Autocorrelation Patterns Research Articles

Occurrences of Dengue Fever, Dengue Hemorrhagic Fever, Dengue Shock Syndrome, Severe Dengue, Dengue Warning Signs in Bandung City: An Spatial Study Based on Moran Index

Dengue cases is a tropical disease transmitted by Aedes aegypti and Aedes albopictus mosquitoes, which has become an international health issue in recent decades. The purpose of this study is to determine the autocorrelation and distribution patterns of Dengue Fever (DF), Dengue Hemorrhagic Fever (DHF), Dengue Shock Syndrome (DSS), Severe Dengue (SD), and Dengue Warning Signs (DWS) in the operational area of Hospital X. This study is classified as correlational, with a retrospective cohort design, utilizing patient medical records from January 2 to May 15, 2024. The total sample consists of 1,698 records, collected through purposive sampling, with the criteria being patient medical records diagnosed with DF, DHF, DSS, SD, and DWS during 2024. The z-score indicates that clustered distribution patterns occur in DF cases (z-score 5.07) and DHF cases (z-score 9.85). Random distribution patterns occur in DSS cases (z-score 0.86), SD cases (z-score -0.35), and DWS cases (z-score -0.15). The hypothesis that shows autocorrelation (p-value 0.000) is that the occurrence of DF in one location correlates with DF occurrences in surrounding locations, and the occurrence of DHF in one location correlates (p-value 0.000) with DHF occurrences in surrounding locations

Autocorrelation as a critical factor of growth depensation of tropical trees in the Chocó biogeographic region

Growth depensation, the variation of size with age exhibited by populations, is attributed to biological, ecological, and environmental factors, as well as autocorrelation. Several studies have focused on the study of diameter growth of tropical trees to obtain information on ontogenic traits and silvicultural metrics of interest for ecology and forest management. However, few studies have rigorously and adequately considered autocorrelation as a primary factor contributing to growth depensation. The aim of this study was to investigate diameter growth in tree species from the Chocó biogeographic region. We used tree-ring data corresponding to 38 trees and 5 species. Our modeling approach included von Bertalanffy type equations to estimate diameter growth trajectories for each species using mixed effects models. ARIMA specifications were included in the residual terms to account for autocorrelation. The estimated parameters allowed us to calculate ontogenic traits and silvicultural metrics for each species. The results indicate that autocorrelation was a critical factor in growth depensation for all species studied, and was satisfactorily accounted for by the proposed modeling approach. Autocorrelation patterns on residuals showed a stochastic trend and were investigated by correlation structures of ARIMA(1,1,0) and ARIMA(2,1,0). Ontogenic traits and silvicultural metrics obtained for these species were biologically consistent, providing reliable and useful information to understand the population ecology of tropical trees and to inform management and conservation strategies of natural forests.

T1-weighted MRI texture analysis in amyotrophic lateral sclerosis patients stratified by the D50 progression model.

Amyotrophic lateral sclerosis, a progressive neurodegenerative disease, presents challenges in predicting individual disease trajectories due to its heterogeneous nature. This study explores the application of texture analysis on T1-weighted MRI in patients with amyotrophic lateral sclerosis, stratified by the D50 disease progression model. The D50 model, which offers a more nuanced representation of disease progression than traditional linear metrics, calculates the sigmoidal curve of functional decline and provides independent quantifications of disease aggressiveness and accumulation. In this research, a representative cohort of 116 patients with amyotrophic lateral sclerosis was studied using the D50 model and texture analysis on MRI images. Texture analysis, a technique used for quantifying voxel intensity patterns in MRI images, was employed to discern alterations in brain tissue associated with amyotrophic lateral sclerosis. This study examined alterations of the texture feature autocorrelation across sub-groups of patients based on disease accumulation, aggressiveness and the first site of onset, as well as in direct regressions with accumulation/aggressiveness. The findings revealed distinct patterns of the texture-derived autocorrelation in grey and white matter, increase in bilateral corticospinal tract, right hippocampus and left temporal pole as well as widespread decrease within motor and extra-motor brain regions, of patients stratified based on their disease accumulation. Autocorrelation alterations in grey and white matter, in clusters within the left cingulate gyrus white matter, brainstem, left cerebellar tonsil grey matter and right inferior fronto-occipital fasciculus, were also negatively associated with disease accumulation in regression analysis. Otherwise, disease aggressiveness correlated with only two small clusters, within the right superior temporal gyrus and right posterior division of the cingulate gyrus white matter. The findings suggest that texture analysis could serve as a potential biomarker for disease stage in amyotrophic lateral sclerosis, with potential for quick assessment based on using T1-weighted images.

Mycorrhizal and endophytic fungi structure forest below-ground symbiosis through contrasting but interdependent assembly processes

BackgroundInteractions between plants and diverse root-associated fungi are essential drivers of forest ecosystem dynamics. The symbiosis is potentially dependent on multiple ecological factors/processes such as host/symbiont specificity, background soil microbiome, inter-root dispersal of symbionts, and fungus–fungus interactions within roots. Nonetheless, it has remained a major challenge to reveal the mechanisms by which those multiple factors/processes determine the assembly of root-associated fungal communities. Based on the framework of joint species distribution modeling, we examined 1,615 root-tips samples collected in a cool-temperate forest to reveal how root-associated fungal community structure was collectively formed through filtering by host plants, associations with background soil fungi, spatial autocorrelation, and symbiont–symbiont interactions. In addition, to detect fungi that drive the assembly of the entire root-associated fungal community, we inferred networks of direct fungus–fungus associations by a statistical modeling that could account for implicit environmental effects.ResultsThe fine-scale community structure of root-associated fungi were best explained by the statistical model including the four ecological factors/processes. Meanwhile, among partial models, those including background soil fungal community structure and within-root fungus–fungus interactions showed the highest performance. When fine-root distributions were examined, ectomycorrhizal fungi tended to show stronger associations with background soil community structure and spatially autocorrelated patterns than other fungal guilds. In contrast, the distributions of root-endophytic fungi were inferred to depend greatly on fungus–fungus interactions. An additional statistical analysis further suggested that some endophytic fungi, such as Phialocephala and Leptodontidium, were placed at the core positions within the web of direct associations with other root-associated fungi.ConclusionBy applying emerging statistical frameworks to intensive datasets of root-associated fungal communities, we demonstrated background soil fungal community structure and fungus–fungus associations within roots, as well as filtering by host plants and spatial autocorrelation in ecological processes, could collectively drive the assembly of root-associated fungi. We also found that basic assembly rules could differ between mycorrhizal and endophytic fungi, both of which were major components of forest ecosystems. Consequently, knowledge of how multiple ecological factors/processes differentially drive the assembly of multiple fungal guilds is indispensable for comprehensively understanding the mechanisms by which terrestrial ecosystem dynamics are organized by plant–fungal symbiosis.

Demographic Change and Regional Specialisation: The Case of Greek NUTS II Regions

This paper investigates the impact of demographic changes on regional specialisation across Greek NUTS2 regions from 2001 to 2021. It explores the relationship between age-specific variations in the economically active population and gross value added (GVA) across different economic sectors. Using demographic and GVA data alongside spatial analysis techniques such as spatial regression models, median Local Moran’s I, and location quotient, the study identifies spatial autocorrelation patterns. Findings reveal strong correlations between the economically active population and GVA, highlighting demographic factors’ crucial role in regional economic performance. Over the period 2001 to 2021, the 15–24 and 25–34 age groups generally experienced declining growth rates in total, male, and female economically active populations, while the 45–54 and 55–64 age groups saw increases. Regions adjacent to those with higher economically active populations showed high–high clustering. Especially, Attiki and Kentriki Makedonia, the two metropolitan regions of Greece, influence neighbouring regions through economic spillover effects, fostering economic sector clustering and emphasising regional specialisation. These findings highlight the complex interplay between demographic shifts and regional economic outcomes, noting spatial disparities and the need for targeted policy interventions. The study provides empirical evidence demonstrating how demographic changes influence regional economic development across different regions.

Spatiotemporal patterns of consumer financial fraud in the United States

PurposePrevious studies on consumer financial fraud (CFF) have primarily focused on micro-level relationships. This study seeks to provide a holistic macro-level perspective of CFF patterns in the USA. We explore whether CFFs follow a geographical pattern in the USA and evaluate whether and how the patterns and strength of spatial interrelations between states have changed over time, particularly pre-, during and post-COVID-19 Pandemic.Design/methodology/approachThis research investigates the spatial patterns inherent in four CFF variables – total reported frauds, percentage of frauds reporting a loss, total losses and median loss – across the contiguous USA from 2018 to 2022. An in-depth examination was conducted at the state level by applying Moran's I method on the consumer sentinel network data, a database administered by the Federal Trade Commission.FindingsThe findings provide robust and statistically significant spatial autocorrelation of four CFF variables across the contiguous USA that are persistent from 2018 to 2022, consistent across all discerned patterns. Moreover, upon aggregating average values over the entire study period, total losses emerge as the dimension displaying the most pronounced positive clustering. Finally, the strength of spatial autocorrelation patterns has increased post-COVID-19 Pandemic for total reported frauds, percentage of frauds reporting a loss and total losses, and it has reduced for the median loss.Practical implicationsThe sustained spatial autocorrelation in total losses underscores an elevated interconnectedness in economic and social dynamics among neighboring states. This implies that states in close proximity are predisposed to exhibit analogous levels of total and median losses. This reveals a discernible pattern in the distribution of total losses across contiguous US states, even though the values of total reported frauds and total losses variables were adjusted based on the state population.Social implicationsThe findings furnish valuable insights for policymakers, consumer protection agencies, federal and local government agencies and law enforcement agencies, offering a nuanced understanding and targeted interventions to address the spatial dimensions of CFF effectively. The increase in the strength of the spatial dependencies following COVID-19 shows the increased importance of considering spatial dependencies when designing policies and activities to combat CFF activities. The sustained spatial autocorrelation in total losses underscores an elevated interconnectedness in economic and social dynamics among neighboring states. States in close proximity are predisposed to exhibit analogous levels of total and median losses. This finding reveals a discernible pattern in the distribution of total losses across contiguous US states. To account for state size, the total number of reported frauds and total monetary losses variables were adjusted based on the state's population.Originality/valueThe study provides empirical evidence for spatial autocorrelation for CFF patterns across the states within the contiguous USA. The work shows that adopting a spatial approach to studying CFF offers a promising area for future research.

Explanation for the Absence of Secondary Peaks in Black Hole Light Curve Autocorrelations.

The observed radiation from hot gas accreting onto a black hole depends on both the details of the flow and the spacetime geometry. The lensing behavior of a black hole produces a distinctive pattern of autocorrelations within its photon ring that encodes its mass, spin, and inclination. In particular, the time autocorrelation of the light curve is expected to display a series of peaks produced by light echoes of the source, with each peak delayed by the characteristic time lapse τ between light echoes. However, such peaks are absent from the light curves of observed black holes. Here, we develop an analytical model for such light curves that demonstrates how, even though light echoes always exist in the signal, they do not produce autocorrelation peaks if the characteristic correlation timescale λ_{0} of the source is greater than τ. We validate our model against simulated light curves of a stochastic accretion model ray traced with a general-relativistic code, and then fit the model to an observed light curve for Sgr A^{*}. We infer that λ_{0}>τ, providing an explanation for the absence of light echoes in the time autocorrelations of Sgr A^{*} light curves. Our results highlight the importance for black hole parameter inference of spatially resolving the photon ring via future space-based interferometry.

Evaluation Index System of Rural Ecological Revitalization in China: A National Empirical Study Based on the Driver-Pressure-State-Impact-Response Framework

Rural ecological revitalization (RER) is one of the five goals of China’s rural revitalization strategy. However, there is a lack of an effective index system to evaluate RER levels, which hinders the implementation of this national policy and reduces the effectiveness and efficiency of public resource input. Using the driver-pressure-state-impact-response (DPSIR) framework, this study developed an evaluation framework consisting of 5 subsystems, 12 secondary indicators, and 33 tertiary indicators. Using the entropy-weighted TOPSIS method, we analyzed a set of 30 provinces’ data and empirically determined the weights of each indicator. We found that the response subsystem had the largest weight (0.338), followed by the state (0.271), impact (0.148), pressure (0.130), and driver (0.113). We then evaluated the RER level in each province and found that five provinces had high RER levels, 16 provinces had moderate RER levels, and nine provinces had low RER levels. Using Moran’s I, we examined spatial autocorrelation of provincial RER levels at global and local dimensions. We found significant positive global autocorrelations across all subsystems, indicating that geological aggregation exists in all RER subsystems. The local autocorrelation results showed that low–low and high–high patterns were the dominant local autocorrelation patterns. According to the findings, we discussed the possible implications of this RER evaluation index system and provided policy recommendations for strengthening RER in different regions across the country.

Monitoring annual meteorological drought in arid and semi-arid watersheds by SPI12 drought index and spatial autocorrelation pattern analysis: a case study of the Khuzestan province, Southwest Iran

Monitoring annual meteorological drought in arid and semi-arid watersheds by SPI12 drought index and spatial autocorrelation pattern analysis: a case study of the Khuzestan province, Southwest Iran

The relationship between online searches and suicide.

In this study, we examined the relationship between 131 suicide related Google search terms, grouped into nine categories, and the number of suicide cases per month in Ecuador from January 2011 to December 2021. First, we applied time-series analysis to eliminate autocorrelation and seasonal patterns to prevent spurious correlations. Second, we used Pearson's correlation to assess the relationship between Google search terms and suicide rates. Third, cross-correlation analysis was used to explore the potential delayed effects between these variables. Fourth, we extended the correlation and cross-correlation analyses by three demographic characteristics - gender, age, and region. Significant correlations were found in all categories between Google search trends and suicide rates in Ecuador, with predominantly positive and moderate correlations. The terms 'stress' (.548), 'prevention' (.438), and 'disorders' (.435) showed the strongest associations. While global trends indicated moderate correlations, sensitivity analysis revealed higher coefficients in men, young adults, and the Highlands region. Specific patterns emerged in subgroups, such as 'digital violence' showing significant correlations in certain demographics, and 'trauma' presenting a unique temporal pattern in women. In general, cross correlation analysis showed an average negative correlation of -.191 at lag 3. Google search data do not provide further information about users, such as demographics or mental health records. Hence, our results are simply correlations and should not be interpreted as causal effects. Our findings highlight a need for tailored suicide prevention strategies that recognize the complex dynamics of suicide risk across demographics and time periods.

A benchmark of individual auto-regressive models in a massive fMRI dataset

Abstract Dense functional magnetic resonance imaging datasets open new avenues to create auto-regressive models of brain activity. Individual idiosyncrasies are obscured by group models, but can be captured by purely individual models given sufficient amounts of training data. In this study, we compared several deep and shallow individual models on the temporal auto-regression of BOLD time-series recorded during a natural video-watching task. The best performing models were then analyzed in terms of their data requirements and scaling, subject specificity, and the space-time structure of their predicted dynamics. We found the Chebnets, a type of graph convolutional neural network, to be best suited for temporal BOLD auto-regression, closely followed by linear models. Chebnets demonstrated an increase in performance with increasing amounts of data, with no complete saturation at 9 h of training data. Good generalization to other kinds of video stimuli and to resting-state data marked the Chebnets’ ability to capture intrinsic brain dynamics rather than only stimulus-specific autocorrelation patterns. Significant subject specificity was found at short prediction time lags. The Chebnets were found to capture lower frequencies at longer prediction time lags, and the spatial correlations in predicted dynamics were found to match traditional functional connectivity networks. Overall, these results demonstrate that large individual functional magnetic resonance imaging (fMRI) datasets can be used to efficiently train purely individual auto-regressive models of brain activity, and that massive amounts of individual data are required to do so. The excellent performance of the Chebnets likely reflects their ability to combine spatial and temporal interactions on large time scales at a low complexity cost. The non-linearities of the models did not appear as a key advantage. In fact, surprisingly, linear versions of the Chebnets appeared to outperform the original non-linear ones. Individual temporal auto-regressive models have the potential to improve the predictability of the BOLD signal. This study is based on a massive, publicly-available dataset, which can serve for future benchmarks of individual auto-regressive modeling.

Machine learning-driven source identification and ecological risk prediction of heavy metal pollution in cultivated soils

To overcome challenges in assessing the impact of environmental factors on heavy metal accumulation in soil due to limited comprehensive data, our study in Yangxin County, Hubei Province, China, analyzed 577 soil samples in combination with extensive big data. We used machine learning techniques, the potential ecological risk index, and the bivariate local Moran’s index (BLMI) to predict Cr, Pb, Cd, As, and Hg concentrations in cultivated soil to assess ecological risks and identify pollution sources. The random forest model was selected for its superior performance among various machine learning models, and results indicated that heavy metal accumulation was substantially influenced by environmental factors such as climate, elevation, industrial activities, soil properties, railways, and population. Our ecological risk assessment highlighted areas of concern, where Cd and Hg were identified as the primary threats. BLMI was used to analyze spatial clustering and autocorrelation patterns between ecological risk and environmental factors, pinpointing areas that require targeted interventions. Additionally, redundancy analysis revealed the dynamics of heavy metal transfer to crops. This detailed approach mapped the spatial distribution of heavy metals, highlighted the ecological risks, identified their sources, and provided essential data for effective land management and pollution mitigation.

Spatial auto-correlation and endemicity pattern analysis of crimes against children in Tamil Nadu from 2017 to 2021.

A child is a nation's supreme asset and future. India homes 444 million children, aged between 0 and 18 years, contributing to 19% of the world's children. Crime against children is detrimental to their mental and physical health and affects their growth and development. The National Crime Record Bureau recently reported that a crime targeting children happens every 4 minutes. There is a paucity of literature regarding the burden of crime against children. To understand the magnitude and spatial distribution of crime against children, a retrospective surveillance study was conducted in the state of Tamil Nadu, India, from 2017 to 2021. This is a cross-sectional analytical type of study conducted in KIMSRC, Chengalpattu, Tamil Nadu. The data from the yearly crime review bulletin of Tamil Nadu from 2017 to 2021 were cleaned, transformed, and analyzed using Python v3.8 and subjected to geospatial auto-correlation and hotspot analysis using the Getis-Ord Gi* in ArcGIS Pro v3.1. The endemicity pattern was studied through cluster analysis with Hierarchical Density Based Scanning in Python and visualization in ArcGIS pro v3.1 in the study area. In Tamil Nadu, only one hotspot district in 2017 [Tiruppattur (95% confidence, P < 0.05)] and one hotspot in 2020 [Villupuram (90% confidence, P < 0.1)] were identified, with others being insignificant. The districts which show very high prevalence of crimes against children are Chennai, Ranipet, Chengalpattu, Viluppuram, Tiruvannamalai, Vellore, Tiruppattur, Krishnagiri, Dharmapuri, Salem, Cuddalore, Thanjavur, Tiruchirappalli, Karur, Tiruppur, Coimbatore, Dindigul, Pudukkottai, Sivaganga, Tenkasi, Thoothukkudi, Tirunelveli, and Kanniyakumari. This study identifies key areas within the state of Tamil Nadu which have a high prevalence of crimes against children and also areas that are hotspots for such crimes. Greater resources and measures can now be targeted toward these areas by stakeholders, which can help in the reduction of crimes against children.

Dengue fever mapping in Bangladesh: A spatial modeling approach

AbstractBackgroundEpidemics of the dengue virus can trigger widespread morbidity and mortality along with no specific treatment. Examining the spatial autocorrelation and variability of dengue prevalence throughout Bangladesh's 64 districts was the focus of this study.MethodsThe spatial autocorrelation is evaluated with the help of Moran and Geary . Local Moran was used to detect hotspots and cold spots, whereas local Getis Ord G was used to identify only spatial hotspots. The spatial heterogeneity has been detected using various conventional and spatial models, including the Poisson‐Gamma model, the Poisson‐Lognormal Model, the Conditional Autoregressive (CAR) model, the Convolution model, and the BYM2 model, respectively. These models are implemented using Gibbs sampling and other Bayesian hierarchical approaches to analyze the posterior distribution effectively, enabling inference within a Bayesian context.ResultsThe study's findings show that Moran and Geary analysis provides a substantial clustering pattern of positive spatial autocorrelation of dengue fever (DF) rates between surrounding districts at a 90% confidence interval. The Local Indicators of Spatial Autocorrelation cluster mapped spatial clusters and outliers based on prevalence rates, while the local Getis‐Ord G displayed a thorough breakdown of high or low rates, omitting outliers. Although Chattogram had the most dengue cases (15,752), Khulna district had a higher prevalence rate (133.636) than Chattogram (104.796). The BYM2 model, determined to be well‐fitted based on the lowest Deviance Information Criterion value (527.340), explains a significant association between spatial heterogeneity and prevalence rates.ConclusionThis research pinpoints the district with the highest prevalence rate for dengue and the neighboring districts that also have high risk, allowing government agencies and communities to take the necessary precautions to mollify the risk effect of DF.

Critical slowing down of the Amazon forest after increased drought occurrence

Dynamic ecosystems, such as the Amazon forest, are expected to show critical slowing down behavior, or slower recovery from recurrent small perturbations, as they approach an ecological threshold to a different ecosystem state. Drought occurrences are becoming more prevalent across the Amazon, with known negative effects on forest health and functioning, but their actual role in the critical slowing down patterns still remains elusive. In this study, we evaluate the effect of trends in extreme drought occurrences on temporal autocorrelation (TAC) patterns of satellite-derived indices of vegetation activity, an indicator of slowing down, between 2001 and 2019. Differentiating between extreme drought frequency, intensity, and duration, we investigate their respective effects on the slowing down response. Our results indicate that the intensity of extreme droughts is a more important driver of slowing down than their duration, although their impacts vary across the different Amazon regions. In addition, areas with more variable precipitation are already less ecologically stable and need fewer droughts to induce slowing down. We present findings indicating that most of the Amazon region does not show an increasing trend in TAC. However, the predicted increase in extreme drought intensity and frequency could potentially transition significant portions of this ecosystem into a state with altered functionality.

Assessing the environmental factors affecting the sustainability of Aini Falaj system.

This study investigates the spatial distribution patterns and environmental factors influencing the Aini Falaj system in a specific study area. The research findings are presented through the lens of the following four categories: collinearity diagnostics, spatial autocorrelation analysis, kernel density (KD) findings, and multivariate geographically weighted regression (MGWR) analysis. The collinearity diagnostics were applied to examine the interrelationships among 18 independent environmental variables. The results indicate the absence of significant multicollinearity concerns, with most variables showing values below the critical threshold of five for variance inflation factors (VIFs). The selected variables indicate minimal intercorrelation, suggesting that researchers should be confident utilizing them in subsequent modelling or regression analyses. A spatial autocorrelation analysis using Moran's Index revealed positive spatial autocorrelation and significant clustering patterns in the distribution of live and non-functional Aini Falajs. High concentrations of live or dead Falajs tended to be surrounded by neighbouring areas with similar characteristics. These findings provide insights into the ecological preferences and habitat associations of Aini Falajs, thereby aiding conservation strategies and targeted studies. The kernel density (KD) analysis depicted distribution patterns of live and dry Aini Falajs through hotspots and cold spots. Specific regions exhibited high-density areas of live Falajs, indicating favourable environmental conditions or historical factors contributing to their concentrated distribution. Identifying these high-density zones can enhance our understanding of the spatial patterns and potential factors influencing the prevalence and sustainability of Aini Falajs. The multivariate geographically weighted regression (MGWR) models revealed strong associations between the live or dead status of Aini Falajs and environmental factors. The precipitation, topographic wetness index (TWI), aspect and slope exerted positive impacts on the live status, while evaporation, solar radiation, distance to drains and drain density exerted negative influences. Similar associations were observed for the dead status, emphasising the importance of controlling evaporation, shading mechanisms, proper drainage planning and sustainable land-use practices. This study provides valuable insights into the spatial distributions and factors influencing the live and dead status of Aini Falajs, thereby contributing to our understanding of their ecological dynamics and guiding conservation efforts and management strategies.

Towards Understanding the Microepidemiology of Lymphatic Filariasis at the Community Level in Ghana.

Studies on the distribution of lymphatic filariasis (LF) have mostly focused on reporting prevalence at the community level and distribution at the district levels. Understanding the distribution patterns at community levels may help in designing surveillance strategies. This study aimed to characterize the spatial distribution of LF infections in four hotspot communities in Ghana. The research, involving 252 participants, collected demographic data, mass drug administration (MDA) information, household GPS coordinates, and antigen detection test results. The LF prevalence varied significantly among the communities, with Asemda having the highest (33.33%) and Mempeasem having the lowest (4.44%). Females had lower odds of infection than males (OR = 2.67, p = 0.003 CI: 1.39-5.13). Spatial analysis using kernel density, Anselin Local Moran's, Getis-Ord Gi models, Ordinary Least Squares, and Geographic Weighted Regression revealed mixed patterns of spatial autocorrelation. This study identified LF hotspots, indicating clusters of high or low prevalence with some areas showing disparities between MDA coverage and LF positivity rates. Despite these hotspots, the overall distribution of LF appeared random, suggesting the importance of purposeful sampling in surveillance activities. These findings contribute valuable insights into the micro-epidemiology of LF, emphasizing the need for community-specific investigations to understand the factors influencing the effectiveness of MDA programs in controlling filarial infections. The study highlights the importance of refining surveillance strategies based on community-level distribution patterns.

Inference of genomic landscapes using ordered Hidden Markov Models with emission densities (oHMMed).

Genomes are inherently inhomogeneous, with features such as base composition, recombination, gene density, and gene expression varying along chromosomes. Evolutionary, biological, and biomedical analyses aim to quantify this variation, account for it during inference procedures, and ultimately determine the causal processes behind it. Since sequential observations along chromosomes are not independent, it is unsurprising that autocorrelation patterns have been observed e.g., in human base composition. In this article, we develop a class of Hidden Markov Models (HMMs) called oHMMed (ordered HMM with emission densities, the corresponding R package of the same name is available on CRAN): They identify the number of comparably homogeneous regions within autocorrelated observed sequences. These are modelled as discrete hidden states; the observed data points are realisations of continuous probability distributions with state-specific means that enable ordering of these distributions. The observed sequence is labelled according to the hidden states, permitting only neighbouring states that are also neighbours within the ordering of their associated distributions. The parameters that characterise these state-specific distributions are inferred. We apply our oHMMed algorithms to the proportion of G and C bases (modelled as a mixture of normal distributions) and the number of genes (modelled as a mixture of poisson-gamma distributions) in windows along the human, mouse, and fruit fly genomes. This results in a partitioning of the genomes into regions by statistically distinguishable averages of these features, and in a characterisation of their continuous patterns of variation. In regard to the genomic G and C proportion, this latter result distinguishes oHMMed from segmentation algorithms based in isochore or compositional domain theory. We further use oHMMed to conduct a detailed analysis of variation of chromatin accessibility (ATAC-seq) and epigenetic markers H3K27ac and H3K27me3 (modelled as a mixture of poisson-gamma distributions) along the human chromosome 1 and their correlations. Our algorithms provide a biologically assumptionfree approach to characterising genomic landscapes shaped by continuous, autocorrelated patterns of variation. Despite this, the resulting genome segmentation enables extraction of compositionally distinct regions for further downstream analyses.

Spatial patterns and multilevel analysis of factors associated with paediatric tuberculosis in India

BackgroundTuberculosis (TB) is a serious global health problem that remains as leading cause of high mortality and morbidity in children. Despite India with a high global tuberculosis burden, very few studies have specifically addressed the problem of TB among children, a vulnerable group where delayed diagnosis aggravates the morbidity. MethodsIdentifying the hotspots with high risk of Paediatric TB by employing a localized clustering method can help in developing regional policies for eliminating TB. Factors specified at various levels must be taken into account in studies of health aetiology and their practical applications for disease control. Multilevel analysis is a viable analytic technique for including components identified at many levels in an epidemiologic study and the interindividual variances can be inferred using multilevel analysis. ResultsIn this study, the incidence of tuberculosis pertaining to individual-level attributes are elucidated at district and state level through a multilevel model using the information from National Family Health Survey-5, carried out in 2019–20 comprising 636,699 households over 28 states and 8 union territories of India and the spatial method has detected 62 hotspots. ConclusionThe model expresses a nested structure with districts and states having significant contribution in the variation of paediatric TB and the autocorrelation pattern exhibited by the hotspots emphasises the need for targeted TB elimination programs.

How does regional economic integration affect carbon emission efficiency? Evidence from the Yangtze River Delta, China.

Rapid urbanization and industrialization promote economic growth as well as bring carbon emissions, which seriously threaten the eco-environment and socioeconomic sustainable development. Facing increasing resource constraints, improving carbon emissions efficiency (CEE) is conducive to promote coordinated development of economy and environmental protection. In recent years, regional economic integration (REI) has rapidly developed. It can not only promote factors flow between regions but also achieve industrial and economic agglomeration. However, few studies have been reported in the literature about the relationship between the REI and CEE. In this study, we first illustrate how the REI influences CEE in theory, then take the Yangtze River Delta (YRD) as a case study to conduct empirical research. The results show that (1) the overall CEE value in the YRD has exhibited an upward trend from 2000 to 2020, and its spatial distribution has revealed a significant auto-correlation pattern. (2) On the whole, the REI act a noteworthy positive impact on CEE. When considering types of cities, it is found to have significant positive impacts for the CEE in economically developed cities, while it exhibits a negative impact in the less-developed ones. (3) Upgrading industrial structure and increasing per capita GDP can promote the CEE, but hinder its growth in surrounding areas. Our findings suggest that the government should formulate a unified overall plan to facilitate REI development and establish a modern industrial system of clean and low-carbon to promote regional sustainable development.