed among three age groups between climate zones (0-14 years old: F = 26.80, P < 0.001; 15-64 years old: F = 25.04, P < 0.001; Above 65 years old: F = 5.27, P < 0.001). Age group 0-14 years had highest average IR in Cwa and Cfa (IR = 6.23 and 6.21) with unique dual peaks in winter and spring season showed by seasonality decomposition. CONCLUSIONS: Seasonal influenza exhibited distinct spatial and temporal patterns in different climate zones. Seasonal influenza primarily emerged in BWk, subsequently in Cfa and Cwa. Cfa, Cwa and BSk pose high risk for seasonal influenza epidemics. The research finds will provide scientific evidence for developing seasonal influenza early warning system based on climate zones.

Unveiling spatial inequalities: Exploring county-level disaster damages and social vulnerability on public disaster assistance in contiguous US.

Understanding the dynamics between public disaster assistance, disaster damages, and social vulnerability at county-level is crucial for designing effective disaster mitigation strategies. This study utilized the Local Bivariate Moran Index (LBMI) and geographically weighted regression (GWR) models to examine spatial patterns and relationships between disaster damages, social vulnerability, and public disaster assistance in contiguous US counties from 2001 to 2021. LBMI results reveal that public disaster assistance has predominantly been directed towards post-disaster recovery efforts, with a particular focus on coastal communities affected by major declared disasters. However, the distributions of public assistance and individual housing assistance, which are the two primary sources of public disaster assistance, do not adequately cover physically and socially vulnerable communities. The distribution of pre-disaster risk mitigation also falls short of sufficiently covering vulnerable communities. Results further indicate the complex interactions between different categories of natural disasters and public assistances. The GWR model results demonstrate spatial variations in predicting each category of public disaster assistance. These findings indicate the need to address disparities in accessing public disaster assistance in the US, and advocate for more equitable disaster mitigation strategies.

Machine learning-based prediction of cadmium bioaccumulation capacity and associated analysis of driving factors in tobacco grown in Zunyi City, China.

Tobacco grown in areas with high-geochemical backgrounds exhibits considerably different cadmium (Cd) bioaccumulation abilities due to regional disparities and environmental changes. However, the impact of key factors on the Cd bioaccumulation ability of tobacco grown in the karst regions with high selenium (Se) geochemical backgrounds is unclear. Herein, 365 paired rhizospheric soil-grown tobacco samples and 321 topsoil samples were collected from typical karst tobacco-growing soil in southwestern China and analyzed for Cd and Se. XGBoost was used to predict and evaluate the Cd bioaccumulation ability of tobacco and potential influencing factors. Results showed that regional geochemical characteristics, such as soil Cd and Se contents, soil type, and lithology, have the highest influence on the Cd bioaccumulation ability of tobacco, accounting for 46.5% of the overall variation. Moreover, soil Se contents in high-geochemical background areas considerably affect Cd bioaccumulation in tobacco, with a threshold for the mutual suppression effects of Cd and Se at a soil Se content of 0.8 mg/kg. According to the results of bivariate local indicators of spatial association analysis, tobacco cultivated in the central, northeast, and southeast regions of Zunyi City carries a lower risk of soil Cd contamination. This study provides new insights for managing tobacco cultivation in karst regions.

Study on the spatial correlation effects and influencing factors of carbon emissions from the electricity industry: a fresh evidence from China.

Carbon emissions from the electricity industry (CEEI) account for a large proportion of China's total carbon emissions, and it is important to study the spatial correlation between CEEI and the influencing factors to promote cross-regional synergistic emission reduction and low-carbon development of the power system. In this paper, the quasi-input-output (QIO) model is applied to assess the transfer of carbon emissions generated by electricity trading based on the consideration of electricity carbon transfer, and the exploratory spatial data analysis (ESDA) method is applied to analyze the spatial correlation effect of carbon emissions from China's electric power sector from 2001 to 2020, analyzes its distribution pattern in both spatial and temporal dimensions, and applies the improved logarithmic mean Divisia index (LMDI) two-stage decomposition model to decompose the changes in CEEI into 11 influencing factors from the perspective of the whole industrial chain of power production, transmission, trade, and consumption. The research results show that (1) the spatial distribution of CEEI has obvious unevenness and aggregation characteristics, with high-high aggregation areas and hot spot aggregation areas generally concentrated in the North China Power Grid and the East China Power Grid, but the aggregation trend is gradually decreasing, while low-low aggregation areas and cold spot aggregation areas are concentrated in the Northwest China Power Grid and the Central China Power Grid, but the area is very limited. (2) The direction of carbon emission diffusion in China's electricity industry is gradually transitioning from southwest-northeast to northwest-southeast, and the east-west diffusion trend is stronger than the north-south diffusion trend and carbon emissions are gradually shifting to the northwest grid. (3) The total amount of electricity production is the most influential factor in the change of CEEI, driving the cumulative growth of CEEI by 4495.34 Mt, followed by GDP per capita and electricity consumption intensity. Coal consumption for power generation, the share of thermal power, and net electricity exports were the main factors inhibiting the increase in carbon emissions from the power sector, with cumulative contributions of -797.74 Mt, -619.99 Mt, and -47.76 Mt, respectively.

Investigation of spatial association network features of construction waste in major Chinese urban agglomeration.

The illegal dumping of construction waste (CW) poses an increasingly serious environmental pollution problem with the accelerated rate of urbanization. As CW disposal capacity struggles to match municipal needs, some CW is being diverted to higher resource endowment cities rather than recycled. To address this situation, it is necessary to obtain reliable information on the characteristics and evolution of CW generation networks in China. This study combines a modified gravity model with Social Network Analysis (SNA) to analyze the spatial association networks of CW generation in four Chinese urban agglomerations between 2000 and 2020. Results reveal the evolution characteristics of the CW generation network, including increasing density and correlation and decreasing network efficiency. Furthermore, the Quality Assurance Procedure (QAP) indicates that urbanization level and population size are positively correlated with CW generations, whereas distance plays a negative role, but resources are insignificant for network formation. The findings provide insight into current patterns of waste distribution and a theoretical basis for government policy formulation in the future.

Within-field spatial patterns of Helicoverpa zea (Lepidoptera: Noctuidae) and spatial associations with stink bugs and their injury in field corn.

The corn earworm, Helicoverpa zea (Boddie) (Lepidoptera: Noctuidae), is a cosmopolitan pest in the field crop landscape in the southeastern United States. Field corn (Zea mays L.) is the most important midseason host for H. zea where intensive selection pressure occurs for resistance to insecticidal toxins from Bacillus thuringiensis (Bt). Because spatial patterns of H. zea in field corn have not been extensively studied, field corn was sampled for H. zea larvae and injury in 2021 and 2022. Patterns of spatial aggregation were identified in a number of fields in both larval populations and injury. Aggregation of H. zea larvae was less common at R5 than at R2. Associations between the spatial patterns of H. zea and the variability in crop phenology were identified in some fields, with positive associations between plant height and H. zea larvae, indicating that ovipositing H. zea moths avoid areas with reduced plant height and delayed reproductive maturity. Additionally, negative spatial associations between stink bug ear injury and H. zea larvae and their injury were found in a small number of cases, indicating some spatial interactions between the two pest complexes and their injury. Results from these studies provide valuable insight into the spatial patterns of H. zea in field corn. An understanding of the local dispersal and population dynamics of H. zea can be used to help further improve integrated pest management and insect resistance management programs for this major polyphagous pest.

An artificial intelligence-based ecological index for prognostic evaluation of colorectal cancer.

BACKGROUND AND OBJECTIVE: In the tumor microenvironment (TME), the dynamic interaction between tumor cells and immune cells plays a critical role in predicting the prognosis of colorectal cancer. This study introduces a novel approach based on artificial intelligence (AI) and immunohistochemistry (IHC)-stained whole-slide images (WSIs) of colorectal cancer (CRC) patients to quantitatively assess the spatial associations between tumor cells and immune cells. To achieve this, we employ the Morisita-Horn ecological index (Mor-index), which allows for a comprehensive analysis of the spatial distribution patterns between tumor cells and immune cells within the TME. MATERIALS AND METHODS: In this study, we employed a combination of deep learning technology and traditional computer segmentation methods to accurately segment the tumor nuclei, immune nuclei, and stroma nuclei within the tumor regions of IHC-stained WSIs. The Mor-index was used to assess the spatial association between tumor cells and immune cells in TME of CRC patients by obtaining the results of cell nuclei segmentation. A discovery cohort (N = 432) and validation cohort (N = 137) were used to evaluate the prognostic value of the Mor-index for overall survival (OS). RESULTS: The efficacy of our method was demonstrated through experiments conducted on two datasets comprising a total of 569 patients. Compared to other studies, our method is not only superior to the QuPath tool but also produces better segmentation results with an accuracy of 0.85. Mor-index was quantified automatically by our method. Survival analysis indicated that the higher Mor-index correlated with better OS in the discovery cohorts (HR for high vs. low 0.49, 95% CI 0.27-0.77, P = 0.0014) and validation cohort (0.21, 0.10-0.46, < 0.0001). CONCLUSION: This study provided a novel AI-based approach to segmenting various nuclei in the TME. The Mor-index can reflect the immune status of CRC patients and is associated with favorable survival. Thus, Mor-index can potentially make a significant role in aiding clinical prognosis and decision-making.

Spatiotemporal evolution and characteristics of worldwide life expectancy.

Exploring global differences in life expectancy can facilitate the development of strategies to narrow regional disparities. However, few researchers have systematically examined patterns in the evolution of worldwide life expectancy over a long time period. Spatial differences among 181 countries in 4 types of worldwide life expectancy patterns from 1990 to 2019 were investigated via geographic information system (GIS) analysis. The aggregation characteristics of the spatiotemporal evolution of life expectancy were revealed by local indicators of spatial association. The analysis employed spatiotemporal sequence-based kernel density estimation and explored the differences in life expectancy among regions with the Theil index. We found that the global life expectancy progress rate shows upward then downward patterns over the last 30 years. Female have higher rates of spatiotemporal progression in life expectancy than male, with less internal variation and a wider spatial aggregation. The global spatial and temporal autocorrelation of life expectancy shows a weakening trend. The difference in life expectancy between male and female is reflected in both intrinsic causes of biological differences and extrinsic causes such as environment and lifestyle habits. Investment in education pulls apart differences in life expectancy over long time series. These results provide scientific guidelines for obtaining the highest possible level of health in countries around the world.

Spatial association network of PM2.5 and its influencing factors in the Beijing-Tianjin-Hebei urban agglomeration.

In this paper, we empirically study the spatial association network of PM2.5 and the factors influencing those correlations using the gravity model, social network analysis (SNA), and the quadratic assignment procedure (QAP) based on data from the Beijing-Tianjin-Hebei urban agglomeration (BTHUA) in China from 2005 to 2018. We draw the following conclusions. First, the spatial association network of PM2.5 exhibits relatively typical network structure characteristics: the network density and network correlations are highly sensitive to efforts to control air pollution, and there are obvious spatial correlations within the network. Second, cities in the center of the BTHUA have large network centrality values, while cities in the peripheral region have small centrality values. Tianjin is a core city in the network, and the spillover effect of PM2.5 pollution in Shijiazhuang and Hengshui is the most noticeable. Third, the 14 cities can be divided into four plates, with each plate having obvious geographical location characteristics and linkage effects. The cities in the association network are divided into three tiers. Beijing, Tianjin, and Shijiazhuang are located in the first tier, and a considerable number of PM2.5 connections are completed through these cities. Fourth, differences in geographical distance and urbanization are the main drivers of the spatial correlations of PM2.5. The greater the urbanization differences, the more likely the generation of PM2.5 links is, while the opposite is true for differences in geographical distance.

The Pattern of Social Parasitism in Maculinea teleius Butterfly Is Driven by the Size and Spatial Distribution of the Host Ant Nests.

The parasitic relationship between Maculinea butterflies and Myrmica ants has been extensively studied but little information is available on the spatial occurrence of Maculinea larvae. We searched for the presence of Maculinea teleius in 211 ant nests at two sites in two crucial phases of its life cycle, i.e., in autumn, during the initial larval development, and in the following late spring, before pupation. We assessed variations in the proportion of infested nests and factors correlated with spatial distributions of parasites in Myrmica colonies. The parasitism rate in autumn was very high (∼50% of infestation rate) but decreased in the following spring. The most important factor explaining parasite occurrence in both seasons was the nest size. Further factors, such as the presence of other parasites, the Myrmica species or the site, concurred to explain the differential survival of Ma. teleius until the final development. Irrespective of the host nest distribution, the parasite distribution changed from even in autumn to clumped in late spring. Our work showed that the survival of Ma. teleius is correlated with colony features but also with the nest spatial distribution, which therefore should be taken into consideration in conservation strategies aiming at preserving these endangered species.

Analysing the global and local spatial associations of medical resources across Wuhan city using POI data.

BACKGROUND: There is a sharp contradiction between the supply and demand of medical resources in the provincial capitals of China. Understanding the spatial patterns of medical resources and identifying their spatial association and heterogeneity is a prerequisite to ensuring that limited resources are allocated fairly and optimally, which, along with improvements to urban residents' quality of life, is a key aim of healthy city planning. However, the existing studies on medical resources pattern mainly focus on their spatial distribution and evolution characteristics, and lack the analyses of the spatial co-location between medical resources from the global and local perspectives. It is worth noting that the research on the spatial relationship between medical resources is an important way to realize the spatial equity and operation efficiency of urban medical resources. METHODS: Localized colocation quotient (LCLQ) analysis has been used successfully to measure directional spatial associations and heterogeneity between categorical point data. Using point of interest (POI) data and the LCLQ method, this paper presents the first analysis of spatial patterns and directional spatial associations between six medical resources across Wuhan city. RESULTS: (1) Pharmacies, clinics and community hospitals show "multicentre + multicircle", "centre + axis + dot" and "banded" distribution characteristics, respectively, but specialized hospitals and general hospitals present "single core" and "double core" modes. (2) Overall, medical resources show agglomeration characteristics. The degrees of spatial agglomeration of the five medical resources, are ranked from high to low as follows: pharmacy, clinic, community hospital, special hospital, general hospital and 3A hospital. (3) Although pharmacies, clinics, and community hospitals of basic medical resources are interdependent, specialized hospitals, general hospitals and 3A hospitals of professional medical resources are also interdependent; furthermore, basic medical resources and professional medical resources are mutually exclusive. CONCLUSIONS: Government and urban planners should pay great attention to the spatial distribution characteristics and association intensity of medical resources when formulating relevant policies. The findings of this study contribute to health equity and health policy discussions around basic medical services and professional medical services.

Spatial shifting of COVID-19 clusters and disease association with environmental parameters in India: A time series analysis.

BACKGROUND: The viability and virulence of COVID-19 are complex in nature. Although the relationship between environmental parameters and COVID-19 is well studied across the globe, in India, such studies are limited. This research aims to explore long-term exposure to weather conditions and the role of air pollution on the infection spread and mortality due to COVID-19 in India. METHOD: District-level COVID-19 data from April 26, 2020 to July 10, 2021 was used for the study. Environmental determinants such as land surface temperature, relative humidity (RH), Sulphur dioxide (SO2), Nitrogen dioxide (NO2), Ozone (O3), and Aerosol Optical Depth (AOD) were considered for analysis. The bivariate spatial association was used to explore the spatial relationship between Case Fatality Rate (CFR) and these environmental factors. Further, the Bayesian multivariate linear regression model was applied to observe the association between environmental factors and the CFR of COVID-19. RESULTS: Spatial shifting of COVID-19 cases from Western to Southern and then Eastern parts of India were well observed. The infection rate was highly concentrated in most of the Western and Southern regions of India, while the CFR shows more concentration in Northern India along with Maharashtra. Four main spatial clusters of infection were recognized during the study period. The time-series analysis indicates significantly more CFR with higher AOD, O3, and NO2 in India. CONCLUSIONS: COVID-19 is highly associated with environmental parameters and air pollution in India. The study provides evidence to warrant consideration of environmental parameters in health models to mediate potential solutions. Cleaner air is a must to mitigate COVID-19.

Can human activities enhance the trade-off intensity of ecosystem services in arid inland river basins? Taking the Taolai River asin as an example.

Driven by economic and social factors, more and more human beings intervene in nature to promote rapid economic and social development at the expense of ecosystem services (ES), which inevitably leads to the occurrence and even aggravation of ES trade-offs. Especially in the arid inland river basin is more serious. Therefore, this paper takes the Taolai River Basin as an example and uses the InVEST model to evaluate the spatial distribution of four typical ES, including carbon sequestration, oxygen release, windbreak and sand fixation, and water production, under the potential-actual states of the watershed. And use the Pearson correlation coefficient and the root mean square error (RMSE) to analyze the trade-off relationship between services from qualitative and quantitative aspects, respectively. Finally, the spatial matching types of trade-offs in the potential-actual states are discussed using Bivariate Local Indicators of Spatial Association, and the degree and scope of the impact of human activities on trade-offs are analyzed. The results show that the spatial distribution of the four ES has obvious heterogeneity in the potential-actual states, and the service volume of most services in the potential state is much higher than in the actual state. Secondly, there is a significant trade-offs relationship between Water production and Carbon sequestration and Oxygen release services under the potential state, while the actual state under the impact of human activities shows a significant synergistic relationship, which shows that human activities will not only increase the probability of trade-off will also increase the probability of synergy between ES. Finally, through the analysis of the meaning and causes of "high and low space dislocation" and "low and high space dislocation", it is shown that human activities will not only increase but also weaken the trade-off intensity of ES. The results of this study can provide a certain scientific basis for regional ecological environment planning and promote regional people to share ecological well-being.

Interspecific association of sika deer in terrestrial animal communities of Liancheng National Nature Reserve, China.

The prevention and control of invasive of alien species is an important work for nature reserves. This study analyzes the development trend of the alien species sika deer in Liancheng National Nature Reserve. From October 2019 to June 2020, 3523 valid photos and videos of terrestrial animals were acquired from 130 camera traps, and sika deer were recorded in 21 photos from 13 traps. The survival of the sika deer population was investigated by means of morphological identification, population structure analysis, species relative abundance indices, and species spatial association analysis. A total of 13 sika deer individuals were identified by camera trapping, including two kids and three subadults representing the reproductive capacity of the population. Spatially, sika deer is not associated with any local species and was outside the spatial association network of terrestrial animals in Liancheng National Nature Reserve, indicating that the sika deer population has not been integrated into the local community and has failed to perform its ecological function. It is worth noting that the reserve provides habitat suitable for sika deer and that the population has adequate reproductive capacity. Due to the lack of large apex predators in the reserve, the population size of ungulates such as sika deer, red deer, and Siberian roe deer may expand and lead to population outbreaks and the associated problems for the ecosystem. To restore large- and medium-sized carnivores and avoid the population outbreak of the species, the present challenges require immediate attention in Liancheng National Nature Reserve.

The Ecological Roles of Medium and Small Carnivores in the Terrestrial Animal Community in Liancheng National Nature Reserve, China.

It is vitally important to understand the ecological roles of medium and small carnivores in the context of the massive decline in the number of large carnivores around the world. Based on a spatial association network of terrestrial birds and mammals, this study analyzed the ecological roles of medium and small carnivores in the community in Liancheng National Nature Reserve. From October 2019 to June 2020, we obtained 3559 independent detections of 20 terrestrial birds and mammals from 112 camera traps. There are seven species that are medium and small carnivores present in the study area, including red fox (Vulpes vulpes), leopard cat (Prionailurus bengalensis), Chinese mountain cat (Felis bieti), stone marten (Martes foina), Asian badger (Meles leucurus), Siberian weasel (Mustela sibirica) and mountain weasel (Mustela altaica). By calculating the Phi coefficient of all species pairs, a spatial association network composed of twelve species was constructed. We analyzed the characterization of spatial associations by the Shannon-Wiener index and Lambda statistic. The results showed that: (1) the status of the network reflects the changes of community composition and structure after the decline in large carnivores and other species; (2) with the exception of the Chinese mountain cat and stone marten, the other five medium and small carnivores were located in the network, which played an important role in the complexity of the network and the maintenance of the community; (3) the medium and small carnivores could not take the place of the large carnivores in order to control the population of herbivores, such as Siberian roe deer (Capreolus pygargus) and Himalayan marmot (Marmota himalayana). The results of this study provide guidance for determining the direction and focus of conservation efforts.