VTechWorks Repository :: Browsing by Author "Eubank, Stephen G."

Browsing by Author "Eubank, Stephen G."

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Application of Network Reliability to Analyze Diffusive Processes on Graph Dynamical Systems
Nath, Madhurima (Virginia Tech, 2019-01-22)
Moore and Shannon's reliability polynomial can be used as a global statistic to explore the behavior of diffusive processes on a graph dynamical system representing a finite sized interacting system. It depends on both the network topology and the dynamics of the process and gives the probability that the system has a particular desired property. Due to the complexity involved in evaluating the exact network reliability, the problem has been classified as a NP-hard problem. The estimation of the reliability polynomials for large graphs is feasible using Monte Carlo simulations. However, the number of samples required for an accurate estimate increases with system size. Instead, an adaptive method using Bernstein polynomials as kernel density estimators proves useful. Network reliability has a wide range of applications ranging from epidemiology to statistical physics, depending on the description of the functionality. For example, it serves as a measure to study the sensitivity of the outbreak of an infectious disease on a network to the structure of the network. It can also be used to identify important dynamics-induced contagion clusters in international food trade networks. Further, it is analogous to the partition function of the Ising model which provides insights to the interpolation between the low and high temperature limits.
Applying Time-Valued Knowledge for Public Health Outbreak Response
Schlitt, James Thomas (Virginia Tech, 2019-06-21)
During the early stages of any epidemic, simple interventions such as quarantine and isolation may be sufficient to halt the spread of a novel pathogen. However, should this opportunity be missed, substantially more resource-intensive, complex, and societally intrusive interventions may be required to achieve an acceptable outcome. These disparities place a differential on the value of a given unit of knowledge across the time-domains of an epidemic. Within this dissertation we explore these value-differentials via extension of the business concept of the time-value of knowledge and propose the C4 Response Model for organizing the research response to novel pathogenic outbreaks. First, we define the C4 Response Model as a progression from an initial data-hungry collect stage, iteration between open-science-centric connect stages and machine-learning centric calibrate stages, and a final visualization-centric convey stage. Secondly we analyze the trends in knowledge-building across the stages of epidemics with regard to open and closed access article publication, referencing, and citation. Thirdly, we demonstrate a Twitter message mapping application to assess the virality of tweets as a function of their source-profile category, message category, timing, urban context, tone, and use of bots. Finally, we apply an agent-based model of influenza transmission to explore the efficacy of combined antiviral, sequestration, and vaccination interventions in mitigating an outbreak of an influenza-like-illness (ILI) within a simulated military base population. We find that while closed access outbreak response articles use more recent citations and see higher mean citation counts, open access articles are published and referenced in significantly greater numbers and are growing in proportion. We observe that tweet viralities showed distinct heterogeneities across message and profile type pairing, that tweets dissipated rapidly across time and space, and that tweets published before high-tweet-volume time periods showed higher virality. Finally, we saw that while timely responses and strong pharmaceutical interventions showed the greatest impact in mitigating ILI transmission within a military base, even optimistic scenarios failed to prevent the majority of new cases. This body of work offers significant methodological contributions for the practice of computational epidemiology as well as a theoretical grounding for the further use of the C4 Response Model.
Complex situation analysis system that generates a social contact network, uses edge brokers and service brokers, and dynamically adds brokers
(United States Patent and Trademark Office, 2013-04-16)
A system for generating a representation of a situation is disclosed. The system comprises one or more computer-readable media including computer-executable instructions that are executable by one or more processors to implement a method of generating a representation of a situation. The method comprises receiving input data regarding a target population. The method further comprises constructing a synthetic data set including a synthetic population based on the input data. The synthetic population includes a plurality of synthetic entities. Each synthetic entity has a one-to-one correspondence with an entity in the target population. Each synthetic entity is assigned one or more attributes based on information included in the input data. The method further comprises receiving activity data for a plurality of entities in the target population.
A Database Supported Modeling Environment for Pandemic Planning and Course of Action Analysis
Ma, Yifei (Virginia Tech, 2013-06-24)
Pandemics can significantly impact public health and society, for instance, the 2009 H1N1
and the 2003 SARS. In addition to analyzing the historic epidemic data, computational simulation of epidemic propagation processes and disease control strategies can help us understand the spatio-temporal dynamics of epidemics in the laboratory. Consequently, the public can be better prepared and the government can control future epidemic outbreaks more effectively. Recently, epidemic propagation simulation systems, which use high performance computing technology, have been proposed and developed to understand disease propagation processes. However, run-time infection situation assessment and intervention adjustment, two important steps in modeling disease propagation, are not well supported in these simulation systems. In addition, these simulation systems are computationally efficient in their simulations, but most of them have
limited capabilities in terms of modeling interventions in realistic scenarios.
In this dissertation, we focus on building a modeling and simulation environment for epidemic propagation and propagation control strategy. The objective of this work is to
design such a modeling environment that both supports the previously missing functions,
meanwhile, performs well in terms of the expected features such as modeling fidelity,
computational efficiency, modeling capability, etc. Our proposed methodologies to build
such a modeling environment are: 1) decoupled and co-evolving models for disease propagation, situation assessment, and propagation control strategy, and 2) assessing situations and simulating control strategies using relational databases. Our motivation for exploring these methodologies is as follows: 1) a decoupled and co-evolving model allows us to design modules for each function separately and makes this complex modeling system design simpler, and 2) simulating propagation control strategies using relational databases improves the modeling capability and human productivity of using this modeling environment. To evaluate our proposed methodologies, we have designed and built a loosely coupled and database supported epidemic modeling and simulation environment. With detailed experimental results and realistic case studies, we demonstrate that our modeling environment provides the missing functions and greatly enhances many expected features, such as modeling capability, without significantly sacrificing computational efficiency and scalability.
Dynamic Behavior Visualizer: A Dynamic Visual Analytics Framework for Understanding Complex Networked Models
Maloo, Akshay (Virginia Tech, 2014-02-04)
Dynamic Behavior Visualizer (DBV) is a visual analytics environment to visualize the spatial and temporal movements and behavioral changes of an individual or a group, e.g. family within a realistic urban environment. DBV is specifically designed to visualize the adaptive behavioral changes, as they pertain to the interactions with multiple inter-dependent infrastructures, in the aftermath of a large crisis, e.g. hurricane or the detonation of an improvised nuclear device. DBV is web-enabled and thus is easily accessible to any user with access to a web browser. A novel aspect of the system is its scale and fidelity. The goal of DBV is to synthesize information and derive insight from it; detect the expected and discover the unexpected; provide timely and easily understandable assessment and the ability to piece together all this information.
Entropy Measurements and Ball Cover Construction for Biological Sequences
Robertson, Jeffrey Alan (Virginia Tech, 2018-08-01)
As improving technology is making it easier to select or engineer DNA sequences that produce dangerous proteins, it is important to be able to predict whether a novel DNA sequence is potentially dangerous by determining its taxonomic identity and functional characteristics. These tasks can be facilitated by the ever increasing amounts of available biological data. Unfortunately, though, these growing databases can be difficult to take full advantage of due to the corresponding increase in computational and storage costs. Entropy scaling algorithms and data structures present an approach that can expedite this type of analysis by scaling with the amount of entropy contained in the database instead of scaling with the size of the database. Because sets of DNA and protein sequences are biologically meaningful instead of being random, they demonstrate some amount of structure instead of being purely random. As biological databases grow, taking advantage of this structure can be extremely beneficial. The entropy scaling sequence similarity search algorithm introduced here demonstrates this by accelerating the biological sequence search tools BLAST and DIAMOND. Tests of the implementation of this algorithm shows that while this approach can lead to improved query times, constructing the required entropy scaling indices is difficult and expensive. To improve performance and remove this bottleneck, I investigate several ideas for accelerating building indices that support entropy scaling searches. The results of these tests identify key tradeoffs and demonstrate that there is potential in using these techniques for sequence similarity searches.
Epidemiological and economic impact of pandemic influenza in Chicago: Priorities for vaccine interventions
Dorratoltaj, Nargesalsadat; Marathe, Achla; Lewis, Bryan L.; Swarup, Samarth; Eubank, Stephen G.; Abbas, Kaja M. (PLOS, 2017-06-01)
The study objective is to estimate the epidemiological and economic impact of vaccine interventions during influenza pandemics in Chicago, and assist in vaccine intervention priorities. Scenarios of delay in vaccine introduction with limited vaccine efficacy and limited supplies are not unlikely in future influenza pandemics, as in the 2009 H1N1 influenza pandemic. We simulated influenza pandemics in Chicago using agent-based transmission dynamic modeling. Population was distributed among high-risk and non-high risk among 0±19, 20±64 and 65+ years subpopulations. Different attack rate scenarios for catastrophic (30.15%), strong (21.96%), and moderate (11.73%) influenza pandemics were compared against vaccine intervention scenarios, at 40% coverage, 40% efficacy, and unit cost of $28.62. Sensitivity analysis for vaccine compliance, vaccine efficacy and vaccine start date was also conducted. Vaccine prioritization criteria include risk of death, total deaths, net benefits, and return on investment. The risk of death is the highest among the high-risk 65+ years subpopulation in the catastrophic influenza pandemic, and highest among the high-risk 0±19 years subpopulation in the strong and moderate influenza pandemics. The proportion of total deaths and net benefits are the highest among the high-risk 20±64 years subpopulation in the catastrophic, strong and moderate influenza pandemics. The return on investment is the highest in the high-risk 0±19 years subpopulation in the catastrophic, strong and moderate influenza pandemics. Based on risk of death and return on investment, high-risk groups of the three age group subpopulations can be prioritized for vaccination, and the vaccine interventions are cost saving for all age and risk groups. The attack rates among the children are higher than among the adults and seniors in the catastrophic, strong, and moderate influenza pandemic scenarios, due to their larger social contact network and homophilous interactions in school. Based on return on investment and higher attack rates among children, we recommend prioritizing children (0±19 years) and seniors (65+ years) after high-risk groups for influenza vaccination during times of limited vaccine supplies. Based on risk of death, we recommend prioritizing seniors (65+ years) after high-risk groups for influenza vaccination during times of limited vaccine supplies.
Immunological, Epidemiological, and Economic modeling of HIV, Influenza, and Fungal Meningitis
Dorratoltaj, Nargesalsadat (Virginia Tech, 2016-07-28)
This dissertation focuses on immunological, epidemiological, and economic modeling of HIV, influenza, and fungal meningitis, and includes three research studies. In the first study on HIV, the study objective is to analyze the dynamics of HIV-1, CD4+ T cells and macrophages during the acute, clinically latent and late phases of HIV infection in order to predict their dynamics from acute infection to clinical latency and finally to AIDS in treatment naive HIV-infected individuals. The findings of the study show that the peak in viral load during acute HIV infection is due to virus production by infected CD4+ T cells, while during the clinically latent and late phases of infection infected macrophages dominate the overall viral production. This leads to the conclusion that macrophage-induced virus production is the significant driver of HIV progression from asymptomatic phase to AIDS in HIV-infected individuals. In the second study on influenza, the study objective is to estimate the direct and indirect epidemiological and economic impact of vaccine interventions during an influenza pandemic in Chicago, and assist in vaccine intervention priorities. Population is distributed among high-risk and non-high risk within 0-19, 20-64 and 65+ years subpopulations. The findings show that based on risk of death and return on investment, high-risk groups of the three age group subpopulations can be prioritized for vaccination, and the vaccine interventions are cost-saving for all age and risk groups. In the third study on fungal meningitis, the study objective is to evaluate the effectiveness and cost of the fungal meningitis outbreak response in New River Valley of Virginia during 2012-2013, from the local public health department and clinical perspectives. We estimate the epidemiological effectiveness of this outbreak response to be 153 DALYs averted among the patients, and the costs incurred by the local health department and clinical facilities to be $30,413 and $39,580 respectively. Moving forward, multi-scale analysis of infectious diseases connecting the different scales of evolutionary, immunological, epidemiological, and economic dynamics has good potential to derive meaningful inferences for decision making in clinical and public health practice, and improve health outcomes.
Mean Field Analysis of Generalized Cyclic Competitions
Mowlaei, Shahir (Virginia Tech, 2015-06-17)
The mean field analysis of stochastic dynamical system allows us to gain insight into the qualitative features of their complex behavior, as well as quantitative estimates of certain aspects of their coarse-grained properties. As such, it usually furnishes a first front in approaching new dynamical systems and informs us about their stability landscape in the absence of fluctuations among other things. A knowledge of this landscape can be a valuable tool in model building for describing real world systems and provides a guiding principle for a justifiable choice of form and model parameters. In this work, we contribute to this analysis for two generic classes of high-dimensional models that possess a cyclic symmetry in the network that specifies their stochastic dynamics at the microscopic level. Our analysis is carried out in a manner that can be readily adapted for the mean field analysis of further generalized models that possess this symmetry. Moreover, in the second class of these models, we propose a new basic process that can change the stability landscape of an existing model and, as such, endow us with potential alternatives to model systems with robust biodiverse regimes.
Modeling Emerging Infectious Diseases for Public Health Decision Support
Rivers, Caitlin (Virginia Tech, 2015-05-05)
Emerging infectious diseases (EID) pose a serious threat to global public health. Computational epidemiology is a nascent subfield of public health that can provide insight into an outbreak in advance of traditional methodologies. Research in this dissertation will use fuse nontraditional, publicly available data sources with more traditional epidemiological data to build and parameterize models of emerging infectious diseases. These methods will be applied to avian influenza A (H7N9), Middle Eastern Respiratory Syndrome Coronavirus (MERS-CoV), and Ebola virus disease (EVD) outbreaks. This effort will provide quantitative, evidenced-based guidance for policymakers and public health responders to augment public health operations.
Multi-scale immunoepidemiological modeling of within-host and between-host HIV dynamics: systematic review of mathematical models
Dorratoltaj, Nargesalsadat; Nikin-Beers, Ryan; Ciupe, Stanca M.; Eubank, Stephen G.; Abbas, Kaja M. (PeerJ, 2017-09-28)
Objective The objective of this study is to conduct a systematic review of multi-scale HIV immunoepidemiological models to improve our understanding of the synergistic impact between the HIV viral-immune dynamics at the individual level and HIV transmission dynamics at the population level. Background While within-host and between-host models of HIV dynamics have been well studied at a single scale, connecting the immunological and epidemiological scales through multi-scale models is an emerging method to infer the synergistic dynamics of HIV at the individual and population levels. Methods We reviewed nine articles using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework that focused on the synergistic dynamics of HIV immunoepidemiological models at the individual and population levels. Results HIV immunoepidemiological models simulate viral immune dynamics at the within-host scale and the epidemiological transmission dynamics at the between-host scale. They account for longitudinal changes in the immune viral dynamics of HIV+ individuals, and their corresponding impact on the transmission dynamics in the population. They are useful to analyze the dynamics of HIV super-infection, co-infection, drug resistance, evolution, and treatment in HIV+ individuals, and their impact on the epidemic pathways in the population. We illustrate the coupling mechanisms of the within-host and between-host scales, their mathematical implementation, and the clinical and public health problems that are appropriate for analysis using HIV immunoepidemiological models. Conclusion HIV immunoepidemiological models connect the within-host immune dynamics at the individual level and the epidemiological transmission dynamics at the population level. While multi-scale models add complexity over a single-scale model, they account for the time varying immune viral response of HIV+ individuals, and the corresponding impact on the time-varying risk of transmission of HIV+ individuals to other susceptibles in the population.
Network Reliability: Theory, Estimation, and Applications
Khorramzadeh, Yasamin (Virginia Tech, 2015-12-17)
Network reliability is the probabilistic measure that determines whether a network remains functional when its elements fail at random. Definition of functionality varies depending on the problem of interest, thus network reliability has much potential as a unifying framework to study a broad range of problems arising in complex network contexts. However, since its introduction in the 1950's, network reliability has remained more of an interesting theoretical construct than a practical tool. In large part, this is due to well-established complexity costs for both its evaluation and approximation, which has led to the classification of network reliability as a NP-Hard problem. In this dissertation we present an algorithm to estimate network reliability and then utilize it to evaluate the reliability of large networks under various descriptions of functionality. The primary goal of this dissertation is to pose network reliability as a general scheme that provides a practical and efficiently computable observable to distinguish different networks. Employing this concept, we are able to demonstrate how local structural changes can impose global consequences. We further use network reliability to assess the most critical network entities which ensure a network's reliability. We investigate each of these aspects of reliability by demonstrating some example applications.
Novel Applications of Geospatial Analysis in the Modeling of Infectious Diseases
Telionis, Pyrros A. (Virginia Tech, 2019-05-08)
At the intersection of geography and public health, the field of spatial epidemiology seeks to use the tools of geospatial analysis to answer questions about disease. In this work we explore two areas: the use of geostatistical modeling as an extension of niche modeling, and the use of mobility metrics to augment modeling for epidemic responses. Niche modeling refers to the practice of using statistical methods to relate the underlying spatially distributed environmental variables to an outcome, typically presence or absence of a species. Such work is common in disease ecology, and often focuses on exploring the range of a disease vector or pathogen. The technique also allows one to explore the importance of each underlying regressor, and the effect it has on the outcome. We demonstrate that this concept can be extended, through geostatistical modeling, to explore non-logistic phenomena such as incidence. When combined with weather forecasts, such efforts can even predict incidence of an upcoming season, allowing us to estimate the total number of expected cases, and where we would expect to find them. We demonstrate this in Chapter 2, by forecasting the incidence of melioidosis in Australia given weather forecasts a year prior. We also evaluate the efficacy of this technique and explore the impact of environmental variables such as elevation on melioidosis. But these techniques are not limited to free-living and vector-borne pathogens. We theorize that they can also be applied to diseases that spread exclusively by person-to-person contact. Exploring this allows us to find areas of underreporting, as well as areas with unusual local forcing which might merit further investigation by the health department. We also explore this in Chapter 4, by relating the incidence of hepatitis C in rural Virginia to demographic data. The West African Ebola Outbreak of 2014 demonstrated the need to include mobility in predictive disease modeling. One can no longer assume that neglected tropical diseases will remain contained and immobile, and the assumption of random mixing across large areas is unwise. Our efforts with modeling mobility are twofold. In Chapter 3, we demonstrate the creation of mobility metrics from open source road and river network data. We then demonstrate the usefulness of such data in a meta-population patch model meant to forecast the spread of Ebola in the Democratic Republic of Congo. In Chapter 4, we also demonstrate that mobility data can be used to strengthen outbreak detection via hotspot analysis, and to augment incidence models by factoring in the incidence rates of neighboring areas. These efforts will allow health departments to more accurately forecast incidence, and more readily identify disease hotspots of atypical size and shape.
A survey of quality of life indicators in the Romanian Roma population following the ‘Decade of Roma Inclusion’
Doherty, Rebecca Powell; Telionis, Pyrros A.; Müller-Demary, Daniel; Hosszu, Alexandra; Duminica, Ana; Bertke, Andrea S.; Lewis, Bryan L.; Eubank, Stephen G. (F1000Research, 2019-09-18)
Background: This study explores how the Roma in Romania, the EU’s most concentrated population, are faring in terms of a number of quality of life indicators, including poverty levels, healthcare, education, water, sanitation, and hygiene. It further explores the role of synthetic populations and modelling in identifying at-risk populations and delivering targeted aid. Methods: 135 surveys were conducted across five geographically diverse Romanian communities. Household participants were selected through a comprehensive random walk method. Analyses were conducted on all data using Pandas for Python. Combining land scan data, time-use survey analyses, interview data, and ArcGIS, the resulting synthetic population was analysed via classification and regression tree (CART) analysis to identify hot-spots of need, both ethnically and geographically. Results: These data indicate that the Roma in Romania face significant disparities in education, with Roma students less likely to progress beyond 8 th grade. In addition, the Roma population remains significantly disadvantaged with regard to safe and secure housing, poverty, and healthcare status, particularly in connection to diarrheal disease. In contrast, however, both Roma and non-Roma in rural areas face difficulties regarding full-time employment, sanitation, and water, sanitation, and hygiene infrastructure. In addition, the use of a synthetic population can generate information about ‘hot spots’ of need, based on geography, ethnicity, and type of aid required. Conclusions: These data demonstrate the challenges that remain to the Roma population in Romania, and also point to the myriad of ways in which all rural Romanians, regardless of ethnicity, are encountering hardship. This study highlights an approach that combines traditional survey data with more wide-reaching geographically based data and CART analysis to determine ‘hot spot’ areas of need in a given population. With the appropriate inputs, this tool can be extrapolated to any population in any country.
Systems analysis of vaccination in the United States: Socio-behavioral dynamics, sentiment, effectiveness and efficiency
Kang, Gloria Jin (Virginia Tech, 2018-09-05)
This dissertation examines the socio-behavioral determinants of vaccination and their impacts on public health, using a systems approach that emphasizes the interface between population health research, policy, and practice. First, we identify the facilitators and barriers of parental attitudes and beliefs toward school-located influenza vaccination in the United States. Next, we examine current vaccine sentiment on social media by constructing and analyzing semantic networks of vaccine information online. Finally, we estimate the health benefits, costs, and cost-effectiveness of influenza vaccination strategies in Seattle using a dynamic agent-based model. The underlying motivation for this research is to better inform public health policy by leveraging the facilitators and addressing potential barriers against vaccination; by understanding vaccine sentiment to improve health science communication; and by assessing potential vaccination strategies that may provide the greatest gains in health for a given cost in health resources.
The utilization of macroergonomics and highly-detailed simulation to reduce healthcare-acquired infections
Jimenez, Jose Mauricio (Virginia Tech, 2014-02-07)
Background: In the United States, it is estimated that 1 in 20 patients become infected with a healthcare acquired infection (HAI). Some of the complications of HAIs include increased morbidity and mortality, and drug-resistant infections. Clostridium difficile has replaced methicillin-resistant Staphylococcus aureus (MRSA) as the most important HAI in the United States by doubling its prevalence during the last decade. Significance of the study: This study is grounded on the subdiscipline of macroergonomics and highly detailed simulation. The Macroergonomic Analysis and Design (MEAD) model is utilized to identify and correct deficiencies in work systems. The MEAD process was applied to develop possible sociotechnical interventions that can be used against HAIs. Highly detailed simulation can evaluate infection exposure, interventions, and individual behavior change for populations in large populations. These two methods provide the healthcare system stakeholders with the ability to test interventions that would otherwise be impossible to evaluate. Objective/Purpose: The purpose of this study is to identify the factors that reduce HAI infections in healthcare facility populations, and provide evidence-based best practices for these facilities. The central research question is: What type of interventions can help reduce Clostridium difficile infections? Methods: We collected one year of patient archival information to include activities, locations and contacts through electronic patient records from two Virginia regional hospitals. Healthcare worker activities were obtained through direct observation (shadowing) at the two Virginia regional hospitals. Experiments were designed to test the different types of interventions using EpiSimdemics, a highly-resolved simulation software. A Clostridium difficile disease model was developed to evaluate interventions. Results: We observed a significant drop in infection cases at a regional Hospital. There is significant evidence to link this drop in HAI infections to a sociotechnical intervention. However, there is not enough information to pinpoint the specific action that caused the drop. We additionally conducted simulation experiments with two hospital simulations. Simulated sociotechnical interventions such as hand washing, room cleaning, and isolation caused significant reductions in the infection rates. Conclusions: The combined use of macroergonomics and simulation can be beneficial in developing and evaluating interventions against HAIs. The use of statistical control charts as an epidemiology tool can help hospitals detect outbreaks or evaluate the use of interventions. Use of systemic interventions in an in-silico environment can help determine cheaper, more flexible, and more effective actions against HAIs.
Vertical Concentration Gradient of Influenza Viruses Resuspended from Floor Dust
Khare, Peeyush (Virginia Tech, 2014-07-21)
Resuspended floor dust constitutes up to sixty percent of the total particulate matter in indoor air. This fraction may also include virus-laden particles that settle on the floor after being emitted by an infected individual. This research focuses on predicting the concentration of influenza A viruses in resuspended dust, generated by people walking in a room, at various heights above the floor. Using a sonic anemometer, we measured the velocity field from floor to ceiling at 10-cm intervals to estimate the magnitude of turbulence generated by walking. The resulting eddy diffusion coefficients varied between 0.06 m2 s-1 and 0.20 m2 s-1 and were maximal at ~0.75-1 m above the floor, approximately the height of the swinging hand. We used these coefficients in an atmospheric transport model to predict virus concentrations as a function of the carrier particle size and height in the room. Results indicate that the concentration of resuspended viruses at 1 m above the floor is about seven times the concentration at 2 m. Thus, shorter people may be exposed to higher concentrations of pathogens in resuspended dust indoors. This study illuminates the possibility that particle resuspension could be a mode of disease transmission. It also emphasizes the importance of considering resuspension of particulate matter when designing ventilation systems and flooring in hospitals and residences.

Browsing by Author "Eubank, Stephen G."

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