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1.
Khare, Peeyush.
Vertical Concentration Gradient of Influenza Viruses Resuspended from Floor Dust.
Degree: MS, Civil and Environmental Engineering, 2014, Virginia Tech
URL: http://hdl.handle.net/10919/49662 
► 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…
(more)
▼ 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.
Advisors/Committee Members: Marr, Linsey C. (committeechair), Eubank, Stephen G. (committee member), Battaglia, Francine (committee member).
Subjects/Keywords: walking; eddy diffusivity; influenza A virus; indoor
…and Environmental Engineering
Virginia Tech, Blacksburg, VA 24060, USA
Corresponding author…
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APA (6th Edition):
Khare, P. (2014). Vertical Concentration Gradient of Influenza Viruses Resuspended from Floor Dust. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/49662
Chicago Manual of Style (16th Edition):
Khare, Peeyush. “Vertical Concentration Gradient of Influenza Viruses Resuspended from Floor Dust.” 2014. Masters Thesis, Virginia Tech. Accessed January 25, 2020.
http://hdl.handle.net/10919/49662.
MLA Handbook (7th Edition):
Khare, Peeyush. “Vertical Concentration Gradient of Influenza Viruses Resuspended from Floor Dust.” 2014. Web. 25 Jan 2020.
Vancouver:
Khare P. Vertical Concentration Gradient of Influenza Viruses Resuspended from Floor Dust. [Internet] [Masters thesis]. Virginia Tech; 2014. [cited 2020 Jan 25].
Available from: http://hdl.handle.net/10919/49662.
Council of Science Editors:
Khare P. Vertical Concentration Gradient of Influenza Viruses Resuspended from Floor Dust. [Masters Thesis]. Virginia Tech; 2014. Available from: http://hdl.handle.net/10919/49662
2.
Robertson, Jeffrey Alan.
Entropy Measurements and Ball Cover Construction for Biological Sequences.
Degree: MS, Computer Science, 2018, Virginia Tech
URL: http://hdl.handle.net/10919/84470
► 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…
(more)
▼ 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.
Advisors/Committee Members: Heath, Lenwood S. (committeechair), Marathe, Madhav Vishnu (committee member), Eubank, Stephen G. (committee member).
Subjects/Keywords: Bioinformatics; Entropy Scaling; Sequence Search; BLAST
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APA (6th Edition):
Robertson, J. A. (2018). Entropy Measurements and Ball Cover Construction for Biological Sequences. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/84470
Chicago Manual of Style (16th Edition):
Robertson, Jeffrey Alan. “Entropy Measurements and Ball Cover Construction for Biological Sequences.” 2018. Masters Thesis, Virginia Tech. Accessed January 25, 2020.
http://hdl.handle.net/10919/84470.
MLA Handbook (7th Edition):
Robertson, Jeffrey Alan. “Entropy Measurements and Ball Cover Construction for Biological Sequences.” 2018. Web. 25 Jan 2020.
Vancouver:
Robertson JA. Entropy Measurements and Ball Cover Construction for Biological Sequences. [Internet] [Masters thesis]. Virginia Tech; 2018. [cited 2020 Jan 25].
Available from: http://hdl.handle.net/10919/84470.
Council of Science Editors:
Robertson JA. Entropy Measurements and Ball Cover Construction for Biological Sequences. [Masters Thesis]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/84470
Virginia Tech
3.
Dorratoltaj, Nargesalsadat.
Immunological, Epidemiological, and Economic modeling of HIV, Influenza, and Fungal Meningitis.
Degree: PhD, Biomedical and Veterinary Sciences, 2016, Virginia Tech
URL: http://hdl.handle.net/10919/81876
► 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…
(more)
▼ 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.
Advisors/Committee Members: Abbas, Kaja Moinudeen (committeechair), Eubank, Stephen G. (committee member), O'Dell, Margaret Lee (committee member), Bassaganya-Riera, Josep (committee member), Rahmandad, Hazhir (committee member).
Subjects/Keywords: Infectious Disease Modeling; HIV; Influenza; Fungal Meningitis; Immunology; Epidemiology; Economics
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APA (6th Edition):
Dorratoltaj, N. (2016). Immunological, Epidemiological, and Economic modeling of HIV, Influenza, and Fungal Meningitis. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/81876
Chicago Manual of Style (16th Edition):
Dorratoltaj, Nargesalsadat. “Immunological, Epidemiological, and Economic modeling of HIV, Influenza, and Fungal Meningitis.” 2016. Doctoral Dissertation, Virginia Tech. Accessed January 25, 2020.
http://hdl.handle.net/10919/81876.
MLA Handbook (7th Edition):
Dorratoltaj, Nargesalsadat. “Immunological, Epidemiological, and Economic modeling of HIV, Influenza, and Fungal Meningitis.” 2016. Web. 25 Jan 2020.
Vancouver:
Dorratoltaj N. Immunological, Epidemiological, and Economic modeling of HIV, Influenza, and Fungal Meningitis. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2020 Jan 25].
Available from: http://hdl.handle.net/10919/81876.
Council of Science Editors:
Dorratoltaj N. Immunological, Epidemiological, and Economic modeling of HIV, Influenza, and Fungal Meningitis. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/81876
Virginia Tech
4.
Khorramzadeh, Yasamin.
Network Reliability: Theory, Estimation, and Applications.
Degree: PhD, Physics, 2015, Virginia Tech
URL: http://hdl.handle.net/10919/64383
► 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…
(more)
▼ 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.
Advisors/Committee Members: Eubank, Stephen G. (committeechair), Tauber, Uwe C. (committeechair), Scarola, Vito W. (committee member), Pleimling, Michel Jean (committee member), Heremans, Jean Joseph (committee member).
Subjects/Keywords: Complex Networks; Network Reliability; Network Topology
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APA (6th Edition):
Khorramzadeh, Y. (2015). Network Reliability: Theory, Estimation, and Applications. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/64383
Chicago Manual of Style (16th Edition):
Khorramzadeh, Yasamin. “Network Reliability: Theory, Estimation, and Applications.” 2015. Doctoral Dissertation, Virginia Tech. Accessed January 25, 2020.
http://hdl.handle.net/10919/64383.
MLA Handbook (7th Edition):
Khorramzadeh, Yasamin. “Network Reliability: Theory, Estimation, and Applications.” 2015. Web. 25 Jan 2020.
Vancouver:
Khorramzadeh Y. Network Reliability: Theory, Estimation, and Applications. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2020 Jan 25].
Available from: http://hdl.handle.net/10919/64383.
Council of Science Editors:
Khorramzadeh Y. Network Reliability: Theory, Estimation, and Applications. [Doctoral Dissertation]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/64383
Virginia Tech
5.
Maloo, Akshay.
Dynamic Behavior Visualizer: A Dynamic Visual Analytics Framework for Understanding Complex Networked Models.
Degree: MS, Computer Science, 2014, Virginia Tech
URL: http://hdl.handle.net/10919/25296
► 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,…
(more)
▼ 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.
Advisors/Committee Members: Marathe, Madhav Vishnu (committeechair), Eubank, Stephen G. (committee member), Vullikanti, Anil Kumar S. (committee member), Xie, Dawen (committee member).
Subjects/Keywords: Information Visualization; Visual Analytics; Data Modeling; Networked Models
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APA (6th Edition):
Maloo, A. (2014). Dynamic Behavior Visualizer: A Dynamic Visual Analytics Framework for Understanding Complex Networked Models. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/25296
Chicago Manual of Style (16th Edition):
Maloo, Akshay. “Dynamic Behavior Visualizer: A Dynamic Visual Analytics Framework for Understanding Complex Networked Models.” 2014. Masters Thesis, Virginia Tech. Accessed January 25, 2020.
http://hdl.handle.net/10919/25296.
MLA Handbook (7th Edition):
Maloo, Akshay. “Dynamic Behavior Visualizer: A Dynamic Visual Analytics Framework for Understanding Complex Networked Models.” 2014. Web. 25 Jan 2020.
Vancouver:
Maloo A. Dynamic Behavior Visualizer: A Dynamic Visual Analytics Framework for Understanding Complex Networked Models. [Internet] [Masters thesis]. Virginia Tech; 2014. [cited 2020 Jan 25].
Available from: http://hdl.handle.net/10919/25296.
Council of Science Editors:
Maloo A. Dynamic Behavior Visualizer: A Dynamic Visual Analytics Framework for Understanding Complex Networked Models. [Masters Thesis]. Virginia Tech; 2014. Available from: http://hdl.handle.net/10919/25296
6.
Rivers, Caitlin.
Modeling Emerging Infectious Diseases for Public Health Decision Support.
Degree: PhD, Animal and Poultry Sciences, 2015, Virginia Tech
URL: http://hdl.handle.net/10919/52023
► 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…
(more)
▼ 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.
Advisors/Committee Members: Eubank, Stephen G. (committeechair), Lewis, Bryan Leroy (committeechair), Chretien, Jean-Paul (committee member), Alexander, Kathleen Ann (committee member).
Subjects/Keywords: epidemiology; zoonoses; emerging infectious diseases; infectious disease modeling
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APA (6th Edition):
Rivers, C. (2015). Modeling Emerging Infectious Diseases for Public Health Decision Support. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/52023
Chicago Manual of Style (16th Edition):
Rivers, Caitlin. “Modeling Emerging Infectious Diseases for Public Health Decision Support.” 2015. Doctoral Dissertation, Virginia Tech. Accessed January 25, 2020.
http://hdl.handle.net/10919/52023.
MLA Handbook (7th Edition):
Rivers, Caitlin. “Modeling Emerging Infectious Diseases for Public Health Decision Support.” 2015. Web. 25 Jan 2020.
Vancouver:
Rivers C. Modeling Emerging Infectious Diseases for Public Health Decision Support. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2020 Jan 25].
Available from: http://hdl.handle.net/10919/52023.
Council of Science Editors:
Rivers C. Modeling Emerging Infectious Diseases for Public Health Decision Support. [Doctoral Dissertation]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/52023
Virginia Tech
7.
Mowlaei, Shahir.
Mean Field Analysis of Generalized Cyclic Competitions.
Degree: PhD, Physics, 2015, Virginia Tech
URL: http://hdl.handle.net/10919/52962
► 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…
(more)
▼ 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.
Advisors/Committee Members: Pleimling, Michel Jean (committeechair), Eubank, Stephen G. (committeechair), Tauber, Uwe C. (committee member), Huber, Patrick (committee member), Sharpe, Eric R. (committee member).
Subjects/Keywords: Population Dynamics; Mean Field; Cyclic Competition
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APA (6th Edition):
Mowlaei, S. (2015). Mean Field Analysis of Generalized Cyclic Competitions. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/52962
Chicago Manual of Style (16th Edition):
Mowlaei, Shahir. “Mean Field Analysis of Generalized Cyclic Competitions.” 2015. Doctoral Dissertation, Virginia Tech. Accessed January 25, 2020.
http://hdl.handle.net/10919/52962.
MLA Handbook (7th Edition):
Mowlaei, Shahir. “Mean Field Analysis of Generalized Cyclic Competitions.” 2015. Web. 25 Jan 2020.
Vancouver:
Mowlaei S. Mean Field Analysis of Generalized Cyclic Competitions. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2020 Jan 25].
Available from: http://hdl.handle.net/10919/52962.
Council of Science Editors:
Mowlaei S. Mean Field Analysis of Generalized Cyclic Competitions. [Doctoral Dissertation]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/52962
Virginia Tech
8.
Schlitt, James Thomas.
Applying Time-Valued Knowledge for Public Health Outbreak Response.
Degree: PhD, Genetics, Bioinformatics, and Computational Biology, 2019, Virginia Tech
URL: http://hdl.handle.net/10919/90399
► During the early stages of an outbreak of disease, simple interventions such as isolating those infected may be sufficient to prevent further cases. However, should…
(more)
▼ During the early stages of an outbreak of disease, simple interventions such as isolating those infected may be sufficient to prevent further cases. However, should this opportunity be missed, substantially more complex interventions such as the development of novel pharmaceuticals may be required. This results in a differential value for specific knowledge across the early, middle, and late stages of epidemic. Within this dissertation we explore these differentials via extension of the business concept of the time-value of knowledge, whereby key findings may yield greater benefits during early epidemics. We propose the C4 Response Model for organizing research regarding this time-value. First, we define the C4 Response Model as a progression from an initial knowledge collection stage, iteration between knowledge connection stages and machine learning-centric calibration stages, and a final conveyance stage. Secondly we analyze the trends in knowledge-building across the stages of epidemics with regard to open and closed access scientific article publication, referencing, and citation. Thirdly, we demonstrate a Twitter application for improving public health messaging campaigns by identifying optimal combinations of source-profile categories, message categories, timing, urban origination, tone, and use of bots. Finally, we apply an agent-based model of influenza transmission to explore the efficacy of combined antiviral, isolation, 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 growing in use and are published and referenced in significantly greater numbers. We observe that tweet viralities showed distinct benefits to certain message and profile type pairings, that tweets faded rapidly across time and space, and that tweets published before high-tweet-volume time periods are retweeted more. Finally, we saw that while early responses and strong pharmaceuticals showed the greatest impact in preventing influenza transmission within military base populations, even optimistic scenarios failed to prevent the majority to new cases. This body of work offers significant methodological contributions for the practice of computational epidemiology as well as a theoretical grounding for the C4 Response Model.
Advisors/Committee Members: Eubank, Stephen G. (committeechair), Lewis, Bryan Leroy (committeechair), Hungerford, Laura L. (committee member), Abbas, Kaja Moinudeen (committee member).
Subjects/Keywords: Epidemiology; outbreak science; open data; open science; infectious diseases; opioids; addiction; social media; twitter; agent-based modeling; influenza; influenza like illness; modeling and simulation; SEIR; preparedness; antiviral; vaccine
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APA ·
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APA (6th Edition):
Schlitt, J. T. (2019). Applying Time-Valued Knowledge for Public Health Outbreak Response. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/90399
Chicago Manual of Style (16th Edition):
Schlitt, James Thomas. “Applying Time-Valued Knowledge for Public Health Outbreak Response.” 2019. Doctoral Dissertation, Virginia Tech. Accessed January 25, 2020.
http://hdl.handle.net/10919/90399.
MLA Handbook (7th Edition):
Schlitt, James Thomas. “Applying Time-Valued Knowledge for Public Health Outbreak Response.” 2019. Web. 25 Jan 2020.
Vancouver:
Schlitt JT. Applying Time-Valued Knowledge for Public Health Outbreak Response. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2020 Jan 25].
Available from: http://hdl.handle.net/10919/90399.
Council of Science Editors:
Schlitt JT. Applying Time-Valued Knowledge for Public Health Outbreak Response. [Doctoral Dissertation]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/90399
Virginia Tech
9.
Telionis, Pyrros A.
Novel Applications of Geospatial Analysis in the Modeling of Infectious Diseases.
Degree: PhD, Genetics, Bioinformatics, and Computational Biology, 2019, Virginia Tech
URL: http://hdl.handle.net/10919/89432
► The focus of this work is called “spatial epidemiology”, which combines geography with public health, to answer the where, and why, of disease. This is…
(more)
▼ The focus of this work is called “spatial epidemiology”, which combines geography with public health, to answer the where, and why, of disease. This is a growing field, and you’ve likely seen it in the news and media. Have you ever seen a map of the United States turning red in some virus disaster movie? The real thing looks a lot like that. After the Ebola outbreak of 2014, public health agencies wanted to know where the next one might hit. Now that there is another outbreak, we need to ask where and how will it spread? What areas are hardest hit, and how bad is it going to get? We can answer all these questions with spatial epidemiology. Our work adds to two aspects of spatial epidemiology: niche modeling, and mobility. We use niche modeling to determine where we could find certain diseases, usually those that are spread by insects or animals. Consider Lyme disease, you get it from the bite of a tick, and the tick gets it from a white-footed mouse. But both the mice and ticks only live in certain parts of the country. With niche modeling we can determine where those are, and we can also guess at what makes those areas attractive to the mice and ticks. Is it winter harshness, summer temperatures, rainfall, and/or elevation? Is it something else? In Chapter 2, we show that you can extend this idea. Instead of just looking at where the disease is, what if we could guess how many people will get infected? What if we could do so, a year in advance? We show that this can be done, but we need a good idea of what the weather will be like next year. In Chapter 4, we show that you can do the same thing with hepatitis C. Instead of Lyme’s ticks and mice, hepatitis C depends on drug-use, unregulated tattooing, and unsafe sex. And like with Lyme, these things are only found in certain places. Instead of temperature or rainfall, we now need to find areas with drug-problems and poverty. But we can get an idea of this from the Census Bureau, and we can make a map of hepatitis C as easily as we did for Lyme. But hepatitis C spreads person-to-person. So, we need some idea of how people move around the area. This is where mobility comes in. Mobility is important for most public health work, from detecting outbreaks to estimating where the disease will spread next. In Chapter 3, we show how one could create a mobility model for a rural area where few maps exist. We also show how to use that model to guess where the next cases of Ebola will show up. In Chapter 4, we show how you could use mobility to improve outbreak and hotspot detection. We also show how it’s used to help estimate the number of cases in an area. Because that number depends on how many cases are imported from the surrounding areas. And the only way to estimate that is with mobility.
Advisors/Committee Members: Lewis, Bryan Leroy (committeechair), Eubank, Stephen G. (committeechair), Abbas, Kaja Moinudeen (committee member), Kolivras, Korine N. (committee member).
Subjects/Keywords: Autocovariate; Ebola; Forecast; GIS; Gravity Model; Hepatitis C; Incidence; Infectious Disease; Melioidosis; Metapopulation-Patch; Mobility; Social Transmission Niche; Spatial Autocorrelation; Spatial Epidemiology; Travel Network.
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APA ·
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Manager
APA (6th Edition):
Telionis, P. A. (2019). Novel Applications of Geospatial Analysis in the Modeling of Infectious Diseases. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/89432
Chicago Manual of Style (16th Edition):
Telionis, Pyrros A. “Novel Applications of Geospatial Analysis in the Modeling of Infectious Diseases.” 2019. Doctoral Dissertation, Virginia Tech. Accessed January 25, 2020.
http://hdl.handle.net/10919/89432.
MLA Handbook (7th Edition):
Telionis, Pyrros A. “Novel Applications of Geospatial Analysis in the Modeling of Infectious Diseases.” 2019. Web. 25 Jan 2020.
Vancouver:
Telionis PA. Novel Applications of Geospatial Analysis in the Modeling of Infectious Diseases. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2020 Jan 25].
Available from: http://hdl.handle.net/10919/89432.
Council of Science Editors:
Telionis PA. Novel Applications of Geospatial Analysis in the Modeling of Infectious Diseases. [Doctoral Dissertation]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/89432
Virginia Tech
10.
Nath, Madhurima.
Application of Network Reliability to Analyze Diffusive Processes on Graph Dynamical Systems.
Degree: PhD, Physics, 2019, Virginia Tech
URL: http://hdl.handle.net/10919/86841
► The research presented here explores the effects of the structural properties of an interacting system on the outcomes of a diffusive process using Moore-Shannon network…
(more)
▼ The research presented here explores the effects of the structural properties of an interacting system on the outcomes of a diffusive process using Moore-Shannon network reliability. The network reliability is a finite degree polynomial which provides the probability of observing a certain configuration for a diffusive process on networks. Examples of such processes analyzed here are outbreak of an epidemic in a population, spread of an invasive species through international trade of commodities and spread of a perturbation in a physical system with discrete magnetic spins. Network reliability is a novel tool which can be used to compare the efficiency of network models with the observed data, to find important components of the system as well as to estimate the functions of thermodynamic state variables.
Advisors/Committee Members: Eubank, Stephen G. (committeechair), Tauber, Uwe C. (committeechair), Sharpe, Eric R. (committee member), Anderson, Lara Briana (committee member), Tao, Chenggang (committee member).
Subjects/Keywords: Dynamics on Networks; Diffusion; Network Analysis; Network Reliability; Graph Dynamical Systems; Structural Network Measures; Network Models; Community Structure
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APA (6th Edition):
Nath, M. (2019). Application of Network Reliability to Analyze Diffusive Processes on Graph Dynamical Systems. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/86841
Chicago Manual of Style (16th Edition):
Nath, Madhurima. “Application of Network Reliability to Analyze Diffusive Processes on Graph Dynamical Systems.” 2019. Doctoral Dissertation, Virginia Tech. Accessed January 25, 2020.
http://hdl.handle.net/10919/86841.
MLA Handbook (7th Edition):
Nath, Madhurima. “Application of Network Reliability to Analyze Diffusive Processes on Graph Dynamical Systems.” 2019. Web. 25 Jan 2020.
Vancouver:
Nath M. Application of Network Reliability to Analyze Diffusive Processes on Graph Dynamical Systems. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2020 Jan 25].
Available from: http://hdl.handle.net/10919/86841.
Council of Science Editors:
Nath M. Application of Network Reliability to Analyze Diffusive Processes on Graph Dynamical Systems. [Doctoral Dissertation]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/86841
Virginia Tech
11.
Ma, Yifei.
A Database Supported Modeling Environment for Pandemic Planning and Course of Action Analysis.
Degree: PhD, Computer Science, 2013, Virginia Tech
URL: http://hdl.handle.net/10919/23264
► 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,…
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▼ 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.
Advisors/Committee Members: Marathe, Madhav Vishnu (committeechair), Chen, Jiangzhuo (committeechair), Fox, Edward A. (committee member), Bisset, Keith R. (committee member), Eubank, Stephen G. (committee member), Vullikanti, Anil Kumar S. (committee member).
Subjects/Keywords: Epidemic simulation; Database system; Distributed system
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APA ·
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CSE |
Export
to Zotero / EndNote / Reference
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APA (6th Edition):
Ma, Y. (2013). A Database Supported Modeling Environment for Pandemic Planning and Course of Action Analysis. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/23264
Chicago Manual of Style (16th Edition):
Ma, Yifei. “A Database Supported Modeling Environment for Pandemic Planning and Course of Action Analysis.” 2013. Doctoral Dissertation, Virginia Tech. Accessed January 25, 2020.
http://hdl.handle.net/10919/23264.
MLA Handbook (7th Edition):
Ma, Yifei. “A Database Supported Modeling Environment for Pandemic Planning and Course of Action Analysis.” 2013. Web. 25 Jan 2020.
Vancouver:
Ma Y. A Database Supported Modeling Environment for Pandemic Planning and Course of Action Analysis. [Internet] [Doctoral dissertation]. Virginia Tech; 2013. [cited 2020 Jan 25].
Available from: http://hdl.handle.net/10919/23264.
Council of Science Editors:
Ma Y. A Database Supported Modeling Environment for Pandemic Planning and Course of Action Analysis. [Doctoral Dissertation]. Virginia Tech; 2013. Available from: http://hdl.handle.net/10919/23264
12.
Jimenez, Jose Mauricio.
The utilization of macroergonomics and highly-detailed simulation to reduce healthcare-acquired infections.
Degree: PhD, Industrial and Systems Engineering, 2014, Virginia Tech
URL: http://hdl.handle.net/10919/25352
► 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…
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▼ 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.
Advisors/Committee Members: Kleiner, Brian M. (committeechair), Wernz, Christian (committee member), Slonim, Anthony Daniel (committee member), Robertson, John L. (committee member), Eubank, Stephen G. (committee member).
Subjects/Keywords: Systems engineering; healthcare acquired infections; macro ergonomics; simulation
…NDSSL) at Virginia Tech developed EpiSimdemics,
with the objective of studying the…
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APA ·
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Export
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APA (6th Edition):
Jimenez, J. M. (2014). The utilization of macroergonomics and highly-detailed simulation to reduce healthcare-acquired infections. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/25352
Chicago Manual of Style (16th Edition):
Jimenez, Jose Mauricio. “The utilization of macroergonomics and highly-detailed simulation to reduce healthcare-acquired infections.” 2014. Doctoral Dissertation, Virginia Tech. Accessed January 25, 2020.
http://hdl.handle.net/10919/25352.
MLA Handbook (7th Edition):
Jimenez, Jose Mauricio. “The utilization of macroergonomics and highly-detailed simulation to reduce healthcare-acquired infections.” 2014. Web. 25 Jan 2020.
Vancouver:
Jimenez JM. The utilization of macroergonomics and highly-detailed simulation to reduce healthcare-acquired infections. [Internet] [Doctoral dissertation]. Virginia Tech; 2014. [cited 2020 Jan 25].
Available from: http://hdl.handle.net/10919/25352.
Council of Science Editors:
Jimenez JM. The utilization of macroergonomics and highly-detailed simulation to reduce healthcare-acquired infections. [Doctoral Dissertation]. Virginia Tech; 2014. Available from: http://hdl.handle.net/10919/25352
. 
Open Access Theses and Dissertations
