The Spatio-Temporal Distribution of Suicide-related Emergency Calls in a European City: Age and Gender Patterns, and Neighborhood Influences

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.

Spatio-temporal analysis and risk modeling of foot-and-mouth disease outbreaks in China

FMD is an acute contagious disease that poses a significant threat to the health and safety of cloven-hoofed animals in Asia, Europe, and Africa. The impact of FMD exhibits geographical disparities within different regions of China. The present investigation undertook an exhaustive analysis of documented occurrences of bovine FMD in China, spanning the temporal range from 2011 to 2020. The overarching objective was to elucidate the temporal and spatial dynamics underpinning these outbreaks. Acknowledging the pivotal role of global factors in FMD outbreaks, advanced machine learning techniques were harnessed to formulate an optimal prediction model by integrating comprehensive meteorological data pertinent to global FMD. Random Forest algorithm was employed with top three contributing factors including Isothermality(bio3), Annual average temperature(bio1) and Minimum temperature in the coldest month(bio6), all relevant to temperature. By encompassing both local and global factors, our study provides a comprehensive framework for understanding and predicting FMD outbreaks. Furthermore, we conducted a phylogenetic analysis to trace the origin of Foot-and-mouth disease virus (FMDV), pinpointing India as the country posing the greatest potential hazard by leveraging the spatio-temporal attributes of the collected data. Based on this finding, a quantitative risk model was developed for the legal importation of live cattle from India to China. The model estimated an average probability of 0.002254% for FMDV-infected cattle imported from India to China. TA sensitivity analysis identified two critical nodes within the model: he possibility of false negative clinical examination in infected cattle at destination (P5) and he possibility of false negative clinical examination in infected cattle at source(P3). This comprehensive approach offers a thorough evaluation of FMD landscape within China, considering both domestic and global perspectives, thereby augmenting the efficacy of early warning mechanisms.

Spatial and temporal trends of dengue infections in Curaçao: A 21-year analysis

Dengue viruses are a significant global health concern, causing millions of infections annually and putting approximately half of the world's population at risk, as reported by the World Health Organization (WHO). Understanding the spatial and temporal patterns of dengue virus spread is crucial for effective prevention of future outbreaks. By investigating these patterns, targeted dengue surveillance and control measures can be improved, aiding in the management of outbreaks in dengue-affected regions. Curaçao, where dengue is endemic, has experienced frequent outbreaks over the past 25 years. To examine the spatial and temporal trends of dengue outbreaks in Curaçao, this study employs an interdisciplinary and multi-method approach. Data on >6500 cases of dengue infections in Curaçao between the years 1995 and 2016 were used. Temporal and spatial statistics were applied. The Moran's I index identified the presence of spatial autocorrelation for incident locations, allowing us to reject the null hypothesis of spatial randomness. The majority of cases were recorded in highly populated areas and a relationship was observed between population density and dengue cases. Temporal analysis demonstrated that cases mostly occurred from October to January, during the rainy season. Lower average temperatures, higher precipitation and a lower sea surface temperature appear to be related to an increase in dengue cases. This effect has a direct link to La Niña episodes, which is the cooling phase of El Niño Southern Oscillation. The spatial and temporal analyses conducted in this study are fundamental to understanding the timing and locations of outbreaks, and ultimately improving dengue outbreak management.

Spatio-temporal effects of digital inclusive finance on the synergy between CO2 and air pollution emissions in 251 Chinese cities

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.

Spatio-temporal dynamics and distributional trend analysis of African swine fever outbreaks (2020-2021) in North-East India

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.

Spatio-temporal evolutionary analysis of surface ecological quality in Pingshuo open-cast mine area, China

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.

Integrated water security and coupling of social-ecological system to improve river basin sustainability

The river basin sustainability depends on both the coordinated development of socio-ecological systems and resilience to water resources. However, the lack of integrating them on spatial and temporal scales compromises our capacity to develop precise interventions towards sustainable river basins. We developed an approach by integrating water security and social-ecological coupling to assess the river basin sustainability. We divided it into four categories including highly sustainable (secure and coordinated), insecure, uncoordinated, and low sustainable (insecure and uncoordinated). The middle reach of Heihe River (MHR) was taken as the study area with the sub-basin as the spatial analysis unit from 2000 to 2020. The results showed that there was heterogeneity and agglomeration in spatial distribution. 23.8 %, 38.8 %, and 11% of the sub-basins mainly clustered in the north and central areas were found in the state of water insecure and SES uncoordinated, or both respectively. The unsustainable areas (five sub-basins) and lose-lose areas (two sub-basins) should be the priority areas for management interventions. Our approach can provide an important reference for assessing and improving the river basin sustainability.

Social disparities and inequalities in healthcare access and expenditures among Iranians exposed to sulfur mustard: a national study using spatio-temporal analysis

BACKGROUND

Sulfur Mustard (SM) is a chemical warfare agent that has serious short-term and long-term effects on health. Thousands of Iranians were exposed to SM during the eight-year Iran-Iraq conflict and permanently injured while the socioeconomic imbalance in their healthcare utilization (HCU) and health expenditures remains. This study aims to describe the HCU of SM-exposed survivors in Iran from 2018 to 2021; identify high-risk areas; and apply an inequality analysis of utilization regarding the socioeconomic groups to reduce the gap by controlling crucial determinants.

METHODS

From Oct 2018 to June 2021, the Veterans and Martyrs Affairs Foundation (VMAF) recorded 58,888 living war survivors with eye, lung, and skin ailments. After cleaning the dataset and removing junk codes, we defined 11 HCU-related variables and predicted the HCU for the upcoming years using Bayesian spatio-temporal models. We explored the association of individual-level HCU and determinants using a Zero-inflated Poisson (ZIP) model and also investigated the provincial hotspots using Local Moran's I.

RESULTS

With ≥ 90% confidence, we discovered eleven HCU clusters in Iran. We discovered that the expected number of HCU 1) rises with increasing age, severity of complications in survivors' eyes and lungs, wealth index (WI), life expectancy (LE), and hospital beds ratio; and 2) decreases with growing skin complications, years of schooling (YOS), urbanization, number of hospital beds, length of stay (LOS) in bed, and bed occupancy rate (BOR). The concentration index (CInd) of HCU and associated costs in age and wealth groups were all positive, however, the signs of CInd values for HCU and total cost in YOS, urbanization, LOS, and Hospital beds ratio groups were not identical.

CONCLUSIONS

We observed a tendency of pro-rich inequity and also higher HCU and expenditures for the elderly population. Finally, health policies should tackle potential socioeconomic inequities to reduce HCU gaps in the SM-exposed population. Also, policymakers should allocate the resources according to the hotspots of HCU.

The emergence and successful elimination of SARS-CoV-2 dominant strains with increasing epidemic potential in Taiwan's 2021 outbreak

Taiwan's experience with severe acute respiratory syndrome coronavirus (SARS-CoV) in 2003 guided its development of strategies to defend against SARS-CoV-2 in 2020, which enabled the successful control of Coronavirus disease 2019 (COVID-19) cases from 2020 through March 2021. However, in late-April 2021, the imported Alpha variant began to cause COVID-19 outbreaks at an exceptional rate in Taiwan. In this study, we aimed to determine what epidemiological conditions enabled the SARS-CoV-2 Alpha variant strains to become dominant and decline later during a surge in the outbreak. In conjunction with contact-tracing investigations, we used our bioinformatics software, CoVConvert and IniCoV, to analyze whole-genome sequences of 101 Taiwan Alpha strains. Univariate and multivariable regression analyses revealed the epidemiological factors associated with viral dominance. Univariate analysis showed the dominant Alpha strains were preferentially selected in the surge's epicenter (p = 0.0024) through intensive human-to-human contact and maintained their dominance for 1.5 months until the Zero-COVID Policy was implemented. Multivariable regression found that the epidemic periods (p = 0.007) and epicenter (p = 0.001) were two significant factors associated with the dominant virus strains spread in the community. These dominant virus strains emerged at the outbreak's epicenter with frequent human-to-human contact and low vaccination coverage. The Level 3 Restrictions and Zero-COVID policy successfully controlled the outbreak in the community without city lockdowns. Our integrated method can identify the epidemiological conditions for emerging dominant virus with increasing epidemiological potential and support decision makers in rapidly containing outbreaks using public health measures that target fast-spreading virus strains.

Influence of urbanization on schistosomiasis infection risk in Anhui Province based on sixteen year's longitudinal surveillance data: a spatio-temporal modelling study

BACKGROUND

Urbanization greatly affects the natural and social environment of human existence and may have a multifactoral impact on parasitic diseases. Schistosomiasis, a common parasitic disease transmitted by the snail Oncomelania hupensis, is mainly found in areas with population aggregations along rivers and lakes where snails live. Previous studies have suggested that factors related to urbanization may influence the infection risk of schistosomiasis, but this association remains unclear. This study aimed to analyse the effect of urbanization on schistosomiasis infection risk from a spatial and temporal perspective in the endemic areas along the Yangtze River Basin in China.

METHODS

County-level schistosomiasis surveillance data and natural environmental factor data covering the whole Anhui Province were collected. The urbanization level was characterized based on night-time light data from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and the National Polar-Orbiting Partnership's Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). The geographically and temporally weighted regression model (GTWR) was used to quantify the influence of urbanization on schistosomiasis infection risk with the other potential risk factors controlled. The regression coefficient of urbanization was tested for significance (α = 0.05), and the influence of urbanization on schistosomiasis infection risk was analysed over time and across space based on significant regression coefficients. Variables studied included climate, soil, vegetation, hydrology and topography.

RESULTS

The mean regression coefficient for urbanization (0.167) is second only to the leached soil area (0.300), which shows that the urbanization is the most important influence factors for schistosomiasis infection risk besides leached soil area. The other important variables are distance to the nearest water source (0.165), mean minimum temperature (0.130), broadleaf forest area (0.105), amount of precipitation (0.073), surface temperature (0.066), soil bulk density (0.037) and grassland area (0.031). The influence of urbanization on schistosomiasis infection risk showed a decreasing trend year by year. During the study period, the significant coefficient of urbanization level increased from - 0.205 to - 0.131.

CONCLUSIONS

The influence of urbanization on schistosomiasis infection has spatio-temporal heterogeneous. The urbanization does reduce the risk of schistosomiasis infection to some extend, but the strength of this influence decreases with increasing urbanization. Additionally, the effect of urbanization on schistosomiasis infection risk was greater than previous reported natural environmental factors. This study provides scientific basis for understanding the influence of urbanization on schistosomiasis, and also provides the feasible research methods for other similar studies to answer the issue about the impact of urbanization on disease risk.

Prediction of fishing intensity and trends across South China Sea biogeographic zones

The volume of industrial fishing in the South China Sea ranks among the top global sustainable fisheries concerns of the Food and Agriculture Organization (FAO). To better understand the scale of management challenges, biogeographic zones of the SCS were characterized, and within each a multivariate GAM (General Additive Model) was fitted to predict and map the complete fishing activities from 2017 to 2020. Model variables, some incomplete or with gaps, included: VIIRS DNB night-time light imagery; Global Fisheries Watch (GFW) data; satellite Ocean Colour; Sea Surface Temperature; and bathymetry data. Four biogeographic zones with differing fishing patterns and trends were identified. We used cross-validation and the GAM model's own tuning method for model prediction accuracy determination, which performed well in four biogeographic zones (R2 respectively: 0.62, 0.68, 0.74 and 0.71). High-intensity fishing grounds are mainly distributed in offshore continental shelf areas. From 2017 to 2019, high-intensity fishing grounds were located near the Beibu Gulf of Vietnam, south Vietnam, part of the Gulf of Thailand and the central Java Sea, where fishing effort greater than 50 h exceeded average annual SCS fishing intensity for several years. By season, intensity and extent of fishing in Spring were largest. In 2020, due to the impact of COVID-19, except for Spring, fishing volume generally decreased. Our experimental results provide new insights and an adaptable biogeographic modelling methodology to map the scale and intensity of regional fishing activities more accurately and completely. This more comprehensive database, that takes account of intrinsic biogeographic fishery context, will help improve and strengthen the regulation of fishing activities around the world.

The spatial spread of HIV in Malawi: An individual-based mathematical model

Background

The prevalence of HIV varies greatly between and within countries. We aimed to build a comprehensive mathematical modelling tool capable of exploring the reasons of this heterogeneity and test its applicability by simulating the Malawian HIV epidemic.

Methods

We developed a flexible individual-based mathematical model for HIV transmission that comprises a spatial representation and individual-level determinants. We tested this model by calibrating it to the HIV epidemic in Malawi and exploring whether the heterogeneity in HIV prevalence could be reproduced. We ran the model for 1975-2030 with five alternative realizations of the geographical structure and mobility: (I) no geographical structure; 28 administrative districts including (II) only permanent inter-district relocations, (III) inter-district permanent relocations and casual sexual relationships, or (IV) permanent relocations between districts and to/from abroad and inter-district casual sex; and (V) a grid of 10 × 10km2 cells, with permanent relocations and between-cell casual relationships. We assumed HIV was present in 1975 in the districts with >10 % prevalence in 2010. We calibrated the models to national and district-level prevalence estimates.

Results

Reaching the national prevalence required all adults to have at least 22 casual sex acts/year until 1990. Models II, III and V reproduced the geographical heterogeneity in prevalence in 2010 to some extent if between-district relationships were excluded (Model II; 4.9 %-21.1 %). Long-distance casual partnership mixing mitigated the differences in prevalence substantially (range across districts 4.1%-18.9 % in 2010 in Model III; 4.0%-17.6 % in Model V); with international migration the differences disappeared (Model IV; range across districts 6.9%-13.3 % in 2010). National prevalence decreased to 5 % by 2030.

Conclusion

Earlier introduction of HIV into the Southern part of Malawi may cause some level of heterogeneity in HIV prevalence. Other factors such as sociobehavioural characteristics are likely to have a major impact and need investigation.

Spatial-temporal analysis of suicide clusters for suicide prevention in Hong Kong: a territory-wide study using 2014-2018 Hong Kong Coroner's Court reports

Background

This study aimed to (i) identify high-risk suicide-methods clusters, based on location of residence and suicide incidence; and (ii) compare the characteristics of cases and spatial units inside and outside clusters.

Methods

Suicide data of 4672 cases was obtained from the Coroner's Court reports in Hong Kong (2014-2018). Monthly aggregated suicide numbers based on location of residence, and suicide incidence, were obtained in small tertiary planning units (STPUs). Community-level characteristics and population of STPUs were retrieved from 2016 Census. Retrospective space-time analyses were performed to identify locations with elevated suicide rates over specific time periods, i.e., spatial-temporal clusters. Clusters were evaluated for overall suicide (any method), as well as jumping, hanging, and charcoal burning methods, in location of residence and suicide incidence. Bi-variate analysis was performed to compare the characteristics of cases, and spatial units, inside and outside the clusters.

Findings

Suicide clusters involving jumping and charcoal burning were identified, but no hanging clusters were found. The within-cluster distribution of types of housing was different from that of outside. For most of the overall suicide and suicide by jumping clusters, spatial units within the clusters were more socially disadvantaged compared to those outside.

Interpretation

Clusters varied by suicide methods, location of residence and location of incidence. The findings highlighted the need for consistent and concerted support from different stakeholders within suicide clusters, to ensure appropriate design, implementation and sustainability of effective suicide prevention programs.

Funding

General Research Fund (37000320) and seed fund from the University of Hong Kong (104006710).

Morphology of possible regions in elite soccer players

The popularity of spatio-temporal analyses in soccer is increasing. As many of these analyses depend on the regions a player can occupy in a certain amount of time (the possible regions), the understanding of this concepts is important for analyses to produce usable results. This study investigated how possible regions of soccer morph with varying times and running speeds. Twenty-four players from the Danish Superliga participated, and 13 players were analysed. The possible regions were analysed with times from 0.5 to 4 s (0.5 s increments) and initial velocities from 1 to 7 m/s (1 m/s increments). In this study, we showed that the possible regions can be described by ellipses (eccentricity of 0.5348 ± 0.1912). When comparing the possible region ellipses at every time and velocity pair, 1.95 % of the ellipses were not significantly different from the others. In conclusion, possible regions are unique in shape and size depending on player running speed and time available. However, as only few strikers participated, the results for this group should be interpreted with caution. Coaches can predict possible regions based on these parameters increasing precision of post-game analyses.

Spatio-temporal dynamic change mechanism analysis of traffic conflict risk based on trajectory data

Analyzing risk dynamic change mechanism under spatio-temporal effects can provide a better understanding of traffic risk, which helps reinforce the safety improvement. Traditionally, spatio-temporal studies based on crash data were mostly conducted to explore crash risk evolution mechanism from a macroscopic perspective. Dynamic change mechanism of short-term risk within a small-scale area deserves exploration, which cannot be captured in macroscopic crash-based studies. It is practical to analyze traffic conflict risk as a surrogate safety measure, which can preferably overcome the limitations of crash-based studies. This study aims to explore the spatio-temporal dynamic change mechanism of conflict risk based on trajectory data. Both conflict frequency and severity are integrated and assessed by applying fuzzy logic theory to develop the whole risk indicator. Trajectories on U.S. Highway101 from NGSIM dataset are utilized and aggregated. A two-step framework is proposed to analyze the risk dynamic change mechanism. The spatial Markov model is firstly applied to explore the transition probability of risk level, and then the panel regression approach is employed to quantify the relationship between spatio-temporal risk and traffic characteristics. Modeling results show that (1) the dynamic change trend of safety states differs under different spatial lag conditions, and it can be well depicted by the spatial Markov model; (2) dynamic spatial panel data modeling method performs better than the model that only considers temporal or spatial dependency. The novel proposed framework promotes a systematic exploration of conflict risk from a mesoscopic perspective, which contributes to assess the real-time road safety more comprehensively.

Evaluation and prediction of water conservation of the Yellow river basin in Sichuan Province, China, based on Google Earth Engine and CA-Markov

The Yellow River Basin in China has the world's most serious soil erosion problem. The Yellow River Basin in Sichuan Province (YRS), as the upper reaches of the Yellow River, and its water conservation (WC) capacity greatly affects the ecological environment of the downstream basin. In recent years, YRS has received more and more attention, and numerous policies have been developed to improve local WC. However, there is a vacancy in the long-term research of WC in the YRS due to the lack of in-situ data. This study quantitatively evaluated the WC of YRS from 2001 to 2020 through Google Earth Engine (GEE) and analyzed the spatio-temporal variations of WC and land cover (LC). CA-Markov predicted the LC and WC in 2025 under three scenarios to assess the contribution of different scenarios to WC. The WC in YRS fluctuated from 1.93 to 6.77 billion m3. The climate is the dominant factor of WC change, but the effect of LC on WC is also evident. The WC capacity increases with vegetation coverage and height. The WC capacity of forests per km2 exceeds 600 mm, while that of grasslands is about 250 mm, and barren can cause around 300 mm of WC loss. In 2025, the WC in YRS may exceed 7.5 billion m3, but the past ecological management mode should be transformed. Improving the quality of land use and converting grasslands to forests is better than reducing cropland to improve WC.

Are at-risk sociodemographic attributes stable across COVID-19 transmission waves?

COVID-19 health impacts and risks have been disproportionate across social, economic, and racial gradients (Chen et al., 2021; Thompson et al., 2021; Mamuji et al., 2021; COVID-19 and Ethnicity, 2020). By examining the first five waves of the pandemic in Ontario, we identify if Forward Sortation Area (FSAs)based measures of sociodemographic status and their relationship to COVID-19 cases are stable or vary by time. COVID-19 waves were defined using a time-series graph of COVID-19 case counts by epi-week. Percent Black visible minority, percent Southeast Asian visible minority and percent Chinese visible minority at the FSA level were then integrated into spatial error models with other established vulnerability characteristics. The models indicate that area-based sociodemographic patterns associated with COVID-19 infection change over time. If sociodemographic characteristics are identified as high risk (increased COVID-19 case rates) increased testing, public health messaging, and other preventative care may be implemented to protect populations from the inequitable burden of disease.

Spatio-temporal patterns in the history of colonial botanical exploration in India

A study of Indian botany during the colonial period provides us with an interdisciplinary sphere covering science, politics, sociology, economics, and other associated domains. The presentation of this rich legacy to general readers often restricts itself to the descriptive accounts of explorers with little analysis of the intermingling of socio-political dynamics, landscape, and geography. We attempt to revisit the colonial plant exploration history through spatio-temporal analyses to understand how explorers with divergent interests traversed the country in different periods and directions. Our findings show that the Himalayas drove most of the explorations followed by the Deccan peninsula, the Western Ghats, and the Gangetic plain. In general, company/crown administered areas were better surveyed and documented than native states. Furthermore, through a prioritization exercise, we identify the centers of prominence, highlighting their political, economic, and social importance in contemporary India. In sum, this article shows the potential of applying Historical Geographic Information System (HGIS) mapping for spearheading future research and creating space for possible dialogs among historians, political scientists, and ecologists.

Implementing Geospatial Science and Technology to Get to Zero New HIV Infections

PURPOSE OF REVIEW

Tremendous advancements have been made in HIV treatment and prevention during the last 40 years that zero new HIV cases has become an attainable goal declared by international agencies. However, new cases of HIV infection persist.

RECENT FINDINGS

The emerging field of geospatial science is positioned to play key role in the reduction of continued HIV incidence through technology-driven interventions and innovative research that gives insights into at-risk populations. As these methods become more utilized, findings consistently show the important role of location and environment plays in HIV incidence and treatment adherence. This includes distance to HIV provider, locations of where HIV transmissions occurs compared to where people with HIV reside, and how geospatial technology has been leveraged to identify unique insights among varying groups of those at increased risk for HIV, among others. Given these insights, leveraging geospatial technology would play a prominent role in achieving zero new cases of HIV infections.

What happens behind doors? Exploring everyday indoor activities when ageing in place

Engaging in social interaction and physical movement during everyday activities has a positive influence on wellbeing in later life. For older adults who age in place, the majority of activities occur indoors, yet studies typically focus on outdoors. Gender influences social and physical activities but is understudied in an ageing-in-place context. We aim to address these gaps by increasing insight into the indoor activities in later life, with a focus on gender differences in social interaction and physical movement. Through a mixed-methods approach, data were collected using global positioning system (GPS) trackers, pedometers and activity diaries. Twenty community-dwelling older adults (11 women and 9 men) who were living in Lancashire collected these data over seven days. An exploratory spatio-temporal analysis was conducted on the 820 activities they undertook. We discovered that our participants spend large amounts of time indoors. We also found that social interaction increases the duration of the activity and, conversely, decreases levels of physical movement. When zooming in to gender differences, men's activities took significantly longer than women's activities and were characterised by higher level of social interaction. Based on these results, we argue that there is a trade-off between social interaction and physical movement in everyday activities. We suggest establishing a balance between socialising and moving in everyday activities in later life, specifically because maintaining high levels of movement and social interaction at the same time seems unachievable. In conclusion, it is important to design indoor environments that facilitate choice between being active and resting, and between being social and being on one's own rather than assume they are mutually-exclusive and/or universally "good" or "bad" per se.

Projected rapid growth in diabetes disease burden and economic burden in China: a spatio-temporal study from 2020 to 2030

Background

This study projects the trend of disease burden and economic burden of diabetes in 33 Chinese provinces and nationally during 2020-2030 and investigates its spatial disparities.

Methods

Time series prediction on the prevalence and disability-adjusted life-year (DALY) rates of diabetes was conducted using a Bayesian modelling approach in 2020-2030. The top-down method and the human capital method were used to predict the direct and indirect costs of diabetes for each Chinese province. Global and local spatial autocorrelation analyses were used to identify geographic clusters of low-or high-burden areas.

Findings

Diabetes prevalence in Chinese adults aged 20-79 years was projected to increase from 8.2% to 9.7% during 2020-2030. During the same period, the total costs of diabetes would increase from $250.2 billion to $460.4 billion, corresponding to an annual growth rate of 6.32%. The total costs of diabetes as a percentage of GDP would increase from 1.58% to 1.69% in China during 2020-2030, suggesting a faster growth in the economic burden of diabetes than China's economic growth. Consistently, the per-capita economic burden of diabetes would increase from $231 to $414 in China during 2020-2030, with an annual growth rate of 6.02%. High disease and economic burden areas were aggregated in Northeast and/or North China.

Interpretation

Our study projects a significant growth of disease and economic burden of diabetes in China during 2020-2030, with strong spatial aggregation in northern Chinese regions. The increase in the economic burden of diabetes will exceed that of GDP.

Funding

National Natural Science Foundation of China, Outstanding Young Scholars Funding.

Impacts of land uses on spatio-temporal variations of seasonal water quality in a regulated river basin, Huai River, China

Land use impacts from agriculture, industrialization, and human population should be considered in surface water quality management. In this study, we utilized an integrated statistical analysis approach mainly including a seasonal Mann-Kendall test, clustering analysis, self-organizing map, Boruta algorithm, and positive matrix factorization to the assessment of the interactions between land use types and water quality in a typical catchment in the Huai River Basin, China, over seven years (2012-2019). Spatially, water quality was clustered into three groups: upstream, midstream, and downstream/mainstream areas. The water quality of upstream sites was better than of mid-, down-, and mainstream. Temporally, water quality did not change significantly during the study period. However, the temporal variation in water quality of up-, down-, and mainstream areas was more stable than in the midstream. The interactions between land use types and water quality parameters at the sub-basin scale varied with seasons. Increasing forest/grassland areas could substantially improve the water quality during the wet season, while nutrients such as phosphorus from cropland and developed land was a driver for water quality deterioration in the dry season. Water area was not a significant factor influencing the variations of ammonia nitrogen (NH₃-N) and total phosphorus (TP) in the wet or dry season, due to the intensive dams and sluices in study area. The parameters TP, and total nitrogen (TN) were principally linked with agricultural sources in the wet and dry seasons. The parameters NH₃-N in the dry season, and chemical oxygen demand (COD_Cr) in the wet season were mainly associated with point source discharges. Agricultural source, and urban point source discharges were the main causes of water quality deterioration in the study area. Collectively, these results highlighted the impacts of land use types on variations of water quality parameters in the regulated basin.

Trends in dental caries of deciduous teeth in Iran: a systematic analysis of the national and sub-national data from 1990 to 2017

BACKGROUND

Dental caries is the most prevalent child affliction in the world and can be reduced through effective preventive interventions. To plan cost-effective interventions, clear and integrated data are needed. This study has been designed to overcome the lack of national trend in deciduous dental caries in Iran.

OBJECTIVE

To estimate the dental caries trend in deciduous teeth in the Iranian population at different ages from 1990 to 2017.

METHODS

From 1990 to 2017 a literature search about dmf and its components (decayed, missed, and filled tooth, abbreviated as dt, mt, and ft) as well as dental caries was done in the Iranian population in three English (PubMed, Web of Science, and Scopus) and three national databases (in Persian). All eligible national oral health surveys in these 28 years were included. National dmft data were categorised based on age (1-4, 5-9, and 10-14), sex, province and year. The final trends were estimated using an age-spatio-temporal hierarchical model. We used the bootstrap method in multilevel models to predict the uncertainty interval (UI) of the modelled results. Finally, the estimations of dmft, dt, mt, and ft with a 95% UI were reported from 1990 to 2017.

RESULTS

Almost 22% of the Iranian deciduous teeth were involved with dental caries in 1990 [dmft = 4.37; (95% UI 2.23, 6.62)] which more than 83% of it was dt [3.64 (1.53, 5.88)] and less than 7% was ft [0.30 (0.06, 0.65)]. During 1990-2017, dmft increased by more than 15% [in 2017, dmft = 5.03 (2.82, 7.29)]. The highest increase was seen in dt which was more than 17% [in 2017, dt = 4.27 (1.96, 6.57)].

CONCLUSION

Increasing dental caries among Iranian children over 28 years shows that oral health policies in Iran need critical evaluation. We need cost-effective nationwide interventions (e.g., supervised tooth brushing and improving dietary habits) and training well-experienced intermediate manpower (e.g., dental hygienists) to reduce dental caries.

What have we learned about socioeconomic inequalities in the spread of COVID-19? A systematic review

This article aims to provide a better understanding of the associations between groups of socioeconomic variables and confirmed cases of COVID-19. The focus is on cross-continental differences of reported positive, negative, unclear, or no associations. A systematic review of the literature is conducted on the Web of Science and SCOPUS databases. Our search identifies 314 eligible studies published on or before 31 December 2021. We detect nine groups of frequently used socioeconomic variables and results are presented by region of the world (Africa, Asia, Europe, Middle East, North American and South America). The review expands to describe the most used statistical and modelling techniques as well as inclusion of additional dimensions such as demographic, healthcare weather and mobility. Meanwhile findings agree on the generalized positive impact of population density, per capita GDP and urban areas on transmission of infections, contradictory results have been found concerning to educational level and income.

The spatio-temporal dynamics of infant mortality in Ecuador from 2010 to 2019

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

Non-Gaussian multivariate statistical monitoring of spatio-temporal wind speed frequencies to improve wind power quality in South Korea

The spatial and temporal variability of renewable energy resources, particularly wind energy, should be statistically evaluated to achieve sustainable economic development to mitigate climate change. In this study, a non-Gaussian multivariate statistical monitoring approach is proposed to investigate the wind speed frequencies across different regions of South Korea. Anemometer data were first collected in 11 different provinces of South Korea with hourly resolution for one year. The best-of-fit for the corresponding distribution function was identified to characterize the behavior of the wind speed frequency at each region among more than 60 candidate functions using the chi-squared test. Furthermore, a non-Gaussian multivariate statistical monitoring method based on the Hotelling T² chart was developed to spatially and temporally analyze the physical patterns of the wind speed frequencies using the estimated distribution parameters. Then determination rule of cut-in and cut-out speeds of wind turbine was suggested to improve the wind power quality across the regions. The results indicated that Weibull and Gamma distributions are best-of-fit functions of each province in South Korea; the physical patterns of wind including the average wind speed and gale can be identified by distribution parameters. Furthermore, the proposed non-Gaussian multivariate monitoring approach can elucidate the spatial and temporal variability of the regional wind speed frequencies, including the average wind speeds and extreme wind events across South Korea. Based on the statistically identified variability of wind behavior, the wind power quality of wind turbines can be improved by 12% on average by adjusting the cut-in and cut-off speed. Thus, the proposed non-Gaussian multivariate monitoring approach can provide practical guidelines for manufacturers to achieve reliable wind energy generation by considering the spatial and temporal wind behavior.

Homicide rates are spatially associated with built environment and socio-economic factors: a study in the neighbourhoods of Toronto, Canada

Objectives

Homicide rate is associated with a large variety of factors and therefore unevenly distributed over time and space. This study aims to explore homicide patterns and their spatial associations with different socioeconomic and built-environment conditions in 140 neighbourhoods of the city of Toronto, Canada.

Methods

A homicide dataset covering the years 2012 to 2021 and neighbourhood-based indicators were analysed using spatial techniques such as Kernel Density Estimation, Global/Local Moran’s I and Kulldorff’s SatScan spatio-temporal methodology. Geographically weighted regression (GWR) and multi-scale GWR (MGWR) were used to analyse the spatially varying correlations between the homicide rate and independent variables. The latter was particularly suitable for manifested spatial variations between explanatory variables and the homicide rate and it also identified spatial non-stationarities in this connection.

Results

The adjusted R² of the MGWR was 0.53, representing a 4.35 and 3.74% increase from that in the linear regression and GWR models, respectively. Spatial and spatio-temporal high-risk areas were found to be significantly clustered in downtown and the north-western parts of the city. Some variables (e.g., the population density, material deprivation, the density of commercial establishments and the density of large buildings) were significantly associated with the homicide rate in different spatial ways.

Conclusion

The findings of this study showed that homicide rates were clustered over time and space in certain areas of the city. Socioeconomic and the built environment characteristics of some neighbourhoods were found to be associated with high homicide rates but these factors were different for each neighbourhood.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13807-4.

Understanding the transmission dynamics of a large-scale measles outbreak in Southern Vietnam

OBJECTIVES

During 2018-2020, Southern Vietnam experienced a large measles outbreak of over 26,000 cases. We aimed to understand and quantify the measles spread in space-time dependence and the transmissibility during the outbreak.

METHODS

Measles surveillance reported cases between 1/2018 and 6/2020, vaccination coverage, and population data at provincial level were used. To illustrate the spatiotemporal pattern of disease spread, we employed the endemic-epidemic multivariate time series model decomposing measles risk additively into autoregressive, spatiotemporal, and endemic component. Likelihood-based estimation procedures were performed to determine the time-varying reproductive number R_e of measles.

RESULTS

Our analysis shows that measles incidence was associated with vaccination coverage heterogeneity and spatial interaction between provincial units. The risk of infections was dominated by between-province transmission (36.1% to 78.8%), followed by local endogenous transmission (4.1% to 61.5%) whereas the endemic behavior had a relatively small contribution (2.1% to 33.4%) across provinces. In the exponential phase of the epidemic, R_e was above the threshold with a maximum value of 2.34 (95%CI: 2.20-2.46).

CONCLUSION

Local vaccination coverage and human mobility are important factors contributing to the measles dynamics in Southern Vietnam and the high risk of inter-provincial transmission is of most concern. Strengthening disease surveillance is recommended, and further research is essential to understand the relative contribution of population immunity and control measures in measles epidemics.

Rapid surveillance of COVID-19 by timely detection of geographically robust, alive and emerging hotspots using Particle Swarm Optimizer

A novel virus, called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly become a pandemic called Coronavirus disease 2019 (COVID-19). According to the World Health Organization, COVID-19 was first detected in Wuhan city in December 2019 and has affected 216 countries with 9473214 confirmed cases and 484249 deaths globally as on June 26th, 2020. Also, this outbreak continues to grow in many countries like the United States of America (U.S.), Brazil, India, and Russia. To ensure rapid surveillance and better decision-making by government authorities in different countries, it is vital to identify alive and emerging hotspots within a country promptly. State-of-the-art methods based on space-time scan statistics (like SaTScan) are not geographically robust. Also, due to the enumeration of many Spatio-temporal cylinders, the computation cost of Spatio-temporal SaTScan (ST-SaTScan) is very high. In the applications like COVID-19 where we need to detect the emerging hotspots daily as soon as the new count of cases gets updated, ST-SaTScan seems inefficient. Therefore, this paper proposes a Particle Swarm Optimizer-based scheme to timely detect geographically robust, alive, and emerging COVID-19 hotspots in a country. Timely detection can help government officials design better control strategies like increasing testing in hotspots, imposing stricter containment rules, or setting up temporary hospital beds. Performance of ST-SaTScan and proposed scheme have been analyzed for four worst-hit U.S. states for the incubation period of 14 days between June 11th, 2020, and June 24th, 2020. Results indicate that the proposed scheme detects hotspots of a higher likelihood ratio (a measure to indicate the significance of hotspot) than ST-SaTScan in significantly less time. We also applied the proposed scheme to detect the emerging COVID-19 hotspots in all states of the U.S. During the study period, the proposed scheme has detected 104 emerging COVID-19 hotspots.

Impact of monsoon teleconnections on regional rainfall and vegetation dynamics in Haryana, India

Our study has investigated the impact of El Niño-Southern Oscillation (ENSO) on spatio-temporal dynamics of Indian summer monsoon rainfall (ISMR) as well as vegetation for a period of 1980 to 2019 at regional scale in Haryana, India. The gridded rainfall datasets of India Meteorological Department (IMD) were examined on monthly and seasonal scale using various statistical methods like mean climatology, coefficient of variation, slope of linear, Sen's slope, Mann-Kendall Z statistic, and hierarchical cluster analysis. The influence of ENSO on spatial distribution of ISMR was observed, where we found increasing and decreasing rainfall patterns during La Niña and El Niño years, respectively. We attempted to establish a link between ISMR and various teleconnections using time series of the National Oceanic and Atmospheric Administration (NOAA) Physical Sciences Laboratory, and statistically significant and positive correlation was observed with the Southern Oscillation Index (SOI), whereas significantly negative correlations were observed with SST of Niño 3, Niño 3.4, and Niño 4 regions. The gridded datasets of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis V5 (ERA5) were used to evaluate the influence of ENSO on atmospheric dynamics using lower and upper tropospheric wind circulation (850 hPa and200 hpa), vertically integrated moisture transport (VIMT), and surface moisture flux (SMF). We have used satellite-based normalised difference vegetation index (NDVI) datasets of the Global Inventory Monitoring and Modeling System (GIMMS) to investigate the impact of ENSO on vegetation dynamics of Haryana and found that NDVI values were higher and lower in case of La Niña and El Niño years, respectively.

Clustering spatio-temporal series of confirmed COVID-19 deaths in Europe

The impact of the COVID-19 pandemic varied significantly across different countries, with important consequences in the definition of control and response strategies. In this work, to investigate the heterogeneity of this crisis, we analyse the spatial patterns of deaths attributed to COVID-19 in several European countries. To this end, we propose a Bayesian nonparametric approach, based on mixture of Gaussian processes coupled with Dirichlet process, to group the COVID-19 mortality curves. The model provides a flexible framework for the analysis of time series data, allowing the inclusion in the clustering procedure of different features of the series, such as spatial correlations, time varying parameters and measurement errors. We evaluate the proposed methodology on the death counts recorded at NUTS-2 regional level for several European countries in the period from March 2020 to February 2021.

Spatio-temporal convolutional residual network for regional commercial vitality prediction

The vitality of commercial entities reflects the business condition of their surrounding area, the prediction of which helps identify the trend of regional development and make investment decisions. The indicators of business conditions, like revenues and profits, can be employed to make a prediction beyond any doubt. Unfortunately, such figures constitute business secrets and are usually publicly unavailable. Thanks to the rapid growing of location based social networks such as Yelp and Foursquare, massive amount of online data has become available for predicting the vitality of commercial entities. In this paper, a Spatio-Temporal Convolutional Residual Neural Network (STCRNN) is proposed for regional commercial vitality prediction, based on public online data, such as reviews and check-ins from mobile apps. Firstly, a commercial vitality map is built to indicate the popularity of business entities. Afterwards, a local convolutional neural network is employed to capture the spatial relationship of surrounding commercial districts on the vitality map. Then, a 3-dimension convolution is applied to deal with both recent and periodic variations, i.e., the sequential and seasonal changes of commercial vitality. Finally, long short-term memory is introduced to synthesize these two variations. In particular, a residual network is used to eliminate gradient vanishing and exploding, caused by the increase of depth of neural networks. Experiments on public Yelp datasets from 2013 to 2018 demonstrate that STCRNN outperforms the current methods in terms of mean square error.

Geographical inequalities in the decreasing 28-day mortality following incident acute myocardial infarction: a Danish register-based cohort study, 1987-2016

Background

Mortality following acute myocardial infarction (AMI) has decreased in western countries for decades; however, it remains unknown whether the decrease is distributed equally across the population independently of residential location. This study investigated whether the observed decreasing 28-day mortality following an incident AMI in Denmark from 1987 to 2016 varied geographically at municipality level after accounting for sociodemographic characteristics.

Methods

A register-based cohort study design was used to investigate 28-day mortality among individuals with an incident AMI. Global spatial autocorrelation (within sub-periods) was analysed at municipality level using Moran's I. Analysis of spatio-temporal autocorrelation before and after adjusting for sociodemographic characteristics was performed using logistic regression and conditional autoregressive models with inference in a Bayesian setting.

Results

In total, 368,839 individuals with incident AMI were registered between 1987 and 2016 in Denmark; 128,957 incident AMIs were fatal. The 28-day mortality decreased over time at national level with an odds ratio of 0.788 (95% credible interval (0.784, 0.792)) per 5-year period after adjusting for sociodemographic characteristics. The decrease in the 28-day mortality was geographically unequally distributed across the country and in a geographical region in northern Jutland, the 28-day mortality decreased significantly slower (4–12%) than at national level.

Conclusions

During the period from 1987 to 2016, the 28-day mortality following an incident AMI decreased substantially in Denmark. However, in a local geographical region, the 28-day mortality decreased significantly slower than in the rest of the country both before and after adjusting for sociodemographic differences. Efforts should be made to keep geographical trend inequalities in the 28-day mortality to a minimum.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-022-02519-7.

Stroke Hospital Admissions during the COVID-19 Outbreak in São Paulo, Brazil

Background

Globally, stroke remains an important cause of death and long-term disability, and the impact of coronavirus disease (COVID-19) on the health system may have impaired stroke care. Previous studies suggest significant reduction in hospital admissions for stroke after COVID-19 onset as patients may hesitate seeking medical help due to fear of exposure.

Methods

This cross-sectional study included cases of hospital admissions for stroke, identified from the Hospital Information System of the Unified Health System (Sistema Único de Saúde), which contains official and public data in Brazil. Data were collected in duplicate, then categorized according to the International Classification of Diseases, tenth revision (ICD-10), considering codes I60–I69. Linear regression was used to estimate the variation in hospital admissions for stroke in the city of São Paulo (SP) − the largest and most populous city in Brazil and Latin America, between January and June of each analyzed year (2017–2020). The percentage variation between June and January 2020 was also compared. The level of significance was set at 5%, and the statistical program used was Stata, version 14.0.

Results

In the city of SP, during the first wave of COVID-19, from January to June 2020, there were registered decreases in absolute numbers and mean monthly admissions for stroke. Compared to January 2020, data from June 2020 showed 17% reduction in hospitalizations for intracerebral hemorrhage, 32% for cerebral infarction, 26% for stroke unspecified, and 47% for other cerebrovascular diseases.

Conclusion

We argue for policies aimed at improving stroke care and developing awareness campaigns regarding the importance of early diagnosis and treatment, as even in less severe presentations, stroke can trigger an increase in mortality, cost, and long-term disability.

Spatio-temporal modeling of COVID-19 prevalence and mortality using artificial neural network algorithms

The outbreak of coronavirus disease (COVID-19) has become one of the most challenging global concerns in recent years. Due to inadequate worldwide studies on spatio-temporal modeling of COVID-19, this research aims to examine the relative significance of potential explanatory variables (n = 75) concerning COVID-19 prevalence and mortality using multilayer perceptron artificial neural network topology. We utilized ten variable importance analysis methods to identify the relative importance of the explanatory variables. The main findings indicated that several variables were persistently among the most influential variables in all periods. Regarding COVID-19 prevalence, unemployment and population density were among the most influential variables with the highest importance scores. While for COVID-19 mortality, health-related variables such as diabetes prevalence and number of hospital beds were among the most significant variables. The obtained findings from this study might provide general insights for public health policymakers to monitor the spread of disease and support decision-making.

Spatio-temporal distribution of reactive sulfur species during methylmercury exposure in the rat brain

Brain susceptibility to methylmercury (MeHg) is developmentally and regionally specific in both humans and rodents, but the mechanism is not well clarified. Reactive sulfur species (RSS) with high nucleophilicity can react with MeHg, leading to the formation of a less toxic metabolite bismethylmercury sulfide, thus exerting cytoprotection. In this study, we assessed the variation of RSS content in the rat brain and evaluated its relevance in sensitivity to MeHg. Analyses of fetal/juvenile rat brains showed low RSS levels in early developmental stages. Site-specific analysis of adult rat brains revealed that cerebellar RSS levels were lower than those of the hippocampus. Microscopically, RSS levels of the granular cell layer were lower than those of the molecular layer in the cerebellum. Thus, low RSS levels corresponded with age and site of the brain that is vulnerable to MeHg. Taken together with the finding that brain RSS were consumed during MeHg exposure, these results indicate that RSS is a factor that defines the specificity of MeHg vulnerability in the brain.

Impact of environmental factors on the spread of dengue fever in Sri Lanka

Dengue fever is a mosquito-borne viral disease caused by the dengue virus of the Flaviviridae family and is responsible for colossal health and economic burden worldwide. This study aimed to investigate the effect of environmental, seasonal, and spatial variations on the spread of dengue fever in Sri Lanka. The study used secondary data of monthly dengue infection and the monthly average of environmental parameters of 26 Sri Lankan regions from January 2015 to December 2019. Besides the descriptive measurements, Kendall's tau_b, Spearman's rho, and Kruskal-Wallis H test have been performed as bivariate analyses. The multivariate generalized linear negative binomial regression model was applied to determine the impacts of meteorological factors on dengue transmission. The aggregate negative binomial regression model disclosed that precipitation (odds ratio: 0.97, p < 0.05), humidity (odds ratio: 1.05, p < 0.01), and air pressure (odds ratio: 1.46, p < 0.01) were significantly influenced the spread of dengue fever in Sri Lanka. The bioclimatic zone is the vital factor that substantially affects the dengue infection, and the wet zone (odds ratio: 6.41, p < 0.05) was more at-risk than the dry zone. The climate season significantly influenced dengue fever transmission, and a higher infection rate was found (odds ratio: 1.46, p < 0.01) in the northeast monsoon season. The findings of this study facilitate policymakers to improve the existing dengue control strategies focusing on the meteorological condition in the local as well as global perspectives.

Spatio-temporal analysis of groundwater chemistry, quality and potential human health risks in the Pinggu basin of North China Plain: Evidence from high-resolution monitoring dataset of 2015-2017

Long-term monitoring reveals the spatio-temporal evolution of groundwater chemistry, quality and human health risk, providing detailed and robust evidence for groundwater utilization. The Pinggu basin of North China Plain is significant place reserving drinking groundwater. 184 samples were collected from fifty-eight monitoring wells during 2015-2017. Ratios of major ions and geochemical modelling were carried out to clarify the factors controlling the hydrogeochemical compositions. Groundwater displayed the hydrochemical type of Ca-HCO₃ and its compositions were determined by calcite and dolomite dissolution with cation exchange reaction. NO₃^- contamination was derived from agriculture activities. The entropy-weighted water quality index (EWQI) results indicated the majority of total groundwater samples except those in some southwestern and northwestern parts were able to meet the requirement of drinking purposes. Groundwater quality was affected by the total dissolved solid, Ca²⁺, HCO₃^- and NO₃^- concentrations. Human health risk of groundwater drinking depended on the NO₃^- concentration and followed the order of children > adult females ≈ adult males, according to the hazard quotient (HQ) used in the human health risk assessment (HHRA) model. Protection and management measures for groundwater resources were made for the Pinggu basin and other similar areas, based on the spatio-temporal analysis of groundwater chemistry, quality and potential human health risks.

A Systematic Review of Spatial and Spatio-temporal Analyses in Public Health Research in Korea

OBJECTIVES

Despite its advantages, it is not yet common practice in Korea for researchers to investigate disease associations using spatio-temporal analyses. In this study, we aimed to review health-related epidemiological research using spatio-temporal analyses and to observe methodological trends.

METHODS

Health-related studies that applied spatial or spatio-temporal methods were identified using 2 international databases (PubMed and Embase) and 4 Korean academic databases (KoreaMed, NDSL, DBpia, and RISS). Two reviewers extracted data to review the included studies. A search for relevant keywords yielded 5919 studies.

RESULTS

Of the studies that were initially found, 150 were ultimately included based on the eligibility criteria. In terms of the research topic, 5 categories with 11 subcategories were identified: chronic diseases (n=31, 20.7%), infectious diseases (n=27, 18.0%), health-related topics (including service utilization, equity, and behavior) (n=47, 31.3%), mental health (n=15, 10.0%), and cancer (n=7, 4.7%). Compared to the period between 2000 and 2010, more studies published between 2011 and 2020 were found to use 2 or more spatial analysis techniques (35.6% of included studies), and the number of studies on mapping increased 6-fold.

CONCLUSIONS

Further spatio-temporal analysis-related studies with point data are needed to provide insights and evidence to support policy decision-making for the prevention and control of infectious and chronic diseases using advances in spatial techniques.

Spatio-temporal analysis of bacillary dysentery in Sichuan province, China, 2011-2019

Background

Bacillary dysentery (BD) is a common infectious disease in China and causes enormous economic burdens. The purpose of this study was to describe the epidemiological characteristics of BD and to identify its possible hot spots and potentially high-risk areas in Sichuan province of China.

Methods

In this study, we collected monthly BD incidence reports of 181 counties in Sichuan province, China, from January 2011 to December 2019. Descriptive statistics were used to evaluate the epidemic characteristics of BD. Moran’s I index was applied to investigate the yearly patterns of the spatial distribution. And spatio-temporal scanning statistics with the spatial unit set as county and the temporal unit set as month were used to investigate the possible high-risk region. Meanwhile, the circular moving windows were also employed in the spatio-temporal scanning to scan the study areas.

Results

The annual incidence of BD ranged between 16.13/100,000 and 6.17/100,000 person-years from 2011 to 2019 in Sichuan. The majority of the cases were children aged 5 years or younger. For the descriptive statistics, a peak from May to October was observed in temporal analysis, the epidemics were mainly concentrated in the northwest and southwest of Sichuan in spatial analysis. After 2016, the scope of BD significantly narrowed and severe epidemic areas were relatively stable. For the spatial autocorrelation analysis, a high global autocorrelation was observed at the county level, and the high–high clusters mainly distributed in the northwest and southwest of Sichuan. For the spatio-temporal scanning, the spatiotemporal clusters of BD occurred every year from 2011 to 2019. The most likely cluster areas mainly distributed in the southwest and northwest of Sichuan at the beginning, and then gradually concentrated in the southwest. The secondary cluster mainly concentrated in the northwest and its surrounding areas. Moreover, the 2nd secondary cluster was relatively small and mainly distributed in the central area. No clusters were noted in eastern Sichuan.

Conclusions

Based on our current analysis, BD is still a common challenge in Sichuan, especially for counties in the southwest and northwest in summer and autumn. More disease prevention and control measures should be taken in such higher-risk susceptible areas at a certain time to allocate the public health resources rationally, and finally reduce the spread of BD.

Human visceral leishmaniasis in Central-Western Brazil: Spatial patterns and its correlation with socioeconomic aspects, environmental indices and canine infection

In this ecological study, we investigated spatial patterns of human visceral leishmaniasis (VL) incidence, its correlation with socioeconomic aspects, environmental indices (obtained through remote sensing) and canine VL during 2011-2016 in the municipality of Rondonópolis, a relevant endemic area for VL in Central-Western Brazil. Human VL cases were georeferenced and point patterns were analyzed by univariate Ripley's K function and Kernel density estimation (KDE). Poisson-based scan statistics were used to investigate spatial and spatiotemporal clusters of human VL incidence at the neighborhood level. Socioeconomic and environmental characteristics were compared between neighborhoods within and outside spatial human VL clusters. Also, we assessed the correlation between smoothed human VL incidence and canine VL seropositivity rates within and between neighborhoods. Human VL cases were clustered up to 2000 m; four hotspots were identified by KDE in peripheral areas. Spatial and spatiotemporal low-risk clusters for human VL were identified in central and southern areas. Neighborhoods within spatial low-risk cluster presented higher mean income, literacy rate, sanitary sewage service coverage and lower altitude, compared to the rest of the municipality. A positive correlation was found between the occurrence of human and canine VL. On the northern outskirts, high human VL incidence was spatially correlated with high canine VL seropositivity in surrounding neighborhoods. In conclusion, human VL demonstrated a heterogeneous, aggregated and peripheral spatial pattern. This distribution was correlated with intra-urban socioeconomic differences and canine VL seropositivity at the neighborhood level.

Spatio-temporal patterns of the COVID-19 pandemic, and place-based influential factors at the neighborhood scale in Tehran

Since its emergence in late 2019, the COVID-19 pandemic has attracted the attention of researchers in various fields, including urban planning and design. However, the spreading patterns of the disease in cities are still not clear. Historically, preventing and controlling pandemics in cities has always been challenging due to various factors such as higher population density, higher mobility of people, and higher contact frequency. To shed more light on the spread patterns of the pandemic, in this study we analyze 43,000 confirmed COVID-19 cases at the neighborhood level in Tehran, the capital of Iran. To examine spatio-temporal patterns and place-based factors contributing to the spread of the pandemic, we used exploratory spatial data analysis and spatial regression. We developed a geo-referenced database composed of 12 quantitative place-based variables related to physical attributes, land use and public transportation facilities, and demographic status. We also used the geographically weighted regression model for the local examination of spatial non-stationarity. According to the results, population density (R² = 0.88) and distribution of neighborhood centers (R² = 0.59), drugstores (R² = 0.64), and chain stores (R² = 0.59) are the main factors contributing to the spread of the disease. Additionally, density of public transportation facilities showed a varying degree of contribution. Overall, our findings suggest that demographic composition and major neighborhood-level physical attributes are important factors explaining high rates of infection and mortality. Results contribute to gaining a better understanding of the role of place-based attributes that may contribute to the spread of the pandemic and can inform actions aimed at achieving Sustainable Development Goals, particularly Goals 3 and 11.

Spatio-temporal characteristics and control strategies in the early period of COVID-19 spread: a case study of the mainland China

COVID-19 has caused huge impacts on human health and the economic operation of the world. Analyzing and summarizing the early propagation law can help reduce the losses caused by public health emergencies in the future. Early data on the spread of COVID-19 in 30 provinces (autonomous regions and municipalities) of mainland China except for Hubei, Hong Kong, Macao, and Taiwan were selected in this study. Spatio-temporal analysis, inflection point analysis, and correlation analysis are used to explore the spatio-temporal characteristics in the early COVID-19 spread. The results suggested that (1) the total confirmed cases have risen in an "S"-shaped curve over time, and the daily new cases have first increased and finally decreased; (2) the spatial distributions of both total and daily new cases show a trend of more in the east and less in the west, with a "multi-center agglomeration distribution" around Hubei Province and some major cities; (3) the spatial agglomeration of total confirmed cases has been increasing over time, while that of the daily new cases shows much more obvious in the mid-stage; and (4) timely release of the first-level public health emergency response can accelerate the emergence of the epidemic inflection point. The above analysis results have a specific reference value for the government's policy-making and measures to face public health emergencies.

[Space-time spread of COVID-19 deaths in ArgentinaDistribuição espaço-temporal de mortes por COVID-19 na Argentina]

OBJECTIVES

Describe the space-time spread of COVID-19 deaths and analyze its socio-spatial inequalities in Argentina.

METHODS

COVID-19 deaths in Argentina as of October 17, 2020 were analyzed using data onday, month, and year, and place of residence. The space-time permutation scan method was used to detect the presence of space-time clusters. Poverty levels, population densities, and percentage of older adults in the population were compared for areas in high-mortality clusters and low-mortality clusters.

RESULTS

Five high-mortality clusters were detected between March 21 and August 27 in the Greater Buenos Aires conurbation and the northeast of the province of Buenos Aires. Low-mortality clusters were located on the periphery of the urban area from mid-September to mid-October and in central and northwestern Argentina between late April and late August. High-mortality clusters were located in areas with higher population densities and higher percentages of older adults in population, comparedto low-mortality clusters.

CONCLUSIONS

No high-mortality clusters were detected between September and mid-October. Norhave we detected a spatial spread of deaths to areas of low socioeconomic status at the national level. Our results support the first phase of the mortality spread model, affecting the largest urban area in Argentina.

Stormwater management impacts of small urbanising towns: The necessity of investigating the 'devil in the detail'

In many parts of the world, small towns are experiencing high levels of population growth and development. However, there is little understanding of how urban growth in these regional towns will impact urban runoff. We used the case study of Wangaratta, located in South-East Australia, between 2006 and 2016, to investigate land cover changes and their impacts on urban runoff discharge. Detailed spatio-temporal analysis (including neighbourhood composition analysis and supervised classification of aerial imagery) identified that population, land use and land cover changes in Wangaratta, although subtle, were mostly driven by residential growth in the outskirts of the town, where there were large increases in impervious surface area. Overall, the urban growth was minimal. However, in spite of these small changes, a sub-catchment only SWMM model showed that the increase in impervious surface area nevertheless resulted in a statistically significant increase in total runoff across the town. Particularly, this increase was most pronounced for frequent and shorter storms. The analysis of urban development pattern changes coupled with urban hydrological modelling indicated that land cover changes in regional towns, especially when analysed in detail, may result in hydrological changes in the urban region (likely to be exacerbated in coming years by changing climate) and that adaptation efforts will need to adopt a variety of approaches in both existing and growth zones. Our findings highlight the necessity of detailed fine-scale analyses in small towns as even subtle changes will have substantial future implications and robust planning and adaptation decisions are even more important when compared to larger cities due to the greater economic constraints that small towns face and their important relationship with the surrounding hinterlands.

High cadmium pollution from sediments in a eutrophic lake caused by dissolved organic matter complexation and reduction of manganese oxide

Eutrophication and metal pollution are global environmental problems. The risk of metal pollution is high in the eutrophic lakes because of high mobility of metal in sediments. However, the mechanism of cadmium (Cd) mobility in sediments is still unclear. Here we study the mobilization of Cd in sediments from the eutrophic Lake Taihu via monthly field monitoring of mobile Cd using diffusive gradient in thin films (DGT) and high resolution dialysis (HR-Peeper) techniques. We found a high mobility of Cd in sediments in February and March, resulting from reductive dissolution of Mn oxide mediation by high microbial activities, as shown by the similarities in distribution patterns of DGT-labile Cd and Mn. A two orders of magnitude increase in dissolved Cd concentrations (about 28 μg L^-1) was observed in May and June, with dissolved Cd concentrations in overlying water about 110 times higher than the criteria continuous concentration set by Environmental Protection Agency. Hourly changes were found to coincide and correlate between dissolved Cd and dissolved organic matter (DOM) under simulated anaerobic conditions, strongly suggesting that the sudden outbreak of Cd pollution observed in the field resulted from the complexation of DOM with Cd in sediments. This was further supported by the NICA-Donnan model that more than 71% of dissolved Cd in the pore water in May and June was present as Cd-DOM complexes. Three components of DOM including humic-, tryptophan-, and tyrosine-like components in the sediments in June was identified using the fluorescence excitation emission matrix-parallel factor analysis. We found that Cd was stable complexed with tyrosine-like component. The Fourier transform infrared and two-dimensional correlation spectroscopy further revealed that Cd was bound to phenolic OH, alkene CC, alcoholic CO, aromatic CH, and alkene CH groups. Our study effectively promotes the understanding of Cd mobilization in sediments and highlights the risk of sudden Cd pollution events in the eutrophic lakes.

Knowledge representation and learning of operator clinical workflow from full-length routine fetal ultrasound scan videos

Ultrasound is a widely used imaging modality, yet it is well-known that scanning can be highly operator-dependent and difficult to perform, which limits its wider use in clinical practice. The literature on understanding what makes clinical sonography hard to learn and how sonography varies in the field is sparse, restricted to small-scale studies on the effectiveness of ultrasound training schemes, the role of ultrasound simulation in training, and the effect of introducing scanning guidelines and standards on diagnostic image quality. The Big Data era, and the recent and rapid emergence of machine learning as a more mainstream large-scale data analysis technique, presents a fresh opportunity to study sonography in the field at scale for the first time. Large-scale analysis of video recordings of full-length routine fetal ultrasound scans offers the potential to characterise differences between the scanning proficiency of experts and trainees that would be tedious and time-consuming to do manually due to the vast amounts of data. Such research would be informative to better understand operator clinical workflow when conducting ultrasound scans to support skills training, optimise scan times, and inform building better user-machine interfaces. This paper is to our knowledge the first to address sonography data science, which we consider in the context of second-trimester fetal sonography screening. Specifically, we present a fully-automatic framework to analyse operator clinical workflow solely from full-length routine second-trimester fetal ultrasound scan videos. An ultrasound video dataset containing more than 200 hours of scan recordings was generated for this study. We developed an original deep learning method to temporally segment the ultrasound video into semantically meaningful segments (the video description). The resulting semantic annotation was then used to depict operator clinical workflow (the knowledge representation). Machine learning was applied to the knowledge representation to characterise operator skills and assess operator variability. For video description, our best-performing deep spatio-temporal network shows favourable results in cross-validation (accuracy: 91.7%), statistical analysis (correlation: 0.98, p < 0.05) and retrospective manual validation (accuracy: 76.4%). For knowledge representation of operator clinical workflow, a three-level abstraction scheme consisting of a Subject-specific Timeline Model (STM), Summary of Timeline Features (STF), and an Operator Graph Model (OGM), was introduced that led to a significant decrease in dimensionality and computational complexity compared to raw video data. The workflow representations were learnt to discriminate between operator skills, where a proposed convolutional neural network-based model showed most promising performance (cross-validation accuracy: 98.5%, accuracy on unseen operators: 76.9%). These were further used to derive operator-specific scanning signatures and operator variability in terms of type, order and time distribution of constituent tasks.

[Spatial-temporal analysis on imported dengue fever in six provinces of China, 2016-2018]

Objective: To explore the spatio-temporal patterns and epidemic characteristics of imported dengue fever cases in six provinces (Yunnan, Guangxi, Guangdong, Hainan, Fujian and Zhejiang) of China from 2016 to 2018. Methods: In this study, we collected the surveillance data of imported dengue fever cases from 2016 to 2018 in six southern provinces of China. The risk intensity, spatio-temporal distribution and epidemiological characteristics of imported dengue fever cases in the six provinces were analyzed from the perspective of space, time and population. Results: Among the imported cases of dengue fever in China from other countries in the world, most of them were from Southeast Asia. In Zhejiang, Fujian and Guangdong provinces, there were greater number of imported cases with wide range of sources. While in Yunnan, Guangxi and Hainan provinces, the imported cases were almost from Southeast Asia. The incidence of imported dengue fever increased during the past three years, and the annual incidence peak was during August-November, but slight differences were observed among provinces. The age of the imported cases in Hainan[(37.0±19.6) years] was higher than that in southeastern coastal provinces[(36.0±11.7) years] and in southwestern provinces[(32.0±16.3) years]. The male-female ratio of the cases in southeastern coastal provinces (2.18∶1) were higher than those in southwestern provinces (1.04∶1) and Hainan (1.38∶1). The occupations of the cases were mainly workers and unemployed people in southeastern coastal provinces and farmers in southwestern provinces, but commercial personnel was the population with high incidence in both southeastern and southwestern provinces. Less imported dengue fever cases were reported in Hainan, the population distribution varied. Conclusions: There were differences in incidence pattern of imported dengue fever between southeastern coastal provinces and southwestern provinces as well as Hainan in China. Different prevention and control measures should be carried out in different areas to implement more precise prevention strategies.

Influence of Multiple Sclerosis on Spatiotemporal Gait Parameters: A Systematic Review and Meta-Regression

OBJECTIVE

To quantify the effect of multiple sclerosis (MS) on spatiotemporal gait characteristics accounting for disability severity and fall classification.

DATA SOURCES

MEDLINE (1946-August 2018), Allied and Complementary Medicine Database (1985-2018 August), and PsycINFO (1806-August 2018) were searched for terms on MS and gait.

STUDY SELECTION

Dual independent screening was conducted to identify observational, cross-sectional studies that compared adults with MS grouped according to Expanded Disability Status Scale (EDSS) level or fall history, reported on spatiotemporal gait characteristics, and were published in English. The search retrieved 5891 results, of which 12 studies satisfied the inclusion criteria.

DATA EXTRACTION

Two authors worked independently to extract and verify data on publication details, study methodology, participant characteristics, gait outcomes, conclusions, and limitations. Risk of bias was assessed using the QualSyst critical appraisal tool. A random-effects meta-regression and meta-analysis were conducted on pooled data.

DATA SYNTHESIS

All studies received quality ratings of very good to excellent and collectively examined 1513 individuals with MS. With every 1-point increase in EDSS, significant changes (P<.05) were observed in gait speed (-0.12 m/s; 95% confidence interval (CI), 0.08-0.15), step length (-0.04 m; 95% CI, 0.03-0.05), step time (+0.04 seconds; 95% CI, 0.02-0.06), step time variability (+0.009 seconds; 95% CI, 0.003-0.016), stride time (+0.08 seconds; 95% CI, 0.03-0.12), cadence (-4.4 steps per minute; 95% CI, 2.3-6.4), stance phase duration (+0.8% gait cycle; 95% CI, 0.1-1.5), and double support time (+3.5% gait cycle; 95% CI, 1.5-5.4). Recent fallers exhibited an 18% (95% CI, 13%-23%) reduction in gait speed compared with nonfallers (P<.001).

CONCLUSIONS

This review provides the most accurate reference values to-date that can be used to assess the effectiveness of MS gait training programs and therapeutic techniques for individuals who differ on disability severity and fall classification. Some gait adaptations could be part of adopting a more cautious gait strategy and should be factored into the design of future interventions.

[Epidemiological characteristics and the development of spatiotemporal analysis models on hemorrhagic fever with renal syndrome in China]

Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne disease of natural infectious focus caused by Hantavirus (HV) with clinical characteristics as fever, hemorrhage, hyperemia, hypotensive shock and renal damage. Through contacting the excreta or secretion of infected rats, human may get infected. The epidemiological characteristics of HFRS are significantly different in terms of population differences, geographical heterogeneity and seasonal variation, which are all closely related to the habitat of host animals and human productive activities. The reported number of HFRS is about 150 000 to 200 000 each year worldwide, and China accounted for 70%-90% of the total reported cases standing the most seriously infected country. In this study, we reviewed the epidemiological characteristics and the influencing factors of HFRS as well as the models and methods used in relevant ecological studies, in order to understand the distribution of time, regional and population and potential influencing factors on the transmission of HFRS better, so as to improve the strategies on investigation, monitoring, prevention and control of the diseases.