Space-time cluster analysis and maximum entropy modeling of Peste des petits ruminants (PPR) in Türkiye.

Peste des petits ruminants (PPR) is an economically important highly serious transboundary disease that mainly occurs in small ruminants such as sheep and goats. The aim of this study was to identify the probability of risk and and space-time clusters of Peste des Petits Ruminants (PPR) in Türkiye. The occurrence of PPR in Türkiye from 2017 to 2019 was investigated in this study using spatial analysis based on geographic information system (GIS). Between these dates, it was determined that 337 outbreaks and 18,467 cases. The highest number of outbreaks were detected in the Central Anatolia region. It was determined that PPR is seen more intensely in sheep compared to goats in Türkiye. In this study, 34 environmental variables (19 bioclimatic, 12 precipitation, altitude and small livestock density variables) were used to explore the environmental influences on PPR outbreak by maximum entropy modeling (Maxent). The clusters of PPR in Türkiye were identified using the retrospective space-time scan data that were computed using the space-time permutation model. A PPR prediction model was created using data on PPR outbreaks combination with environmental variables. Nineteen significant (p < 0.001) space-time clusters were determined. It was discovered that the variables altitude, sheep density, precipitation in june, and average temperature in the warmest season made important contributions to the model and the PPR outbreak may be strongly related with these variables. In this study, PPR in Türkiye has been characterized significantly spatio-temporal and enviromental factors. In this context, the disease pattern and obtained these findings will contribute to policymakers in the prevention and control of the disease.

Geographical Ambulatory Endovascular Revascularisation Disparities in France From 2015 to 2019.

OBJECTIVE: Despite efficiency and safety evidence, ambulatory endovascular revascularisation for lower extremity arterial disease (LEAD) accounted for only 5% of interventions in France in 2016. Such a low rate suggests temporal and geographical space disparities. The aim of this study was to describe the space-time development of ambulatory endovascular revascularisation for LEAD in France and to investigate the contributions of healthcare services and population characteristics as potential determinants. METHODS: A retrospective study of discharge data from French hospitals that performed endovascular procedures for LEAD between 2015 and 2019 was conducted. Space-time analyses with Moran's Index, zero inflated Poisson regression, and clustering approaches were applied. Spatial clusters were compared on the basis of healthcare services and population characteristics (including poverty and single man household as proxies of social isolation). RESULTS: Between 2015 and 2019, the number of ambulatory interventions tripled (1 104 vs.3 130). Of the 86 French departments, the proportion with >5% of ambulatory interventions increased from 10.7% to 28.7% over the study period. In 2019, ambulatory activity in French departments ranged from 0% to 39%. This change was accompanied by a northwest to northeast spatial trend. The clusters of 27 departments with substantial ambulatory activity differed from the others notably by the mortality rate of lower limb arterial thromboembolic diseases in males (OR 3.15, 95% CI 1.2-8.1), the proportion of single man households of age ≥75 (OR 0.37, 95% CI 0.2-0.8), and the poverty rate of people aged 50-59 years (OR 0.69, 95% CI 0.5-0.9). CONCLUSIONS: The development of ambulatory interventions for LEAD in France is encouraging but heterogeneous. Some determinants of this evolution are clearly population based, with a positive impact of needs to take care of the burden of LEAD but negative effects of social isolation and poverty. Research should be conducted to overcome some patient constraints such as isolation.

A new combination rule for Spatial Decision Support Systems for epidemiology.

BACKGROUND: Decision making in the health area usually involves several factors, options and data. In addition, it should take into account technological, social and spatial aspects, among others. Decision making methodologies need to address this set of information , and there is a small group of them with focus on epidemiological purposes, in particular Spatial Decision Support Systems (SDSS). METHODS: Makes uses a Multiple Criteria Decision Making (MCDM) method as a combining rule of results from a set of SDSS, where each one of them analyzes specific aspects of a complex problem. Specifically, each geo-object of the geographic region is processed, according to its own spatial information, by an SDSS using spatial and non-spatial data, inferential statistics and spatial and spatio-temporal analysis, which are then grouped together by a fuzzy rule-based system that will produce a georeferenced map. This means that, each SDSS provides an initial evaluation for each variable of the problem. The results are combined by the weighted linear combination (WLC) as a criterion in a MCDM problem, producing a final decision map about the priority levels for fight against a disease. In fact, the WLC works as a combining rule for those initial evaluations in a weighted manner, more than a MCDM, i.e., it combines those initial evaluations in order to build the final decision map. RESULTS: An example of using this new approach with real epidemiological data of tuberculosis in a Brazilian municipality is provided. As a result, the new approach provides a final map with four priority levels: "non-priority", "non-priority tendency", "priority tendency" and "priority", for the fight against diseases. CONCLUSION: The new approach may help public managers in the planning and direction of health actions, in the reorganization of public services, especially with regard to their levels of priorities.

[Temporal and spatial distribution of air pollution in Shenzhen City during 2014-2016].

OBJECTIVE: To investigate spatial-temporal distribution characteristics of air quality indexes( AQI) in Shenzhen City and provide scientific basis for control of air pollution. METHODS: The monitoring data of AQI collected at the 19 monitoring posts in Shenzhen City from 2014 to 2016 were pooled and analyzed, and daily, seasonal and annual means of AQI at each monitoring post were forked out. The theory of spatial autocorrelation analysis was adopted in analyzing strength of the spatial autocorrelation and spatial clustering patterns of AQI. RESULTS: The median of AQI of Shenzhen City was50. 0, and the air quality level of Shenzhen City was level 2. PM_(2. 5) was the primary air pollutant in 2014, and O_3 was the primary air pollutant in 2015 and 2016 respectively. From the time distribution, the air quality in Shenzhen City was the worst in the winter but the best in the summer, and the air quality in night was worse than the day. Spatial autocorrelation analysis showed that there was positive spatial autocorrelation of AQI in Shenzhen. The global moran's I of AQI was 0. 167( Z = 2. 000, P < 0. 05), and the greary coefficient was 0. 801( Z = 1. 711, P < 0. 05). The local moran's I of AQI was 1. 027( P < 0. 05) in Guangming New District. The moran scatter plot of AQI in Guangming New District was in the first quadrant, and appeared to be in a high-high clustering pattern. The moran scatter plot of AQI in Yantian District was in the third quadrant, and appeared to be in a low-low clustering pattern. CONCLUSION: The air quality of Shenzhen City was better. PM_(2. 5) and O_3 were the primary air pollutant. It is necessary to emphasize and strengthen the prevention and control of air pollution in Winter, especially in several administrative districts of the northwest of Shenzhen City.

Geoepidemiology and space-time analysis of Primary biliary cirrhosis in Crete, Greece.

BACKGROUND & AIMS: The prevalence of Primary biliary cirrhosis varies in different geographical areas. This might reflect genetic or environmental risk factors. We aimed to define Primary biliary cirrhosis prevalence and incidence, describe patient's spatial distribution, generate prediction maps and detect any possible routing pattern of time-spatial appearance of the disease in Crete, Greece. METHODS: From 1990-2010, 245 Primary biliary cirrhosis patients diagnosed and followed up at the Gastroenterology Department of the University Hospital and the District Hospitals of the island, were contacted and 222 were included in the time-spatial analysis. To map their spatial distribution per 5-year periods, geospatial models were applied in Gis-ArcMap 9.3 software. Kriging Interpolation methods were used to generate prediction maps for the disease in Crete. Areas of high and low probability of disease occurrence were estimated through multicriteria modelling. The disease route was defined by Gis-ArcMap's toolbox. RESULTS: Prevalence was found to be 365 cases per million, with a mean incidence of 20.88 (range 3.79-35.99). Prediction map estimates from 1.22 to 11 patients per 50 km2 all over Crete. Areas of high risk of disease occurrence are located in the Eastern part, while low risk in the Western part of the island. DISCUSSION: Prevalence and incidence of Primary biliary cirrhosis in Crete are among the higher published in Europe. Given the homogeneous and stable study population and the geopolitics of the island, the heterogeneity in the time-spatial distribution and the route of disease appearance strongly suggest a role for environmental causative agents.

Cross-border surveillance of sheep and goat rabies in Brazil.

Since Brazil has the largest territory in South America and borders 10 other countries, rabies control is strategic to prevent cross-border spread. However, prevention and control of rabies in small ruminants is neglected. The present study evaluated the spatiotemporal distribution and temporal trends of rabies in small ruminants in Brazil between 2005 and 2023. Official data on rabies case notifications and the population density of goats and sheep from the Brazilian states were used. Descriptive epidemiology and temporal analyses of high-risk clusters and trends were conducted. Rabies cases were reported in all states, except for the Federal District, Roraima, Amapá, Amazonas, and Rondônia. In sheep, 174 cases were reported, with an emphasis on Paraná (25.29 %), being highest. There were 64 cases in goats, with a highlight on Bahia (37.50 %) and Maranhão (18.75 %). However, Espírito Santo presented the highest incidence risk (IR) for goats and sheep. The highest peaks in IR occurred in 2005, 2006 and 2013. A temporal trend of decreasing goat cases was observed in northeastern Brazil from 2005 to 2023. In sheep, the Northeast region showed a downward trend in rabies cases. Three high-risk clusters were identified: the primary cluster for goats occurred in 2006, and for sheep, between 2005 and 2013. Rabies in small ruminants occurs across all Brazilian regions, with high-risk areas in the Northeast, Southeast, and South, as well as a risk of cross-border transmission. These findings support animal health authorities in strengthening rabies control for small ruminants and reducing the risk of transboundary spread.

Effects of the Sacramento Neighborhood Alcohol Prevention Project on rates of child abuse and neglect 7 years post-implementation (1999-2010).

INTRODUCTION: Evaluations of alcohol environmental prevention efforts examine short-term effects of these interventions on alcohol-related problems. We examine whether the effects of the Sacramento Neighborhood Alcohol Prevention Project (SNAPP), an alcohol environmental intervention aimed to reduce alcohol-related problems in two neighbourhoods, on child abuse and neglect remained 7 years post-implementation. METHODS: SNAPP used a quasi-experimental non-equivalent control group design, where intervention activities occurred in the South area, followed by those in the North area 2 years later. Our sample size is 3912 space-time units (326 census block groups × 12 years [1999-2010]). Outcomes were measured at the household level and included: (i) all foster care entries total; and (ii) the subset of foster care entries that were alcohol related. Data were analysed using Bayesian conditionally autoregressive space-time models. RESULTS: We find that the decreases in total (relative rate [RR] = 0.882, 95% credible interval [CrI] 0.795, 0.980) and alcohol-related (RR = 0.888, 95% CrI 0.791, 0.997) foster care entries remain in the North intervention area although the magnitude of those changes are smaller than immediately post-intervention. Increases found in alcohol-related foster care entries in the South area immediately post-intervention were not significant 7 years later (RR = 1.128, 95% CrI 0.975, 1.307). DISCUSSION AND CONCLUSIONS: Reductions in child abuse and neglect due to an alcohol environmental intervention can be maintained. Environmental interventions that provide community-level primary prevention strategies could be more easily sustained and more cost effective than individual-level interventions, although more research is needed to identify why interventions may be successful in specific contexts and not others.

Enhanced cluster detection and noise reduction for geospatial time series data of COVID-19.

Spatial-temporal analysis of the COVID-19 cases is critical to find its transmitting behaviour and to detect the possible emerging clusters. Poisson's prospective space-time analysis has been successfully implemented for cluster detection of geospatial time series data. However, its accuracy, number of clusters, and processing time are still a major problem for detecting small-sized clusters. The aim of this research is to improve the accuracy of cluster detection of COVID-19 at the county level in the U.S.A. by detecting small-sized clusters and reducing the noisy data. The proposed system consists of the Poisson prospective space-time analysis along with Enhanced cluster detection and noise reduction algorithm (ECDeNR) to improve the number of clusters and decrease the processing time. The results of accuracy, processing time, number of clusters, and relative risk are obtained by using different COVID-19 datasets in SaTScan. The proposed system increases the average number of clusters by 7 and the average relative risk by 9.19. Also, it provides a cluster detection accuracy of 91.35% against the current accuracy of 83.32%. It also gives a processing time of 5.69 minutes against the current processing time of 7.36 minutes on average. The proposed system focuses on improving the accuracy, number of clusters, and relative risk and reducing the processing time of the cluster detection by using ECDeNR algorithm. This study solves the issues of detecting the small-sized clusters at the early stage and enhances the overall cluster detection accuracy while decreasing the processing time.

Assessing the effects of the Sacramento Neighborhood Alcohol Prevention Project (SNAPP) on child abuse and neglect.

BACKGROUND: The role of alcohol use in the etiology of abusive and neglectful parenting is significant. We examined how the Sacramento Neighborhood Alcohol Prevention Project (SNAPP) may have reduced rates of substantiated child maltreatment, entries into foster care, and entries into foster care where alcohol use was a factor. PARTICIPANTS AND SETTING: The study sample is 326 Census block groups: 21 and 16 in the South and North intervention areas, respectively, and 289 in the At-Large comparison area in Sacramento, California. METHODS: SNAPP used a quasi-experimental design to reduce alcohol supply and alcohol-related problems among 15-29 year olds in two economically, racially, and ethnically diverse neighborhoods. The dependent variables are substantiated child abuse and neglect; total foster care entries; and alcohol-related foster care entries. RESULTS: Substantiated child abuse and neglect was inconclusive for both intervention areas. In the North, total (RR = 0.822, 95 % CI [0.721, 0.933]) and alcohol-related (RR = 0.760, 95 % CI [0.634, 0.914]) foster care entries decreased by 17.8 % and 24.0 %, respectively. Intervention effects in the South were not well-supported for foster care entries (RR = 1.118, 95 % CI [0.988, 1.258]), but increased alcohol-related foster care entries (RR = 1.264, 95 % CI [1.075, 1.484]). CONCLUSIONS: Environmental intervention strategies may be effective at reducing child abuse and neglect. However, given the mixed findings from our work, we need to identify under what conditions these interventions work best and whether some components of these strategies (e.g. awareness vs. enforcement of underage sales) differentially affect child abuse and neglect.

Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada.

Introduction: This study aimed to produce community-level geo-spatial mapping of confirmed COVID-19 cases in Ontario Canada in near real-time to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals. Methods: COVID-19 cases and locations were curated for geostatistical analyses from March 2020 through June 2021, corresponding to the first, second, and third waves of infections. Daily cases were aggregated according to designated forward sortation area (FSA), and postal codes (PC) in municipal regions Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, and Windsor/Essex county. Hotspots were identified with area-to-area tests including Getis-Ord Gi*, Global Moran's I spatial autocorrelation, and Local Moran's I asymmetric clustering and outlier analyses. Case counts were also interpolated across geographic regions by Empirical Bayesian Kriging, which localizes high concentrations of COVID-19 positive tests, independent of FSA or PC boundaries. The Geostatistical Disease Epidemiology Toolbox, which is freely-available software, automates the identification of these regions and produces digital maps for public health professionals to assist in pandemic management of contact tracing and distribution of other resources.  Results: This study provided indicators in real-time of likely, community-level disease transmission through innovative geospatial analyses of COVID-19 incidence data. Municipal and provincial results were validated by comparisons with known outbreaks at long-term care and other high density residences and on farms. PC-level analyses revealed hotspots at higher geospatial resolution than public reports of FSAs, and often sooner. Results of different tests and kriging were compared to determine consistency among hotspot assignments. Concurrent or consecutive hotspots in close proximity suggested potential community transmission of COVID-19 from cluster and outlier analysis of neighboring PCs and by kriging. Results were also stratified by population based-categories (sex, age, and presence/absence of comorbidities). Conclusions: Earlier recognition of hotspots could reduce public health burdens of COVID-19 and expedite contact tracing.

Temporal and Spatial Evolution of China's Human Development Index and Its Determinants: An Extended Study Based on Five New Development Concepts.

This paper innovatively expands China's human development index under the background of development concept in the new era, constructs the index system of China's human development index (CHDI) with four core dimensions of "life span, education, income and sustainable development" and measures the human development level of 31 provinces and the whole country from 1990 to 2017. Using the method of exploratory spatial data analysis (ESDA), this paper studies the spatiotemporal evolution characteristics of CHDI in 31 provinces from 1990 to 2017 and discusses the mechanism of CHDI in China. The results show that: ① CHDI in eastern China is obviously ahead of other regions and there is a big gap in human development among different regions. CHDI of 31 provinces had positive spatial correlation, showing significant spatial aggregation effect. ② In addition to its own influencing factors, the CHDI of a province is also affected by the CHDI of its "neighboring" provinces. ③ Urbanization rate, per capita financial expenditure on education and the number of health professionals are the three main positive driving factors of China's CHDI and the per capita carbon emission is the main reverse driving factor. This study provides policy suggestions for improving the level of human development in China and realizing regional balanced development.

Dynamics of Classical Swine Fever Spread in Wild Boar in 2018-2019, Japan.

The prolongation of the classic swine fever (CSF) outbreak in Japan in 2018 was highly associated with the persistence and widespread of the CSF virus (CSFV) in the wild boar population. To investigate the dynamics of the CSF outbreak in wild boar, spatiotemporal analyses were performed. The positive rate of CSFV in wild boar fluctuated dramatically from March to June 2019, but finally stabilized at approximately 10%. The Euclidean distance from the initial CSF notified farm to the farthest infected wild boar of the day constantly increased over time since the initial outbreak except in the cases reported from Gunma and Saitama prefectures. The two-month-period prevalence, estimated using integrated nested Laplace approximation, reached >80% in half of the infected areas in March-April 2019. The area affected continued to expand despite the period prevalence decreasing up to October 2019. A large difference in the shapes of standard deviational ellipses and in the location of their centroids when including or excluding cases in Gunma and Saitama prefectures indicates that infections there were unlikely to have been caused simply by wild boar activities, and anthropogenic factors were likely involved. The emergence of concurrent space-time clusters in these areas after July 2019 indicated that CSF outbreaks were scattered by this point in time. The results of this epidemiological analysis help explain the dynamics of the spread of CSF and will aid in the implementation of control measures, including bait vaccination.

A spatio-temporal geodatabase of mortalities due to respiratory tract diseases in Tehran, Iran between 2008 and 2018: a data note.

OBJECTIVES: Respiratory tract diseases (RTDs) are among the top five leading causes of death worldwide. Mortality rates due to respiratory tract diseases (MRRTDs) follow a spatial pattern and this may suggest a potential link between environmental risk factors and MRRTDs. Spatial analysis of RTDs mortality data in an urban setting can provide new knowledge on spatial variation of potential risk factors for RTDs. This will enable health professionals and urban planners to design tailored interventions. We aim to release the datasets of MRRTDs in the city of Tehran, Iran, between 2008 and 2018. DATA DESCRIPTION: The Research data include four datasets; (a) mortality dataset which includes records of deaths and their attributes (age, gender, date of death and district name where death occurred), (b) population data for 22 districts (age groups with 5 years interval and gender by each district). Furthermore, two spatial datasets about the city are introduced; (c) the digital boundaries of districts and (d) urban suburbs of Tehran.

A temporal beta-diversity index to identify sites that have changed in exceptional ways in space-time surveys.

AIM: This paper presents the statistical bases for temporal beta-diversity analysis, a method to study changes in community composition through time from repeated surveys at several sites. Surveys of that type are presently done by ecologists around the world. A temporal beta-diversity Index (TBI) is computed for each site, measuring the change in species composition between the first (T1) and second surveys (T2). TBI indices can be decomposed into losses and gains; they can also be tested for significance, allowing one to identify the sites that have changed in composition in exceptional ways. This method will be of value to identify exceptional sites in space-time surveys carried out to study anthropogenic impacts, including climate change. INNOVATION: The null hypothesis of the TBI test is that a species assemblage is not exceptionally different between T1 and T2, compared to assemblages that could have been observed at this site at T1 and T2 under conditions corresponding to H0. Tests of significance of coefficients in a dissimilarity matrix are usually not possible because the values in the matrix are interrelated. Here, however, the dissimilarity between T1 and T2 for a site is computed with different data from the dissimilarities used for the T1-T2 comparison at other sites. It is thus possible to compute a valid test of significance in that case. In addition, the paper shows how TBI dissimilarities can be decomposed into loss and gain components (of species, or abundances-per-species) and how a B-C plot can be produced from these components, which informs users about the processes of biodiversity losses and gains through time in space-time survey data. MAIN CONCLUSION: Three applications of the method to different ecological communities are presented. This method is applicable worldwide to all types of communities, marine, and terrestrial. R software is available implementing the method.

Epidemiological Characteristics and Space-Time Analysis of the 2015 Dengue Outbreak in the Metropolitan Region of Tainan City, Taiwan.

The metropolitan region of Tainan City in southern Taiwan experienced a dengue outbreak in 2015. This manuscript describes basic epidemiological features of this outbreak and uses spatial and temporal analysis tools to understand the spread of dengue during the outbreak. The analysis found that, independently of gender, dengue incidence rate increased with age, and proportionally affected more males below the age of 40 years but females above the age of 40 years. A spatial scan statistic was applied to detect clusters of disease transmission. The scan statistic found that dengue spread in a north-south diffusion direction, which is across the North, West-Central and South districts of Tainan City. Spatial regression models were used to quantify factors associated with transmission. This analysis indicated that neighborhoods with high proportions of residential area (or low wetland cover) were associated with dengue transmission. However, these association patterns were non-linear. The findings presented here can help Taiwanese public health agencies to understand the fundamental epidemiological characteristics and diffusion patterns of the 2015 dengue outbreak in Tainan City. This type of information is fundamental for policy making to prevent future uncontrolled dengue outbreaks, given that results from this study suggest that control interventions should be emphasized in the North and West-Central districts of Tainan city, in areas with a moderate percentage of residential land cover.

Space-time epidemiology of Crimean-Congo hemorrhagic fever (CCHF) in Iran.

Iran, as an endemic country of Crimean-Congo hemorrhagic fever (CCHF), has been suffering from severe health issues and substantial economic burdens imposed by the disease. We analyzed monthly and yearly spatial and temporal distributions of CCHF to better understand the epidemiology of the disease in Iran. A cross-sectional survey was performed on 1027 recorded cases between 2000 and 2014. Global Moran's I analysis was applied to statistically evaluate the spatial pattern of the disease. Additionally, spatial and space-time scan statistics were used to study the presence of possible spatial and space-time hotspots. Global Moran's I analysis proved that the incidence of the disease is strongly clustered in Iran (p<0.01). Purely spatial scan statistics identified that there were three clusters in the eastern, southern and western parts of the country. Through space-time analysis, we found that the highest incidence of CCHF occurred in the eastern parts of the country between 2006 and 2012. Monthly clusters, which include cities with lower (average) temperatures, had been occurring in relatively short periods. The distribution of CCHF incidence in this country is spatially and temporally clustered. The majority of the clusters emerged during the critical years of 2009 and 2013. Summer is the predominant period for the formation of CCHF clusters.

A space-time analysis of mortality in older people living with HIV/AIDS in the state of São Paulo, Brazil / Uma análise espaço temporal da mortalidade em pessoas idosas que vivem com HIV/AIDS no estado de São Paulo, Brasil

ABSTRACT Objective: To analyze the spatiotemporal distribution of mortality in older people living with the human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) in the state of São Paulo, Brazil. Methods: This is an ecological study with temporal and spatial approaches to analyze mortality from HIV/AIDS in the older adult population in the 2010-2020 period in the state of São Paulo, Brazil. Analysis of temporal trends was performed using the joinpoint regression, and spatial analysis was carried out using the Moran's index and the local empirical Bayesian model. Results: We identified a total of 3,070 deaths from HIV/AIDS among older adults and a mortality rate of 51.71 per 100 thousand inhabitants during the study period. The joinpoint method showed a growing trend for the age groups from 70 to 79 years (annual percent change [APC]=3.45; p=0.01) and ≥80 years (APC=6.60; p=0.006) and stability for the general older adult population (APC=0.99; p=0.226). The spatial distribution of the crude mortality rate was diffuse throughout the state. After smoothing by the Bayesian estimator, we observed greater concentration in the eastern mesoregions. In Moran's analysis, we observed clusters of lower mortality rates in more central regions; and of higher rates in the southern and northern regions of the state. Conclusions: We found a major growing trend in mortality from HIV/AIDS in the age group of older adults over 69 years during the 2010-2020 period. Clusters of high mortality rates were located in regions further to the south and north of the state, where places of greater social inequalities are concentrated.

RESUMO Objetivo: O estudo tem como objetivo analisar a distribuição espaço-temporal da mortalidade em idosos que vivem com HIV/AIDS no estado de São Paulo, Brasil. Métodos: Estudo ecológico com abordagens temporal e espacial para análise da mortalidade por HIV/AIDS em pessoas idosas no período de 2010-2020 no estado de São Paulo, Brasil. A análise das tendências temporais foi realizada por meio da regressão joinpoint e as análises espaciais foram realizadas usando o índice de Moran e o modelo bayesiano empírico local. Resultados: Foram identificados 3.070 óbitos por HIV/AIDS entre pessoas idosas e taxa de mortalidade de 51,71 por 100 mil habitantes no período de estudo. O método joinpoint revelou tendência crescente para as faixas etárias de 70 a 79 anos (variação percentual anual — APC=3,45 p=0,01) e 80 anos ou mais (APC=6,60, p=0,006) e de estabilidade para a população idosa geral (APC=0,99, p=0,226). A distribuição espacial da taxa bruta de mortalidade demonstrou-se difusa em todo o estado. Após suavização pelo estimador bayesiano, observou-se maior concentração nas mesorregiões ao leste. Na análise de Moran, foram observados aglomerados das menores taxas de mortalidade em regiões mais centrais e das altas taxas em regiões mais ao sul e norte do estado. Conclusão: O grupo etário em que ocorreu maior tendência de crescimento da mortalidade por HIV/AIDS durante o período de 2010-2020 foi o de pessoas idosas com mais de 69 anos. Os aglomerados das altas taxas de mortalidade foram localizados em regiões mais ao sul e norte do estado, onde se concentram locais de maiores desigualdades sociais.

[Surveillance data on typhoid fever and paratyphoid fever in 2015, China].

Objective: Through analyzing the surveillance data on typhoid fever and paratyphoid fever in 2015 to understand the related epidemiological features and most possible clustering areas of high incidence. Methods: Individual data was collected from the passive surveillance program and analyzed by descriptive statistic method. Characteristics on seasonal, regional and distribution of the diseases were described. Spatial-temporal clustering characteristics were estimated, under the retrospective space-time method. Results: A total of 8 850 typhoid fever cases were reported from the surveillance system, with incidence rate as 0.65/100 000. The number of paratyphoid fever cases was 2 794, with incidence rate as 0.21/100 000. Both cases of typhoid fever and paratyphoid fever occurred all year round, with high epidemic season from May to October. Most cases involved farmers (39.68%), children (15.89%) and students (12.01%). Children under 5 years showed the highest incidence rate. Retrospective space-time analysis for provinces with high incidence rates would include Yunnan, Guangxi, Guizhou, Hunan and Guangdong, indicating the first and second class clusters were mainly distributed near the bordering adjacent districts and counties among the provinces. Conclusion: In 2015, the prevalence rates of typhoid fever and paratyphoid fever were low, however with regional high prevalence areas. Cross regional transmission existed among provinces with high incidence rates which might be responsible for the clusters to appear in these areas.

Accelerating the discovery of space-time patterns of infectious diseases using parallel computing.

Infectious diseases have complex transmission cycles, and effective public health responses require the ability to monitor outbreaks in a timely manner. Space-time statistics facilitate the discovery of disease dynamics including rate of spread and seasonal cyclic patterns, but are computationally demanding, especially for datasets of increasing size, diversity and availability. High-performance computing reduces the effort required to identify these patterns, however heterogeneity in the data must be accounted for. We develop an adaptive space-time domain decomposition approach for parallel computation of the space-time kernel density. We apply our methodology to individual reported dengue cases from 2010 to 2011 in the city of Cali, Colombia. The parallel implementation reaches significant speedup compared to sequential counterparts. Density values are visualized in an interactive 3D environment, which facilitates the identification and communication of uneven space-time distribution of disease events. Our framework has the potential to enhance the timely monitoring of infectious diseases.

Investigating trends in asthma and COPD through multiple data sources: A small area study.

This paper investigates trends in asthma and COPD by using multiple data sources to help understanding the relationships between disease prevalence, morbidity and mortality. GP drug prescriptions, hospital admissions, and deaths are analysed at clinical commissioning group (CCG) level in England from August 2010 to March 2011. A Bayesian hierarchical model is used for the analysis, which takes into account the complex space and time dependencies of asthma and COPD, while it is also able to detect unusual areas. Main findings show important discrepancies across the different data sources, reflecting the different groups of patients that are represented. In addition, the detection mechanism that is provided by the model, together with inference on the spatial, and temporal variation, provide a better picture of the respiratory health problem.