Suicide has drawn much attention from both the scientific community and the public. Examining the impact of socio-environmental factors on suicide is essential in developing suicide prevention strategies and interventions, because it will provide health authorities with important information for their decision-making. However, previous studies did not examine the impact of socio-environmental factors on suicide using a spatial analysis approach. The purpose of this study was to identify the patterns of suicide and to examine how socio-environmental factors impact on suicide over time and space at the Local Governmental Area (LGA) level in Queensland. The suicide data between 1999 and 2003 were collected from the Australian Bureau of Statistics (ABS). Socio-environmental variables at the LGA level included climate (rainfall, maximum and minimum temperature), Socioeconomic Indexes for Areas (SEIFA) and demographic variables (proportion of Indigenous population, unemployment rate, proportion of population with low income and low education level). Climate data were obtained from Australian Bureau of Meteorology. SEIFA and demographic variables were acquired from ABS. A series of statistical and geographical information system (GIS) approaches were applied in the analysis. This study included two stages. The first stage used average annual data to view the spatial pattern of suicide and to examine the association between socio-environmental factors and suicide over space. The second stage examined the spatiotemporal pattern of suicide and assessed the socio-environmental determinants of suicide, using more detailed seasonal data. In this research, 2,445 suicide cases were included, with 1,957 males (80.0%) and 488 females (20.0%). In the first stage, we examined the spatial pattern and the determinants of suicide using 5-year aggregated data. Spearman correlations were used to assess associations between variables. Then a Poisson regression model was applied in the multivariable analysis, as the occurrence of suicide is a small probability event and this model fitted the data quite well. Suicide mortality varied across LGAs and was associated with a range of socio-environmental factors. The multivariable analysis showed that maximum temperature was significantly and positively associated with male suicide (relative risk [RR] = 1.03, 95% CI: 1.00 to 1.07). Higher proportion of Indigenous population was accompanied with more suicide in male population (male: RR = 1.02, 95% CI: 1.01 to 1.03). There was a positive association between unemployment rate and suicide in both genders (male: RR = 1.04, 95% CI: 1.02 to 1.06; female: RR = 1.07, 95% CI: 1.00 to 1.16). No significant association was observed for rainfall, minimum temperature, SEIFA, proportion of population with low individual income and low educational attainment. In the second stage of this study, we undertook a preliminary spatiotemporal analysis of suicide using seasonal data. Firstly, we assessed the interrelations between variables. Secondly, a generalised estimating equations (GEE) model was used to examine the socio-environmental impact on suicide over time and space, as this model is well suited to analyze repeated longitudinal data (e.g., seasonal suicide mortality in a certain LGA) and it fitted the data better than other models (e.g., Poisson model). The suicide pattern varied with season and LGA. The north of Queensland had the highest suicide mortality rate in all the seasons, while there was no suicide case occurred in the southwest. Northwest had consistently higher suicide mortality in spring, autumn and winter. In other areas, suicide mortality varied between seasons. This analysis showed that maximum temperature was positively associated with suicide among male population (RR = 1.24, 95% CI: 1.04 to 1.47) and total population (RR = 1.15, 95% CI: 1.00 to 1.32). Higher proportion of Indigenous population was accompanied with more suicide among total population (RR = 1.16, 95% CI: 1.13 to 1.19) and by gender (male: RR = 1.07, 95% CI: 1.01 to 1.13; female: RR = 1.23, 95% CI: 1.03 to 1.48). Unemployment rate was positively associated with total (RR = 1.40, 95% CI: 1.24 to 1.59) and female (RR=1.09, 95% CI: 1.01 to 1.18) suicide. There was also a positive association between proportion of population with low individual income and suicide in total (RR = 1.28, 95% CI: 1.10 to 1.48) and male (RR = 1.45, 95% CI: 1.23 to 1.72) population. Rainfall was only positively associated with suicide in total population (RR = 1.11, 95% CI: 1.04 to 1.19). There was no significant association for rainfall, minimum temperature, SEIFA, proportion of population with low educational attainment. The second stage is the extension of the first stage. Different spatial scales of dataset were used between the two stages (i.e., mean yearly data in the first stage, and seasonal data in the second stage), but the results are generally consistent with each other. Compared with other studies, this research explored the variety of the impact of a wide range of socio-environmental factors on suicide in different geographical units. Maximum temperature, proportion of Indigenous population, unemployment rate and proportion of population with low individual income were among the major determinants of suicide in Queensland. However, the influence from other factors (e.g. socio-culture background, alcohol and drug use) influencing suicide cannot be ignored. An in-depth understanding of these factors is vital in planning and implementing suicide prevention strategies. Five recommendations for future research are derived from this study: (1) It is vital to acquire detailed personal information on each suicide case and relevant information among the population in assessing the key socio-environmental determinants of suicide; (2) Bayesian model could be applied to compare mortality rates and their socio-environmental determinants across LGAs in future research; (3) In the LGAs with warm weather, high proportion of Indigenous population and/or unemployment rate, concerted efforts need to be made to control and prevent suicide and other mental health problems; (4) The current surveillance, forecasting and early warning system needs to be strengthened, to trace the climate and socioeconomic change over time and space and its impact on population health; (5) It is necessary to evaluate and improve the facilities of mental health care, psychological consultation, suicide prevention and control programs; especially in the areas with low socio-economic status, high unemployment rate, extreme weather events and natural disasters.

This study attempts to answer the research question ‘Can a novel model of health information system strengthen process for conducting research to understand the effects of air pollution on CVD in developing countries?’ There is limited research output from Asia and in particular, from India on studies of the deleterious effects of air pollution on CVD. This research aimed to investigate the barriers in developing countries and proposed the use of a spatiotemporal methodology to assess the effects of air pollution on CVD by developing an application based on a GIS platform. Choosing Bangalore as a case study area, secondary data from various governmental departments that included demographic data, air pollution data and mortality data were obtained. An Environmental Health Information system application based on GIS platform was developed specifically for Bangalore and with the characteristics of the datasets available. Data quality assessment was carried out on these datasets that resulted in the recommendation of a generalisable data quality framework to enable better data collection that will aid in strengthening health development policies. The data was analysed using spatial and non-spatial techniques. Results showed that levels of PM10 were of concern to the city with all areas having either high or critical levels of pollution. CVD deaths also were of concern contributing to almost 40% of total mortality. The potential years of life lost (PYLL), which is an estimate of the average years a person would have lived if he or she had not died prematurely was calculated for the years from 2010 to 2013; this revealed that 2.1 million person years were lost in Bangalore due to CVD alone. These potential years lost is an important factor to consider, as preventive measures taken by the Government will result in a significant economic impact on the city. The limitations of few monitoring stations were overcome by using spatial interpolation techniques such as Inverse Distance Weighted interpolation technique. The performance of the interpolation was tested using cross-validation techniques and the results revealed that Bangalore city would benefit from increased measuring stations for PM10. The logistic regression conducted showed that pollution especially PM10 was a likely predictor of CVD in the city. Spatial analysis was conducted and included buffering, overlay maps, queries and Hotspot analysis highlighting the zone hotspots. The results from the research guided the development of the novel 5-I model that would assist other similar developing cities to assess the effects of air pollution on CVD. The impetus is that based on evidence, intervention policies and programs may be implemented to inform research and practice which will ultimately have social, economic and health impact on the population. On implementation of the model, hotspots will be identified in order to roll out interventions to priority areas and populations most at risk that will ultimately prevent millions of deaths and enhance overall quality of life.

Abstract COVID-19, as named by the World Health Organization, is a disease caused by severe acute respiratory syndrome, coronavirus 2 (SARS CoV-2). This study is a spatiotemporal analysis of the COVID-19 pandemic in school-age children (5-18 years) in Washington and Johnson County, Tennessee and the possible relationship between public policies and the rate of infection. The first cases in Tennessee were documented in March 2020, with data being collected since that time. Daily data are accessible on the Tennessee Health Department COVID-19 dashboard with the number of new cases, hospitalizations, and deaths grouped by county in ages 5-11 years and 12-18 years. As this disease spread, government officials mandated different policies: mask mandates, stay at home, restrictions of public gatherings, and school closure, but many schools eventually allowed physical attendance. Emerging spatiotemporal hotspots are analyzed to identify the spatial clustering patterns of hot and cold spots with statistical significance using the Moran I statistical model in ArcGIS. The Change point detection tool in ArcGIS makes inferences about significant changes in trends over time; it was used to identify when significant changes occur. This is an ongoing project that will inform the approach I will adopt for my thesis, statistical tools will be used to determine the correlation between the time the change occurred and the implementation of policies, with an estimated 14-day lag time. Finally, the findings from both age groups will be compared. This study aims to help policymakers make better-informed decisions when responding to future pandemics.

Influenza is accompanying humans for centuries and for centuries people are dying in hundreds. Every year there is seasonal influenza epidemy, which is caused by common circling influenza viruses in which happened small changes. Every 10-20 years is discovered completely new influenza virus subtype which is created by big genetical changes. Most affected areas are poor states in Asia or in different countries with insufficient hygiene or unavailable medical care, these countries also have huge mortality. Czech Republic wasn't independent for many centuries and describing historical evolution of this disease is very complicated, but we have few data form Spanish influenza time. Influenza and other acute or chronical respiratory are reported, so it is possible to create some demographic or statistical analysis. Unfortunately, in Czech Republic only 50-55 % of all cases are reported. This work is devoted to spatiotemporal analysis of influenza in Czechia and is done through statistical analysis like corelation analysis or time series analysis that are examining spatiotemporal way of the spread of the virus. Goals of this thesis with the help of these methods are to answer questions about questions concerning the spatiotemporal patterns of influenza spread, therefore what the repeatability of...

This study is a spatiotemporal analysis of the COVID-19 pandemic in school-age children (5-18 years) in Tennessee, from 2020-03-19 to 2022-02-12. Trend Analysis, Emerging Hot Spot Analysis, and a time series revealed three significant waves in both age groups. Therefore, Change Point Detection at the county level was completed using six defined change points to identify the wax and wane of the three COVID-19 waves. Hierarchical Cluster Analysis grouped counties with similar change points into six clusters. No spatial pattern was observed in distribution of the six clusters, however, when each change point was evaluated separately, spatial autocorrelation was present, showing that timing of the individual waves was clustered in space. This research describes appropriate spatioanalytical methods useful at different stages of a pandemic and could inform policymaking by public health officials.

Abstract Probabilistic reasoning under uncertainty suits well to analysis of disease dynamics. The stochastic nature of disease progression is modeled by applying the principles of Bayesian learning. Bayesian learning predicts the disease progression, including prevalence and incidence, for a geographic region and demographic composition. Public health resources, prioritized by the order of risk levels of the population, will efficiently minimize the disease spread and curtail the epidemic at the earliest. A Bayesian network representing the outbreak of influenza and pneumonia in a geographic region is ported to a newer region with different demographic composition. Upon analysis for the newer region, the corresponding prevalence of influenza and pneumonia among the different demographic subgroups is inferred for the newer region. Bayesian reasoning coupled with disease timeline is used to reverse engineer an influenza outbreak for a given geographic and demographic setting. The temporal flow of the epidemic among the different sections of the population is analyzed to identify the corresponding risk levels. In comparison to spread vaccination, prioritizing the limited vaccination resources to the higher risk groups results in relatively lower influenza prevalence. HIV incidence in Texas from 1989-2002 is analyzed using demographic based epidemic curves. Dynamic Bayesian networks are integrated with probability distributions of HIV surveillance data coupled with the census population data to estimate the proportion of HIV incidence among the different demographic subgroups. Demographic based risk analysis lends to observation of varied spectrum of HIV risk among the different demographic subgroups. A methodology using hidden Markov models is introduced that enables to investigate the impact of social behavioral interactions in the incidence and prevalence of infectious diseases. The methodology is presented in the context of simulated disease outbreak data for influenza. Probabilistic reasoning analysis enhances the understanding of disease progression in order to identify the critical points of surveillance, control and prevention. Public health resources, prioritized by the order of risk levels of the population, will efficiently minimize the disease spread and curtail the epidemic at the earliest.

For many species, population size data is difficult to obtain or even unavailable. Therefore, estimations or indirect abundance measures of populations are crucial for ungulate management. Hunting has an important role in wildlife management, and is a partnership between state, landowners, and hunting communities. In ungulate management harvest statistics, as well as ungulate-vehicle collisions (UVCs) and observations, are often used as proxies for population densities and provide detailed information on a spatial and temporal scale. A Bayesian approach was used to model hunters’ response to population densities of fallow deer (Dama dama), roe deer (Capreolus capreolus), and wild boar (Sus scrofa). The results indicate that the variability in number of individuals observed and the non-linearity of both hunters’ harvest and UVCs response varies between roe deer, fallow deer, and wild boar. Both hunters’ harvest and UVC display a sub-linear response to population densities to all three species. Additionally, roe deer show a low variability in the number of individuals observed compared to the other two species. Predictions of population densities by using proxies like harvest statistics and UVC statistics need to be species-specific to be reliable, and by considering a potential sub-linearity and temporal trends for the species of interest more confident and realistic estimates can be developed.

No description available.

The volume of data acquired in production systems continues to expand. Emerging imaging technologies, such as machine vision systems (MVSs) and 3D surface scanners, diversify the types of data being collected, further pushing data collection beyond discrete dimensional data. These large and diverse datasets increase the challenge of extracting useful information. Unfortunately, industry still relies heavily on traditional quality methods that are limited to fault detection, which fails to consider important diagnostic information needed for process recovery. Modern measurement technologies should spur the transformation of statistical process control (SPC) to provide practitioners with additional diagnostic information. This dissertation focuses on how MVSs and 3D laser scanners can be further utilized to meet that goal. More specifically, this work: 1) reviews image-based control charts while highlighting their advantages and disadvantages; 2) integrates spatiotemporal methods with digital image processing to detect process faults and estimate their location, size, and time of occurrence; and 3) shows how point cloud data (3D laser scans) can be used to detect and locate unknown faults in complex geometries. Overall, the research goal is to create new quality control tools that utilize high density data available in manufacturing environments to generate knowledge that supports decision-making beyond just indicating the existence of a process issue. This allows industrial practitioners to have a rapid process recovery once a process issue has been detected, and consequently reduce the associated downtime. / Ph. D.

Focos geográficos bem definidos de pênfigo prevalecem no mundo todo, inclusive no Brasil. Nas últimas décadas vem sendo estudada a possibilidade de fatores ambientais participarem do desencadeamento da doença. A região nordeste do Estado de São Paulo, onde localizam-se três Bacias Hidrográficas, apresenta prevalência de duas formas clínicas do pênfigo, pênfigo vulgar (PV) e pênfigo foliáceo endêmico (PFE) sendo uma importante área para o estudo da doença. Nesse estudo, foi utilizado um Sistema de Informação Geográfica (SIG) para descrever a distribuição espacial e o comportamento temporal de PV e PFE nessa região do Estado de São Paulo nas últimas cinco décadas e caracterizar o uso e ocupação do solo no município com maior número de ocorrências de pênfigo. Os pacientes foram identificados baseados nos prontuários médicos entre 1965 e 2014. Os mapas temáticos foram desenvolvidos com o software ArcGIS 10.2. Para representar a distribuição espacial do pênfigo, os mapas foram organizados em décadas de 1965 a 2014. Para o município com maior número de ocorrências de PV e PFE, o uso e ocupação do solo, de acordo com a hidrografia, a vegetação nativa, à área agrícola, o solo exposto e a área urbana, foi analisado em um raio de 2 km no entorno da residência dos pacientes no momento do surgimento dos sintomas. Como análise adicional, mapas ilustrando a distribuição dos casos de pênfigo de acordo com as classes hipsométricas, declividade do solo e densidade populacional por distrito do município (Norte, Sul, Leste, Oeste e Central) foram também desenvolvidos. Quatrocentos e vinte e seis casos foram analisados. Os casos de PFE predominaram, com 285 (67%) dos casos. De acordo com a distribuição espacial e evolução temporal, PV não foi reportado de 1965 a 1974, entretanto, os casos de PV tiveram um aumento contínuo nas próximas décadas e ultrapassou o número de casos de PFE na última década. Analisando de forma acumulada os casos, tanto o PV quanto o PFE tiveram aumento ao longo do período estudado, revelando uma expansão espacial. A Bacia Hidrográfica do Rio Pardo teve o maior número de casos com um total de 153 (41% PV e 59% PFE). No período de 1965 a 2014 o número de cidades com registros de casos de PV e PFE aumentou de 0 para 49 e de 13 para 60, respectivamente, com Ribeirão Preto e Batatais sendo os principais focos geográficos de PV e PFE, respectivamente. Ribeirão Preto foi o município com maior ocorrência de pênfigo (35 casos de PV e 37 casos de PFE). A área agrícola (42%) e o solo exposto (33,2%) foram os usos do solo que predominam no município. Além disso, todos os pacientes com PV ou PFE moram perto de rios e área agrícola. Em Ribeirão Preto, os casos de pênfigo estão concentrados nos distritos norte e oeste, os casos de PFE estão distribuídos em baixas altitudes quando comparadas com o PV e tanto o PV quanto o PFE predominam em áreas com baixa porcentagem de declividade do solo. No contexto da saúde pública, o SIG se tornou uma importante ferramenta que ajuda os pesquisadores entenderem a ocorrência e tendência de certos eventos, conduzindo nas melhores estratégias de controle de doenças. As análises de distribuição espacial e evolução temporal mostraram que os casos de PV e PFE aumentaram na região nordeste do Estado de São Paulo nas últimas cinco décadas. Esse monitoramento também ajudou a identificar os principais focos geográficos de pênfigo nessa região. A predominância de agricultura e solo exposto em Ribeirão Preto e a proximidade dos casos com rios e agricultura reforça a hipótese de que os fatores ambientais desempenham um importante papel na etiopatogênese do pênfigo / Defined foci of pemphigus prevalence worldwide, including Brazil, raise the possibility that environmental factors trigger the onset of this disease. The northeastern region of the state of São Paulo is located within three Watersheds and is an appropriate site to investigate pemphigus because this disease is prevalent in both clinical forms--endemic pemphigus foliaceus (PFE) and pemphigus vulgaris (PV)--therein. In this study, we have used Geographic Information Systems (GIS) to describe the spatial distribution and temporal behavior of PV and PFE in this region of the state of São Paulo over the last five decades; we have also characterized land use in the city with the highest number of cases. Patients were identified based on patients\' medical records between 1965 and 2014. Thematic maps were developed with the ArcGIS 10.2 software. To represent the spatial distribution of pemphigus, maps were organized in decades from 1965 to 2014. For the city with the highest occurrence of PFE and PV cases, land use regarding hydrography, native vegetation, agriculture, exposed soil, and urbanization was analyzed within a 2-km buffer surrounding from the patients\' residencies considering the address where the pemphigus clinical signs and symptoms started. For additional analysis, thematic maps illustrating the distribution of pemphigus cases according to hypsometric classes, soil declivity, and population density by sector (North, South, East, West, and Central) were designed. Four hundred and twenty-six cases were analyzed. PFE cases predominated: they corresponded to 285 or 67% of the cases. Regarding spatial distribution and temporal evolution, PV was not reported from 1965 to 1974; notwithstanding, PV rose in the following four decades and overcame the number of PFE cases in the last decade. Regarding cumulative cases, both PV and PFE increased throughout the studied period, which revealed spatial expansion. The Pardo River Basin had the highest number of cases with a total of 153 (41% PV and 59% PFE). In the studied period, the number of cities with recorded cases of PV and PFE increased from 0 to 49 and from 13 to 60, respectively, with Ribeirão Preto and Batatais being the main geographical foci of PV and PFE, respectively. Ribeirão Preto was the city with the highest occurrence of pemphigus--35 PV cases and 37 PFE cases. Agricultural area (42%) and exposed soil (33.2%) were the land uses that predominated in the city. In addition, all patients with PV and PFE lived close to rivers and agricultural areas. In Ribeirão Preto, pemphigus cases were concentrated in the northern and western sectors; PFE cases were distributed at lower altitudes as compared to PV; and both PV and PFE predominated in areas with lower percentage of declivity. In the context of public health, GIS has become a powerful tool that helps researchers to understand the occurrence and trend of some events, leading to improved interventional strategies and disease control. The spatial distribution and temporal evolution analyses showed PV and PFE increased in the northeastern region of the state of São Paulo over the last five decades. This monitoring also helped to identify the main geographical foci of pemphigus. The predominance of agriculture and exposed soil in Ribeirão Preto and the proximity of the cases to rivers and agriculture reinforced the hypothesis that environmental factors play a role in pemphigus etiopathogenesis