Moran Statistics Research Articles

Disparities in access to water, sanitation, and hygiene (WASH) services and the status of SDG-6 implementation across districts and states in India

Disparities in access to water, sanitation, and hygiene (WASH) services and the status of SDG-6 implementation across districts and states in India

IDENTIFIKASI POLA DEMOGRAFI DI PROVINSI JAWA BARAT DENGAN SPATIAL AUTOCORRELATION: MEMPERSIAPKAN MOMENTUM PUNCAK BONUS DEMOGRAFI

West Java is the most populated province in Indonesia, and this places quite a heavy burden in facing the wave of demographic dividend which is predicted to occur in 2030-2035. This article aims to identify patterns of similarity and anomalous values from West Java demographics using spatial autocorrelation. The data used consists of population growth rates at the Regency/City level in West Java as well as growth factors such as birth rates (TFR), infant mortality (IMR), and migration. To identify spatial patterns from demographic variables, Global Moran statistics and Local Indicators of Spatial Association (LISA) with the ArcMap application are used. The results show that there is global and local spatial autocorrelation for the demographic variables considered. The use of LISA also shows the existence of clusters and outliers that are formed as well as an indication of their location in West Java. The contribution of this study is to provide an overview of significant locations in West Java for policy studies and further research in preparing for the momentum of the demographic dividend.

Spatio-Temporal Analysis and Clinical-Epidemiological Characterization of Visceral Leishmaniasis in Maranhão, Brazil, from 2009 to 2020.

This study was carried out to identify the spatial distribution and characterize the clinical-epidemiological profile of Visceral Leishmaniasis (VL) in Maranhão state, Brazil, from 2009 to 2020. This descriptive ecological study collected sociodemographic and clinical data of VL cases from the Brazilian Notifiable Diseases Information System database. A spatial autocorrelation analysis (Moran statistics) was performed. From 2009 to 2020, 5699 cases of VL were reported, with incidence of 6.5 cases/100,000 and prevalence of 7.1 cases/100,000. The temporal analysis showed a significant growth in incidence from 2009 to 2018, followed by a significant decrease between 2019 and 2020. The Moran map shows hotspots of high values in the central-west and central-east regions, and hotspots of low values in the northern region of Maranhão. The profile of patients affected by VL comprises males (OR = 1.8; IC95% = 1.72-1.92), aged under 14 years, brown, and with incomplete elementary schooling. The main symptoms reported were fever, fatigue, and edema. The main diagnostic method was laboratory. The mortality rate was 6.8%, and co-infection with HIV was reported by 8.5% of patients. The results of this study indicated the increase in incidence and lethality, as well as the expansion, of leishmaniasis in the state of Maranhão.

Confounded Local Inference: Extending Local Moran Statistics to Handle Confounding

Local statistical analysis has long been of interest to social and environmental scientists who analyze geographic data. Research into local spatial statistics experienced a step-change in the mid-1990s, which provided a large class of local statistical methods and models. The local Moran statistic is one commonly used local indicator of spatial association, able to detect both areas of similarity and observations that are very dissimilar from their surroundings. From this, many further local statistics have been developed to characterize spatial clusters and outliers. These statistics have seen limited adoption because they do not sufficiently model the relationships involved in confounded spatial data, where the analyst seeks to understand the local spatial structure of a given outcome variable that is influenced by one or more additional factors. Recent innovations used to do joint multivariate local analysis also do not model this kind of conditional local structure in data. This article provides tools to rigorously characterize confounded local inference and a new and different class of multivariate conditional local Moran statistics that can account for confounding. To do this, we return to the Moran scatterplot as the critical tool for local Moran-style covariance statistics. Extending this concept, a new method is available directly from a “Moran-form” multiple regression. We show the empirical and theoretical properties of this statistic, show how some existing heuristic approaches arise naturally from this framework, and show how the use of conditional inference can change interpretations in an empirical analysis of rent and housing stock in a rapidly changing neighborhood.

Spatial-temporal risk clusters, social vulnerability, and identification of priority areas for surveillance and control of cutaneous leishmaniasis in Maranhão, Brazil: an ecological study.

Cutaneous leishmaniasis (CL) is a neglected disease widely distributed in Maranhão, Brazil and presents a significant public health problem. However, its transmission dynamics and determining factors are not clearly understood. In this context, geospatial technologies help interpret the process. This study, then, characterized the space-time dynamics and the influence of social vulnerability on CL in an endemic area in Northeast Brazil. This is an ecological study about new cases of CL in Maranhão, from 2007 to 2020, obtained directly from the Notifiable Diseases Information System. The incidence rate was smoothed using a spatial empirical Bayesian method. Subsequently, global and local Moran statistics and their association with social vulnerability indicators were determined. Disease distribution was not random but grouped in space and time. All Social Vulnerability Index domains were positively correlated with the CL incidence. A likely cluster was detected in western Maranhão (P < 0.001), which encompassed 18 municipalities, from January 2007 to December 2013, with a high relative risk (5.06). The research findings suggest that planning public health actions and allocating resources should be prioritized in these areas to help effectively reduce the incidence of the disease.

Bridging the Gaps: Investigating the Complex Impact of the COVID-19 Pandemic on Tuberculosis Records in Brazil.

This study aimed to analyze the temporal evolution, spatial distribution, and impact of the COVID-19 pandemic on tuberculosis records in a northeastern state of Brazil. This is an ecological study involving all diagnoses of Tuberculosis (TB) in residents of the state of Pernambuco/Brazil. Data were extracted from the National System of Notifiable Diseases. A pre-pandemic COVID-19 temporal analysis (2001-2019), a spatial analysis before (2015-2019) and during the first two pandemic years (2020-2021), and the impact of the COVID-19 pandemic on cases of TB diagnoses in Pernambuco in the years 2020 and 2021 were performed. Inflection point regression models, Global and Local Moran's statistics, and spatial scan statistics were used. In the period from 2001 to 2019, 91,225 cases of TB were registered in Pernambuco (48.40/100,000 inhabitants), with a tendency of growth starting in 2007 (0.7% per year; p = 0.005). In the pre-pandemic period (2015-2019), 10.8% (n = 20) of Pernambuco municipalities had TB incidence rates below 10/100,000. In 2020, this percentage reached 27.0% (n = 50) and in 2021 it was 17.8% (n = 33). Risk clusters were identified in the eastern region of the state, with five clusters in the pre-pandemic period and in 2021 and six in 2020. In the first year of the pandemic, an 8.5% reduction in the number of new TB cases was observed. In 2021, the state showed a slight increase (1.1%) in the number of new TB cases. The data indicate that the COVID-19 pandemic may have caused a reduction in the number of new TB case reports in the state of Pernambuco, Brazil.

An FCM-Based Image De-Noising with Spatial Statistics Pilot Study

Image de-noising is an important scheme that makes an image visually prominent and obtains enough useful information to produce a clear image. Many applications have been developed for effective noise suppression that produce good image quality. This study assumed that a residual image consisted of noise with edges produced by subtracting the original image with a low-pass-filter-smoothed image. The Moran statistics were then used to measure the variation in spatial information in residual images and we then used this information as feature data input into the Fuzzy C-means (FCM) algorithm. Three clusters were pre-assumed for FCM in this work: they were heavy, medium, and less noisy areas. The rates for each position partially belonged to each cluster determined using an FCM membership function. Each pixel in a noisy image was assumed in de-noising processing as a linear combination of the product of three de-noised images with membership functions in the same position. Average filters with different windows and a Gaussian filter were a priori applied to this noisy image to create three de-noised versions. The results showed that this scheme worked better than the non-adaptive smoothing. This scheme‘s performance was evaluated and compared to the bilateral filter and non-local means (NLM) using the peak signal to noise ratio (PSNR) and structure similarity index measure (SSIM). The developed scheme is a pilot study. Further future studies are needed on the optimized number of clusters and smoother versions used in linear combination.

Spatial analysis of vaccine coverage in children under the age of 1 year by mesoregions in Paraíba a northeastern Brazilian state.

Immunization is one of the most effective measures in public health, and it is responsible for the reduction of vaccine-preventable diseases. In the present study, vaccine coverage (VC) and the spatial dynamics of homogeneity of VC (HVC) were compared and analyzed in the terms of the immunobiologicals administered to children aged < 1 year in a state in Paraíba, Brazil. This is a mixed ecological study that used public-domain secondary data from the years 2016 and 2017 from the Information System of the Brazilian National Immunization Program (SI-PNI) and the Brazilian National Information System of Live Births (SINASC). VC rates were calculated by dividing the number of administered doses by the number of live births. Then, VC was classified into four categories. The Municipal HVC was considered adequate when the overall VC exceeds 75%. The study included a descriptive analysis and a spatial autocorrelation analysis for HVC using global and local Moran's statistics. The stratified VC analysis revealed a significant number of municipalities in each of the state's mesoregions with low or very low VC rates for all immunobiologicals, with the Mata Paraibana mesoregion having the worst percentages in both years studied. The spatial analysis of HVC revealed several clusters of inadequate homogeneity, with Mata Paraibana being the worst mesoregion in 2016. The analysis of spatial dynamics and spatial statistics techniques allows the precise identification of vulnerable areas, "vaccination pockets," making it possible to develop plans aimed at meeting the targets of the PNI.

Spatio-temporal analysis of malaria incidence and its risk factors in North Namibia

BackgroundMillions of dollars have been spent in fighting malaria in Namibia. However, malaria remains a major public health concern in Namibia, mostly in Kavango West and East, Ohangwena and Zambezi region. The primary goal of this study was to fit a spatio-temporal model that profiles spatial variation in malaria risk areas and investigate possible associations between disease risk and environmental factors at the constituency level in highly risk northern regions of Namibia.MethodsMalaria data, climatic data, and population data were merged and Global spatial autocorrelation statistics (Moran’s I) was used to detect the spatial autocorrelation of malaria cases while malaria occurrence clusters were identified using local Moran statistics. A hierarchical Bayesian CAR model (Besag, York and Mollie’s model “BYM”) known to be the best model for modelling the spatial and temporal effects was then fitted to examine climatic factors that might explain spatial/temporal variation of malaria infection in Namibia.ResultsAverage rainfall received on an annual basis and maximum temperature were found to have a significant spatial and temporal variation on malaria infection. Every mm increase in annual rainfall in a specific constituency in each year increases annual mean malaria cases by 0.6%, same to average maximum temperature. The posterior means of the time main effect (year t) showed a visible slightly increase in global trend from 2018 to 2020.ConclusionThe study discovered that the spatial temporal model with both random and fixed effects best fit the model, which demonstrated a strong spatial and temporal heterogeneity distribution of malaria cases (spatial pattern) with high risk in most of the Kavango West and East outskirt constituencies, posterior relative risk (RR: 1.57 to 1.78).

Investments in small retention as a factor influencing land-use changes. A case study of Poland

Building permit decisions are one of the most important elements of the investment process in Poland. It should be noted that water reservoirs influence the diversification of landscapes by increasing their attractiveness in both urban and rural areas. The article aimed to verify the relationship between the changes in land-use development and investments related to small retention. Another goal was classifying objects for which building permits have been obtained and registered. Changes in land-use development associated with the introduction of ponds, which blend in with the landscape, are desirable from the perspective of retaining water resources in urban and rural ecosystems. The research methodology was based on spatial data and included statistical analyses in three regions: Mazowieckie, Lodzkie and Swietokrzyskie. Studies carried out in these regions showed a spatial correlation associated with investments in small retention. The research used methods of the global I Moran statistic and local Moran statistics. The data used in the study came from the Register of Applications, Decisions and Notifications, made available by the Main Office of Construction Site. The research indicates clusters of investments in small retention in analysed regions. The majority of investors are residents who invest in earth ponds. The study shows that investment in small retention is connected with ecosystem services.

Integration of Moran's I, geographically weighted regression (GWR), and ordinary least square (OLS) models in spatiotemporal modeling of COVID-19 outbreak in Qom and Mazandaran Provinces, Iran.

Globally, the COVID-19 pandemic is a top-level public health concern. This paper attempts to identify the COVID-19 pandemic in Qom and Mazandaran provinces, Iran using spatial analysis approaches. This study was based on secondary data of confirmed cases and deaths from February 3, 2020, to late October 2021, in two Qom and Mazandaran provinces from hospitals and the website of the National Institute of Health. In this paper, three geographical models in ArcGIS 10.8.1 were utilized to analyze and evaluate COVID-19, including geographic weight regression (GWR), ordinary least squares (OLS), and spatial autocorrelation (Moran I). The results from this study indicate that the rate of scattering of confirmed cases for Qom province for the period was 44.25%, while the rate of dispersal of the deaths was 4.34%. Based on the GWR and OLS model, Moran's statistics demonstrated that confirmed cases, deaths, and recovered followed a clustering pattern during the study period. Moran's Z-score for all three indicators of confirmed cases, deaths, and recovered was confirmed to be greater than 2.5 (95% confidence level) for both GWR and OLS models. The spatial distribution of indicators of confirmed cases, deaths, and recovered based on the GWR model has been more scattered in the northwestern and southwestern cities of Qom province. Whereas the spatial distribution of the recoveries of the COVID-19 pandemic in Qom province was 61.7%, the central regions of this province had the highest spread of recoveries. The spatial spread of the COVID-19 pandemic from February 3, 2020, to October 2021 in Mazandaran province was 35.57%, of which 2.61% died, according to information published by the COVID-19 pandemic headquarters. Most confirmed cases and deaths are scattered in the north of this province. The ordinary least squares model results showed that the spatial dispersion of recovered people from the COVID-19 pandemic is more significant in the central and southern regions of Mazandaran province. The Z-score for the deaths Index is more significant than 14.314. The results obtained from this study and the information published by the National Headquarters for the fight against the COVID-19 pandemic showed that tourism and pilgrimages are possible factors for the spatial distribution of the COVID-19 pandemic in Qom and Mazandaran provinces. The spatial information obtained from these modeling approaches could provide general insights to authorities and researchers for further targeted investigations and policies in similar circumcises.

A framework to measure transit-oriented development around transit nodes: Case study of a mass rapid transit system in Dhaka, Bangladesh.

Transit-oriented development (TOD) is a tool that aids in achieving sustainable urban development. It promotes economic, environmental, and social sustainability by integrating land use and transportation planning. Many researchers have investigated mass rapid transit (MRT) station regions for TOD in developed cities. However, in a developing city such as Dhaka, measuring node-based TOD (TOD index) during MRT construction has been disregarded in planning future land use. Furthermore, no prior research on quantitative TOD measurement in Dhaka exists. As a result, we developed a framework for both quantitative and spatial node-based TOD measurement based on the four Ds (density, diversity, destination accessibility, and design) of the TOD concept. With 17 stations under construction, MRT 6 was selected as our study area. The TOD index was measured by nine indicators based on the four criteria (4Ds), spatially in the geographic information system (GIS). After calculating the indicators, the TOD index for each station's 800m buffer was estimated using the spatial multi-criteria analysis (SMCA). A sensitivity analysis of four TOD scenarios was performed to check the model's robustness. Additionally, a heatmap of the TOD index for MRT 6 was created for informed planning and policymaking. Furthermore, statistically significant hotspots (both Getis Org Gi* and Anselen Local Moran Statistics) and hotspot clusters were identified. Finally, we illustrate the station-based ranking based on the maximum TOD score. In addition, a detailed spider-web of nine indicators for 17 stations depicts sustainable TOD planning. However, regarding density and diversity, sustainable development and (re)development policies should be implemented not only for MRT 6 but for all Dhaka's TOD regions.

Vaccination coverage of triple viral and poliomyelitis in Brazil, 2011-2021: temporal trend and spatial dependency.

To analyze the coverage of MMR and polio vaccines, the temporal trend and spatial dependence, in children up to one year of age in Brazil, between 2011 and 2021. Ecological study with secondary data on vaccination coverage rates, made available by the National Immunization Program Information System. Trend analysis was carried out using the joinpoint method, according to geographic regions, estimating the annual percentage change (APC) and its respective confidence interval (95%CI). Choropleth maps of distribution by health region were constructed and, subsequently, the spatial dependence was verified using Moran's statistics. Between 2011 and 2021, vaccination coverage declined in Brazil, both for MMR (APC: -6.4%; 95%CI -9.0; -3.8) and for poliomyelitis (APC: -4. 5%; 95%CI -5.5; -3.6). There was a decline in coverage of both vaccines in all geographic regions over the years of the study, except in the South and Midwest for the MMR vaccine. Since 2015, few health regions in the country have achieved adequate vaccination coverage (≥95.0% to <120.0%). The North and Northeast health regions showed low-low clusters in the univariate analysis for both immunobiological. It is urgent to consider studies like this one for the planning of more effective strategies for immunizing children, especially in areas with higher falls. In this way, barriers to access to immunization can be broken, given Brazil's heterogeneity, and access to reliable information that increases confidence in vaccine efficacy can be expanded.

Spatio-temporal analysis of the COVID-19 pandemic in Iran

Globally, the COVID-19 pandemic is a top-level public health concern. This paper is an attempt to identify and COVID-19 pandemic in Iran using spatial analysis approaches. This study was based on secondary data of confirmed cases, deaths, recoveries, number of hospitals, hospital beds and population from March 2, 2019 to the end of November 2021 in 31 provinces of Iran from hospitals and the website of the National Institute of Health. In this paper, three geographical models in ArcGIS10.3 were utilized to analyze and evaluate COVID-19, including Geographic Weight Regression (GWR), Getis-OrdGi* (G-i-star) statistics (hot and cold spot), and Moran autocorrelation spatial analysis. Moran statistics, based on the GWR model, demonstrated that deaths and recoveries followed a clustering pattern for the confirmed cases index during the study period. The Moran Z-score for all three indicators confirmed cases, deaths, and recoveries, which was greater than 2.5 (95% confidence level). The Getis-OrdGi* (G-I-Star) (hot and cold spot) data revealed a wide range of levels for six variables (confirmed cases, deaths, recoveries, population, hospital beds, and hospital) across Iran's provinces. The overall number of deaths exceeded the population and the number of hospitals in the central and southern regions, including the provinces of Qom, Alborz, Tehran, Markazi, Isfahan, Razavi Khorasan, East Azerbaijan, Fars, and Yazd, which had the largest number and The Z-score for the deaths Index is greater than 14.314. The results of this research can pave the way for future studies.

Spatial epidemiology of bacterial meningitis in the Upper West Region of Ghana: Analysis of disease surveillance data 2018-2020.

Spatial epidemiology of bacterial meningitis in the Upper West Region of Ghana: Analysis of disease surveillance data 2018-2020.

Spatial scan statistics and identification of high-risk areas for COVID-19 in the state of Bahia, Brazil

Introduction: The number of COVID-19 cases in Brazil has increased substantially in all regions of the country. The North and Northeastern region of Brazil has been severely affected by COVID-19, with high mortality rates. The first case of COVID-19 in northeastern Brazil was confirmed in the state of Bahia. The state is also the largest in the region in terms of area and population size. The study aimed to analyze the spatial distribution of incidence and mortality rates due to COVID-19 in the state of Bahia, Brazil.&#x0D; Methods: An observational study was carried out with COVID-19 data accumulated on May 16, 2020. Global and local Moran statistics and spatial scan statistics were used.&#x0D; Results: Between March 6, 2020 and May 16, 2020 there were 8,288 confirmed cases and 284 deaths (1.9/100,000) of COVID-19 in Bahia state. Over that time frame the incidence risk of disease was 55,7 (95%CI 54,1-58,2) per 100,000 individuals at risk. The mortality rate is 1.9 per 100,000 (95%CI 1,81-1.98). The spatial scan statistics identified five high-risk clusters for incidence rate, among which cluster 1 stood out, with 86 municipalities. This cluster accounts for 85.24% of all cases registered in the state, and it has a RR of 10.6.&#x0D; Conclusion: The study showed heterogeneous distribution of occurrence and death in Bahia, with areas of higher relative risk in the east and south. Actions to fight the disease in the state must take into account the risk areas identified in the study.&#x0D; Keywords: coronavirus; COVID-19; epidemiology; SARS-CoV-2; spatial analysis.

Temporal trend, space risk and factors associated with the occurrence of dengue in northeast Brazil, 2009-2018.

Dengue is an acute viral disease of major relevance and impact on public health, causing major epidemics around the world, especially in tropical regions. Here we aimed to analyse the temporal trend and spatial risk, as well as social vulnerability factors, associated with the occurrence of dengue in the state of Bahia, Brazil between 2009 and 2018. This is an ecological study carried out with all suspected cases of dengue in Bahia between 2009 and 2018. The data were obtained from the National Notifiable Diseases Information System, available on the website of the Health Department of the State of Bahia, and from the Brazilian Institute of Geography and Statistics. We used the Joinpoint regression model, local empirical Bayesian model for smoothing, global and local Moran statistics and spatial scanning statistics. The relationship between the dengue incidence rate and social determinants was tested using Moran's bivariate correlation. During the study period, 451847 probable dengue cases were registered in Bahia. A declining trend was observed in 39.28% (n=11) of the state's health regions and 60.71% (n=17) showed a stationary tendency. The spatiotemporal scanning statistic showed nine clusters of dengue occurrence. The largest cluster had a radius of 342.14km, consisting of 160 municipalities, 120094 cases (710.20 cases/100000 inhabitants) and a relative risk of 2.80. In the multivariate regression model, 11 variables showed a significant association: Social Vulnerability Index (SVI), Municipal Human Development Index (MHDI), SVI urban infrastructure, SVI human capital, MHDI longevity, MHDI education, proportion of people living in households with per capita income less than half the minimum wage (in 2010) and who spend more than 1h commuting, proportion of mothers who are heads of household who did not complete elementary school and with children <15y of age, activity rate of persons ages 10-14y and per capita income. In the analysis of the spatial distribution, areas of risk of disease transmission throughout the state were identified. These results can provide subsidies for the strategic planning of actions, as well as for the implementation of programs and/or public policies in order to control the incidence of dengue in the population.

Is social cohesion decisive for energy cooperatives existence? A quantitative analysis

Energy Cooperatives (ECoops), the most prominent example for Energy Communities, are attributed great importance for the energy transition both through the engagement of energy end-consumers and the increase of local renewable energy sources. We conducted an exploratory (spatial) data analysis to study which indicators of the European Social Progress Index and Quality of Life Index co-occure with the presence of ECoops. Results show that these indexes and most of their sub-components present values significantly better at the regions where the ECoops are located compared to all EU regions. While correlation and regression coefficients between the number of ECoops per region and the indexes are relatively small, the individual indicator “Life-long learning” reaches the highest correlation and explanatory values. Additionally, Global moran statistics show that the concentration of ECoops and their relation to the indexes in space are rather random but a local analysis shows clusters emerging throughout the continent.

Sustainable Development Programming of Airports by Identification of Non-Efficient Units

This article concerns the identification of inefficient airports and the exploration of spatial autocorrelation for programming sustainable development. The first research question was: do domestic airports cooperate by shifting passenger service and traffic to the geographically closest airport to respect the idea of sustainable development (in view of the rationalization of energy consumption)? The second question was: do they excessively compete for passengers and the carriers serving them? The aim was to identify ineffective units (taking into account energy consumption, airplane traffic, and passenger movement) and to evaluate the spatial autocorrelation between national airports, which shows whether airports cooperate or compete with each other. The study was conducted on 12 airports. An innovative extension of the data envelopment analysis method using methods in the field of spatial econometrics (including two-dimensional Moran I statistics and local LISA statistics) and artificial intelligence was applied. It was verified that ineffective airports have a non-rationalized structure of inputs to outputs. Based on the map-graph of connections, airports have been identified to which part of airplane traffic service can be transferred. Based on Moran statistics and local LISA statistics, it was confirmed that airports compete with each other. There was a strong polarization of efficient airports.

Time trend, social vulnerability, and identification of risk areas for tuberculosis in Brazil: An ecological study.

Tuberculosis is one of the ten leading causes of death and the leading infectious cause worldwide. The disease represents a challenge to health systems around the world. In 2018, it is estimated that 10 million people were affected by tuberculosis, and approximately 1.5 million people died due to the disease worldwide, including 251,000 patients coinfected with HIV. In Brazil, the disease caused 4,490 deaths, with rate of 2.2 deaths per 100,000 inhabitants. The objective of this study was to analyze the time behavior, spatial, spatial-temporal distribution, and the effects of social vulnerability on the incidence of TB in Brazil during the period from 2001 to 2017. A spatial-temporal ecological study was conducted, including all new cases of tuberculosis registered in Brazil during the period from 2001 to 2017. The following variables were analyzed: incidence rate of tuberculosis, the Social Vulnerability Index, its subindices, its 16 indicators, and an additional 14 variables available on the Atlas of Social Vulnerability. The statistical treatment of the data consisted of the following three stages: a) time trend analysis with a joinpoint regression model; b) spatial analysis and identification of risk areas based on smoothing of the incidence rate by local empirical Bayesian model, application of global and local Moran statistics, and, finally, spatial-temporal scan statistics; and c) analysis of association between the incidence rate and the indicators of social vulnerability. Brazil reduced the incidence of tuberculosis from 42.8 per 100,000 to 35.2 per 100,000 between 2001 and 2017. Only the state of Minas Gerais showed an increasing trend, whereas nine other states showed a stationary trend. A total of 326 Brazilian municipalities were classified as high priority, and 22 high-risk spatial-temporal clusters were identified. The overall Social Vulnerability Index and the subindices of Human Capital and Income and Work were associated with the incidence of tuberculosis. It was also observed that the incidence rates were greater in municipalities with greater social vulnerability. This study identified clusters with high risk of TB in Brazil. A significant association was observed between the incidence rate of TB and the indices of social vulnerability.