Doctoral Dissertations
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Browsing Doctoral Dissertations by Author "Abbas, Kaja M."
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- Applying Time-Valued Knowledge for Public Health Outbreak ResponseSchlitt, 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.
- Immunological, Epidemiological, and Economic modeling of HIV, Influenza, and Fungal MeningitisDorratoltaj, 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.
- Investigating the Valley Fever – Environment Relationship in the Western U.S.Weaver, Elizabeth Ann (Virginia Tech, 2019-05-06)Valley fever, or coccidioidomycosis, is a disease caused by the Coccidioides immitis and Coccidioides posadasii fungal species that dwell in the soil but can become airborne and infect a human or mammalian host through their respiratory tract. Disease rates in the western U.S. have significantly increased over the past two decades, creating an emerging public health burden. Studies have been conducted that attempt to elucidate the association between environmental conditions and the growth and dispersal of the pathogen, yet the specific ecology of and environmental precursors to the disease remain uncertain. This research project investigates the relationship between environmental variables and valley fever by modeling the spatial and temporal dynamics of the disease using varying techniques. Chapter 1 discusses relevant literature before discussing the challenges associated with studying valley fever. Chapter 2 analyzes the temporal relationships between valley fever and climatic variables, focusing on Kern County, California, an understudied region in the U.S. where valley fever is highly endemic. Chapter 3 focuses on a regional spatial analysis using ecological niche modeling to better understand the environmental factors that influence the overall spatial distribution of valley fever in the U.S. Finally, combining both spatial and temporal components, Chapter 4 uses a hierarchical Bayesian spatio-temporal model to investigate the patterns and drivers of this disease, focusing on state of California, which saw an approximate 200% increase in cases from 2014 to 2018. Cumulatively, this work offers new insights on relationships between climate, landcover, and valley fever disease risk. Significant findings include climate variables explaining up to 76% of valley fever variability in Kern County, California, the significance of both climatic and landcover variables in characterizing the geographic distribution of the disease, and identification of patterns increasing risk in geographic regions of California not currently considered highly endemic. These findings advance scholarly understandings of valley fever's environmental disease drivers. The results of this research can be applied by public health officials in the allocation of surveillance and public education resources, focusing upon regions that are most likely to encounter the illness.
- Novel Applications of Geospatial Analysis in the Modeling of Infectious DiseasesTelionis, 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.
- Systems analysis of vaccination in the United States: Socio-behavioral dynamics, sentiment, effectiveness and efficiencyKang, 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.