Spatial versus spatio-temporal approaches for studying metacommunities: a multi-taxon analysis in Mediterranean and tropical temporary ponds.

Prior research on metacommunities has largely focused on snapshot surveys, often overlooking temporal dynamics. In this study, our aim was to compare the insights obtained from metacommunity analyses based on a spatial approach repeated over time, with a spatio-temporal approach that consolidates all data into a single model. We empirically assessed the influence of temporal variation in the environment and spatial connectivity on the structure of metacommunities in tropical and Mediterranean temporary ponds. Employing a standardized methodology across both regions, we surveyed multiple freshwater taxa in three time periods within the same hydrological year from multiple temporary ponds in each region. To evaluate how environmental, spatial and temporal influences vary between the two approaches, we used nonlinear variation partitioning analyses based on generalized additive models. Overall, this study underscores the importance of adopting spatio-temporal analytics to better understand the processes shaping metacommunities. While the spatial approach suggested that environmental factors had a greater influence, our spatio-temporal analysis revealed that spatial connectivity was the primary driver influencing metacommunity structure in both regions. Temporal effects were equally important as environmental effects, suggesting a significant role of ecological succession in metacommunity structure.

Epidemiological Characteristics of Varicella in Anhui Province, China, 2012-2021: Surveillance Study.

BACKGROUND: Varicella is a mild, self-limited disease caused by varicella-zoster virus (VZV) infection. Recently, the disease burden of varicella has been gradually increasing in China; however, the epidemiological characteristics of varicella have not been reported for Anhui Province. OBJECTIVE: The aim of this study was to analyze the epidemiology of varicella in Anhui from 2012 to 2021, which can provide a basis for the future study and formulation of varicella prevention and control policies in the province. METHODS: Surveillance data were used to characterize the epidemiology of varicella in Anhui from 2012 to 2021 in terms of population, time, and space. Spatial autocorrelation of varicella was explored using the Moran index (Moran I). The Kulldorff space-time scan statistic was used to analyze the spatiotemporal aggregation of varicella. RESULTS: A total of 276,115 cases of varicella were reported from 2012 to 2021 in Anhui, with an average annual incidence of 44.8 per 100,000, and the highest incidence was 81.2 per 100,000 in 2019. The male-to-female ratio of cases was approximately 1.26, which has been gradually decreasing in recent years. The population aged 5-14 years comprised the high-incidence group, although the incidence in the population 30 years and older has gradually increased. Students accounted for the majority of cases, and the proportion of cases in both home-reared children (aged 0-7 years who are not sent to nurseries, daycare centers, or school) and kindergarten children (aged 3-6 years) has changed slightly in recent years. There were two peaks of varicella incidence annually, except for 2020, and the incidence was typically higher in the winter peak than in summer. The incidence of varicella in southern Anhui was higher than that in northern Anhui. The average annual incidence at the county level ranged from 6.61 to 152.14 per 100,000, and the varicella epidemics in 2018-2021 were relatively severe. The spatial and temporal distribution of varicella in Anhui was not random, with a positive spatial autocorrelation found at the county level (Moran I=0.412). There were 11 districts or counties with high-high clusters, mainly distributed in the south of Anhui, and 3 districts or counties with high-low or low-high clusters. Space-time scan analysis identified five possible clusters of areas, and the most likely cluster was distributed in the southeastern region of Anhui. CONCLUSIONS: This study comprehensively describes the epidemiology and changing trend of varicella in Anhui from 2012 to 2021. In the future, preventive and control measures should be strengthened for the key populations and regions of varicella.

Extraction and spatio-temporal analysis of phenological dates of winter wheat in north Henan Province of China from 2003 to 2018 based on MODIS NDVI time series.

Monitoring of spatio-temporal changes in crop phenology is an important part of the remote sensing of agricultural ecosystems. In this study, the segment turning point method was utilised to determine several phenological dates of winter wheat in northern Henan from 2003 to 2018. The spatio-temporal variation characteristics of these main phenological dates were analyzed, and the effects of temperature and precipitation on phenological changes were investigated. The results showed that: (1) The segment turning point method had strong space-time adaptability, and the RMSE of extracted phenoloical dates of multi-stations in a single year or single station in multi-years was less than 10d. (2) Roughly bounded by 114°E, the trefoil stage, tillering stage, overwintering stage and rising stage of winter wheat in the west were earlier than those in the east of northern Henan in 2018. (3) From 2003 to 2018, the interannual change rates of the trefoil date, tillering date, overwintering date, rising date, booting date, and milky date of winter wheat were 6.92 d/10a, 4.36 d/10a, 0.74 d/10a, -0.1 d/10a, -3.97 d/10a and -2.91 d/10a, indicating the trend of delaying pre-winter phenology and advancing post-winter phenology. (4) The delay of pre-winter phenology and the advance of post-winter phenology of winter wheat were significantly related to the increase in growing season temperature. The results of the study should provide a basis for further understanding of the effects of climate change on winter wheat phenology and to provide a reference for remote sensing monitoring of winter wheat phenology.

Spatiotemporal analysis of mRNA-protein relationships enhances transcriptome-based developmental inference.

Elucidating the complex relationships between mRNA and protein expression at high spatiotemporal resolution is critical for unraveling multilevel gene regulation and enhancing mRNA-based developmental analyses. In this study, we conduct a single-cell analysis of mRNA and protein expression of transcription factors throughout C. elegans embryogenesis. Initially, cellular co-presence of mRNA and protein is low, increasing to a medium-high level (73%) upon factoring in delayed protein synthesis and long-term protein persistence. These factors substantially affect mRNA-protein concordance, leading to potential inaccuracies in mRNA-reliant gene detection and specificity characterization. Building on the learned relationship, we infer protein presence from mRNA expression and demonstrate its utility in identifying tissue-specific genes and elucidating relationships between genes and cells. This approach facilitates identifying the role of sptf-1/SP7 in neuronal lineage development. Collectively, this study provides insights into gene expression dynamics during rapid embryogenesis and approaches for improving the efficacy of transcriptome-based developmental analyses.

Spatiotemporal Analysis of Out-of-Hospital Cardiac Arrest Incidence and Survival Outcomes in Korea (2009-2021).

BACKGROUND: Out-of-hospital cardiac arrest is a major public health concern in Korea. Identifying spatiotemporal patterns of out-of-hospital cardiac arrest incidence and survival outcomes is crucial for effective resource allocation and targeted interventions. Thus, this study aimed to investigate the spatiotemporal epidemiology of out-of-hospital cardiac arrest in Korea, with a focus on identifying high-risk areas and populations and examining factors associated with prehospital outcomes. METHODS: We conducted this population-based observational study using data from the Korean out-of-hospital cardiac arrest registry from January 2009 to December 2021. Using a Bayesian spatiotemporal model based on the Integrated Nested Laplace Approximation, we calculated the standardized incidence ratio and assessed the relative risk to compare the spatial and temporal distributions over time. The primary outcome was out-of-hospital cardiac arrest incidence, and the secondary outcomes included prehospital return of spontaneous circulation, survival to hospital admission and discharge, and good neurological outcomes. RESULTS: Although the number of cases increased over time, the spatiotemporal analysis exhibited a discernible temporal pattern in the standardized incidence ratio of out-of-hospital cardiac arrest with a gradual decline over time (1.07; 95% credible interval [CrI], 1.04-1.09 in 2009 vs. 1.00; 95% CrI, 0.98-1.03 in 2021). The district-specific risk ratios of survival outcomes were more favorable in the metropolitan and major metropolitan areas. In particular, the neurological outcomes were significantly improved from relative risk 0.35 (0.31-0.39) in 2009 to 1.75 (1.65-1.86) in 2021. CONCLUSION: This study emphasized the significance of small-area analyses in identifying high-risk regions and populations using spatiotemporal analyses. These findings have implications for public health planning efforts to alleviate the burden of out-of-hospital cardiac arrest in Korea.

Spatio-Temporal analysis of exports of cultural products and their affecting factors for spatial distribution.

Cultural products constitute a significant portion of global trade, and understanding their export patterns can shed light on economic trends, trade dynamics, and market opportunities. This study conducted the spatio-temporal analysis of exports of cultural products, exploring the relationship between various influencing factors and their impact on the spatial distribution of these exports. Leveraging a diverse dataset encompassing 55 BRI countries for the period of 2005-2022, this research employs advanced spatial analysis techniques, including spatial autocorrelation and spatial regression models, to examine the spatial patterns and determinants of exports if cultural product exports. Moreover, this study delves into the multifaceted determinants affecting the spatial distribution of these exports. The findings of this study reveal significant spatio-temporal variations in the exports of cultural products. Spatial autocorrelation analysis indicates the presence of spatial clustering, suggesting that regions with high cultural product exports tend to be geographically close to each other. The spatial regression models further identify several key factors like economic development, productive capacities, cultural tourism, information development and human capital influence the spatial distribution of these exports. The findings of the study reveal that there is strong spatial relationship for exports of cultural products in BRI countries. The findings of this research contribute valuable insights for policymakers, businesses, and stakeholders regarding a deeper comprehension of the driving forces behind the spatial distribution of these cultural products, facilitating informed decision-making processes to optimize strategies for promoting and sustaining the trade of cultural products in an increasingly interconnected world.

Distribution of hepatitis C virus in eastern China from 2011 to 2020: a Bayesian spatiotemporal analysis.

Objective: This study aimed to evaluate the spatiotemporal distribution of patients with hepatitis C virus (HCV) and the factors influencing this distribution in Jiangsu Province, China, from 2011 to 2020. Methods: The incidence of reported HCV in Jiangsu Province from 2011 to 2020 was obtained from the Chinese Information System for Disease Control and Prevention (CISDCP). R and GeoDa software were used to visualize the spatiotemporal distribution and the spatial autocorrelation of HCV. A Bayesian spatiotemporal model was constructed to explore the spatiotemporal distribution of HCV in Jiangsu Province and to further analyze the factors related to HCV. Results: A total of 31,778 HCV patients were registered in Jiangsu Province. The registered incidence rate of HCV increased from 2.60/100,000 people in 2011 to 4.96/100,000 people in 2020, an increase of 190.77%. Moran's I ranged from 0.099 to 0.354 (P < 0.05) from 2011 to 2019, indicating a positive spatial correlation overall. The relative risk (RR) of the urbanization rate, the most important factor affecting the spread of HCV in Jiangsu Province, was 1.254 (95% confidence interval: 1.141-1.376), while other factors had no significance. Conclusion: The reported HCV incidence rate integrally increased in the whole Jiangsu Province, whereas the spatial aggregation of HCV incidence was gradually weakening. Our study highlighted the importance of health education for the floating population and reasonable allocation of medical resources in the future health work.

The impact of meteorological factors on tuberculosis incidence in Spain: a spatiotemporal analysis.

Tuberculosis (TB) remains a global leading cause of death, necessitating an investigation into its unequal distribution. Sun exposure, linked to vitamin D (VD) synthesis, has been proposed as a protective factor. This study aimed to analyse TB rates in Spain over time and space and explore their relationship with sunlight exposure. An ecological study examined the associations between rainfall, sunshine hours, and TB incidence in Spain. Data from the National Epidemiological Surveillance Network (RENAVE in Spanish) and the Spanish Meteorological Agency (AEMET in Spanish) from 2012 to 2020 were utilized. Correlation and spatial regression analyses were conducted. Between 2012 and 2020, 43,419 non-imported TB cases were reported. A geographic pattern (north-south) and distinct seasonality (spring peaks and autumn troughs) were observed. Sunshine hours and rainfall displayed a strong negative correlation. Spatial regression and seasonal models identified a negative correlation between TB incidence and sunshine hours, with a four-month lag. A clear spatiotemporal association between TB incidence and sunshine hours emerged in Spain from 2012 to 2020. VD levels likely mediate this relationship, being influenced by sunlight exposure and TB development. Further research is warranted to elucidate the causal pathway and inform public health strategies for improved TB control.

Bayesian spatio-temporal analysis of the COVID-19 pandemic in Catalonia.

In this study, we modelled the incidence of COVID-19 cases and hospitalisations by basic health areas (ABS) in Catalonia. Spatial, temporal and spatio-temporal incidence trends were described using estimation methods that allow to borrow strength from neighbouring areas and time points. Specifically, we used Bayesian hierarchical spatio-temporal models estimated with Integrated Nested Laplace Approximation (INLA). An exploratory analysis was conducted to identify potential ABS factors associated with the incidence of cases and hospitalisations. High heterogeneity in cases and hospitalisation incidence was found between ABS and along the waves of the pandemic. Urban areas were found to have a higher incidence of COVID-19 cases and hospitalisations than rural areas, while socio-economic deprivation of the area was associated with a higher incidence of hospitalisations. In addition, full vaccination coverage in each ABS showed a protective effect on the risk of COVID-19 cases and hospitalisations.

Bayesian spatio-temporal analysis of malaria prevalence in children between 2 and 10 years of age in Gabon.

BACKGROUND: Gabon still bears significant malaria burden despite numerous efforts. To reduce this burden, policy-makers need strategies to design effective interventions. Besides, malaria distribution is well known to be related to the meteorological conditions. In Gabon, there is limited knowledge of the spatio-temporal effect or the environmental factors on this distribution. This study aimed to investigate on the spatio-temporal effects and environmental factors on the distribution of malaria prevalence among children 2-10 years of age in Gabon. METHODS: The study used cross-sectional data from the Demographic Health Survey (DHS) carried out in 2000, 2005, 2010, and 2015. The malaria prevalence was obtained by considering the weighting scheme and using the space-time smoothing model. Spatial autocorrelation was inferred using the Moran's I index, and hotspots were identified with the local statistic Getis-Ord General Gi. For the effect of covariates on the prevalence, several spatial methods implemented in the Integrated Nested Laplace Approximation (INLA) approach using Stochastic Partial Differential Equations (SPDE) were compared. RESULTS: The study considered 336 clusters, with 153 (46%) in rural and 183 (54%) in urban areas. The prevalence was highest in the Estuaire province in 2000, reaching 46%. It decreased until 2010, exhibiting strong spatial correlation (P < 0.001), decreasing slowly with distance. Hotspots were identified in north-western and western Gabon. Using the Spatial Durbin Error Model (SDEM), the relationship between the prevalence and insecticide-treated bed nets (ITNs) coverage was decreasing after 20% of coverage. The prevalence in a cluster decreased significantly with the increase per percentage of ITNs coverage in the nearby clusters, and per degree Celsius of day land surface temperature in the same cluster. It slightly increased with the number of wet days and mean temperature per month in neighbouring clusters. CONCLUSIONS: In summary, this study showed evidence of strong spatial effect influencing malaria prevalence in household clusters. Increasing ITN coverage by 20% and prioritizing hotspots are essential policy recommendations. The effects of environmental factors should be considered, and collaboration with the national meteorological department (DGM) for early warning systems is needed.

[Epidemiological and spatial-temporal clustering characteristics of pertussis in Hebei Province from 2013 to 2022].

Objective: To analyze the spatial-temporal epidemiological characteristics of pertussis from 2013 to 2022 in Hebei Province and to provide a reference for improving prevention and control measures. Methods: Based on the data of pertussis reported in Hebei Province during 2013-2022 to analyze the popular characteristic, the ArcGIS 10.8 software was used to construct a ring map and to perform spatial autocorrelation analysis; the SaTScan 10.1 software was used for spatial-temporal scan statistics. Results: There were 6 715 cases of the cumulative report in Hebei Province from 2013 to 2022 without death. The annual report incidence was 0.90/100 000. The overall incidence rate showed an upward trend from 2013 to 2019, and during 2020-2021, it showed a sharp decline, but in 2022, it showed a sharp increase. Summer and autumn are the peak seasons of the epidemic. The incidence was highest in age group <1 year (48.67%), and the lowest age group in age group ≥15 years (0.45%) and mainly scattered children (78.03%); the incidence about men is higher than women. Spatial autocorrelation analysis showed that the onset of pertussis has spatial clustering, and high-high clusters were found in Langfang, Baoding, and Cangzhou, the top three countries with reported incidence. The area covered by a low-low cluster was consistent with the distribution of the corresponding low-incidence areas in this study. Space-time scan detects five statistically significant areas, and three zones were concentrated in 2022. Conclusions: The incidence of pertussis in Hebei had obvious season, population, and area-specific differences. There was obvious spatiotemporal and clustering, so the control of key areas should target the characteristics of time and space.

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

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

Mortality in Women with Coronary Artery Disease in Paraná State, Brazil: A Bayesian Spatiotemporal Analysis.

Background: Mortality resulting from coronary artery disease (CAD) among women is a complex issue influenced by many factors that encompass not only biological distinctions but also sociocultural, economic, and healthcare-related components. Understanding these factors is crucial to enhance healthcare provisions. Therefore, this study seeks to identify the social and clinical variables related to the risk of mortality caused by CAD in women aged 50 to 79 years old in Paraná state, Brazil, between 2010 and 2019. Methods: This is an ecological study based on secondary data sourced from E-Gestor, IPARDES, and DATASUS. We developed a model that integrates both raw and standardized coronary artery disease (CAD) mortality rates, along with sociodemographic and healthcare service variables. We employed Bayesian spatiotemporal analysis with Markov Chain Monte Carlo simulations to assess the relative risk of CAD mortality, focusing specifically on women across the state of Paraná. Results: A total of 14,603 deaths from CAD occurred between 2010 and 2019. Overall, temporal analysis indicates that the risk of CAD mortality decreased by around 22.6% between 2010 (RR of 1.06) and 2019 (RR of 0.82). This decline was most prominent after 2014. The exercise stress testing rate, accessibility of cardiology centers, and IPARDES municipal performance index contributed to the reduction of CAD mortality by approximately 4%, 8%, and 34%, respectively. However, locally, regions in the Central-West, Central-South, Central-East, and Southern regions of the Central-North parts of the state exhibited risks higher-than-expected. Conclusion: In the last decade, CAD-related deaths among women in Paraná state decreased. This was influenced by more exercise stress testing, better access to cardiology centers, improved municipal performance index. Yet, elevated risks of deaths persist in certain regions due to medical disparities and varying municipal development. Therefore, prioritizing strategies to enhance women's access to cardiovascular healthcare in less developed regions is crucial.

Understanding spatiotemporal patterns of COVID-19 incidence in Portugal: A functional data analysis from August 2020 to March 2022.

During the SARS-CoV-2 pandemic, governments and public health authorities collected massive amounts of data on daily confirmed positive cases and incidence rates. These data sets provide relevant information to develop a scientific understanding of the pandemic's spatiotemporal dynamics. At the same time, there is a lack of comprehensive approaches to describe and classify patterns underlying the dynamics of COVID-19 incidence across regions over time. This seriously constrains the potential benefits for public health authorities to understand spatiotemporal patterns of disease incidence that would allow for better risk communication strategies and improved assessment of mitigation policies efficacy. Within this context, we propose an exploratory statistical tool that combines functional data analysis with unsupervised learning algorithms to extract meaningful information about the main spatiotemporal patterns underlying COVID-19 incidence on mainland Portugal. We focus on the timeframe spanning from August 2020 to March 2022, considering data at the municipality level. First, we describe the temporal evolution of confirmed daily COVID-19 cases by municipality as a function of time, and outline the main temporal patterns of variability using a functional principal component analysis. Then, municipalities are classified according to their spatiotemporal similarities through hierarchical clustering adapted to spatially correlated functional data. Our findings reveal disparities in disease dynamics between northern and coastal municipalities versus those in the southern and hinterland. We also distinguish effects occurring during the 2020-2021 period from those in the 2021-2022 autumn-winter seasons. The results provide proof-of-concept that the proposed approach can be used to detect the main spatiotemporal patterns of disease incidence. The novel approach expands and enhances existing exploratory tools for spatiotemporal analysis of public health data.

The spatiotemporal trend of human brucellosis in China and driving factors using interpretability analysis.

Human brucellosis has reemerged in China, with a distinct change in its geographical distribution. The incidence of human brucellosis has significantly risen in inland regions of China. To gain insights into epidemic characteristics and identify factors influencing the geographic spread of human brucellosis, our study utilized the Extreme Gradient Boosting (XGBoost) algorithm and interpretable machine learning techniques. The results showed a consistent upward trend in the incidence of human brucellosis, with a significant increase of 8.20% from 2004 to 2021 (95% CI: 1.70, 15.10). The northern region continued to face a serious human situation, with a gradual upward trend. Meanwhile, the western and southern regions have experienced a gradual spread of human brucellosis, encompassing all regions of China over the past decade. Further analysis using Shapley Additive Explanations (SHAP) demonstrated that higher Gross Domestic Product (GDP) per capita and increased funding for education have the potential to reduce the spread. Conversely, the expansion of human brucellosis showed a positive correlation with bed availability per 1000 individuals, humidity, railway mileage, and GDP. These findings strongly suggest that socioeconomic factors play a more significant role in the spread of human brucellosis than other factors.

Spatio-Temporal Analysis of Cholangiocarcinoma in a High Prevalence Area of Northeastern Thailand: A 10-Year Large Scale Screening Program.

BACKGROUND: Cholangiocarcinoma (CCA) is experiencing a global increase, particularly in Northeast Thailand, which has the highest global incidence rates. However, there is a paucity of studies on CCA screening, especially in high-risk populations. This study aimed to investigate the distribution and spatial patterns of CCA in Northeast Thailand over a ten-year screening period. METHODS: The study included CCA patients from the Cholangiocarcinoma Screening and Care Program (CASCAP) between 2013 and 2022, which encompasses 20 provinces and 282 districts in Northeast of Thailand. CCA data were based on pathological diagnosis to determine the distribution and spatial patterns. RESULTS: Of the 2,515 CCA patients, approximately two-thirds were males (63.98%), and the majority were aged over 55 years (72.72%), with a mean age of 61.12 ± 9.13 years. The highest percentage of CCA cases occurred in 2014 at 19.01% of all patients, followed by 2018 at 15.23%. The overall CCA incidence rate in Northeast Thailand over ten years was 32 per 100,000 population. Hotspot statistical analysis identified high-scoring geographic clusters in the upper and middle regions, showing a tendency to expand from hotspot areas into nearby areas. CONCLUSION: The distribution of CCA in Northeast Thailand has continued to rise over the past decade, particularly in the upper and middle regions. Targeted screening in high-risk areas and increased awareness of CCA risks are crucial to mitigate its impact.

Was the COVID-19 epidemic synchronous in space? An analysis in the health regions of the Rio de Janeiro state, 2020-2022.

OBJECTIVE: To analyze the spatio-temporal dynamics of COVID-19 in the Rio de Janeiro state within the nine health regions, between March 2020 and December 2022. METHODS: The Poisson model with random effects was used to smooth and estimate the incidence of COVID-19 hospitalizations reported in the Influenza Epidemiological Surveillance Information System (SIVEP-Gripe) to verify the synchronicity of the epidemic in the state. RESULTS: The COVID-19 epidemic in the state is characterized by the presence of seven peaks during the analyzed period corresponding to seven found. An asynchrony in hospitalizations was identified, varying according to the different virus variants in the nine health regions of the state. The incidence peaks of hospitalizations ranged from 1 to 12 cases per 100,000 inhabitants during the pandemic. CONCLUSION: This spatio-temporal analysis is applicable to other scenarios, enabling monitoring and decision-making for the control of epidemic diseases in different areas.

Spatiotemporal pattern of leprosy in southwest China from 2010 to 2020: an ecological study.

BACKGROUND: Despite many efforts to control leprosy worldwide, it is still a significant public health problem in low- and middle-income regions. It has been endemic in China for thousands of years, and southwest China has the highest leprosy burden in the country. METHODS: This observational study was conducted with all newly detected leprosy cases in southwest China from 2010 to 2020. Data were extracted from the Leprosy Management Information System (LEPMIS) database in China. The Joinpoint model was used to determine the time trends in the study area. Spatial autocorrelation statistics was performed to understand spatial distribution of leprosy cases. Spatial scan statistics was applied to identify significant clusters with high rate. RESULTS: A total of 4801 newly detected leprosy cases were reported in southwest China over 11 years. The temporal trends declined stably. The new case detection rate (NCDR) dropped from 4.38/1,000,000 population in 2010 to 1.25/1,000,000 population in 2020, with an average decrease of 12.24% (95% CI: -14.0 to - 10.5; P < 0.001). Results of global spatial autocorrelation showed that leprosy cases presented clustering distribution in the study area. Most likely clusters were identified during the study period and were frequently located at Yunnan or the border areas between Yunnan and Guizhou Provinces. Secondary clusters were always located in the western counties, the border areas between Yunnan and Sichuan Provinces. CONCLUSIONS: Geographic regions characterized by clusters with high rates were considered as leprosy high-risk areas. The findings of this study could be used to design leprosy control measures and provide indications to strengthen the surveillance of high-risk areas. These areas should be prioritized in the allocation of resources.

Spatial and spatio-temporal clusters of lung cancer incidence by stage of disease in Michigan, United States 1985-2018.

Lung cancer is the most common cause of cancer-related death in Michigan. Most patients are diagnosed at advanced stages of the disease. There is a need to detect clusters of lung cancer incidence over time, to generate new hypotheses about causation and identify high-risk areas for screening and treatment. The Michigan Cancer Surveillance database of individual lung cancer cases, 1985 to 2018 was used for this study. Spatial and spatiotemporal clusters of lung cancer and level of disease (localized, regional and distant) were detected using discrete Poisson spatial scan statistics at the zip code level over the study time period. The approach detected cancer clusters in cities such as Battle Creek, Sterling Heights and St. Clair County that occurred prior to year 2000 but not afterwards. In the northern area of the lower peninsula and the upper peninsula clusters of late-stage lung cancer emerged after year 2000. In Otter Lake Township and southwest Detroit, late-stage lung cancer clusters persisted. Public and patient education about lung cancer screening programs must remain a health priority in order to optimize lung cancer surveillance. Interventions should also involve programs such as telemedicine to reduce advanced stage disease in remote areas. In cities such as Detroit, residents often live near industry that emits air pollutants. Future research should therefore, continue to focus on the geography of lung cancer to uncover place-based risks and in response, the need for screening and health care services.

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.