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- All Subjects: public health
- Genre: Doctoral Dissertation
- Creators: Hruschka, Daniel
- Creators: Mubayi, Anuj
Description
Food system and health characteristics were evaluated across the last Waorani hunter-gatherer group in Amazonian Ecuador and a remote neighboring Kichwa indigenous subsistence agriculture community. Hunter-gatherer food systems like the Waorani foragers may not only be nutritionally, but also pharmaceutically beneficial because of high dietary intake of varied plant phytochemical compounds. A modern diet that reduces these dietary plant defense phytochemicals below levels typical in human evolutionary history may leave humans vulnerable to diseases that were controlled through a foraging diet. Few studies consider the health impact of the recent drastic reduction of plant phytochemical content in the modern global food system, which has eliminated essential components of food because they are not considered "nutrients". The antimicrobial and anti-inflammatory nature of the food system may not only regulate infectious pathogens and inflammatory disease, but also support beneficial microbes in human hosts, reducing vulnerability to chronic diseases. Waorani foragers seem immune to certain infections with very low rates of chronic disease. Does returning to certain characteristics of a foraging food system begin to restore the human body microbe balance and inflammatory response to evolutionary norms, and if so, what implication does this have for the treatment of disease? Several years of data on dietary and health differences across the foragers and the farmers was gathered. There were major differences in health outcomes across the board. In the Waorani forager group there were no signs of infection in serious wounds such as 3rd degree burns and spear wounds. The foragers had one-degree lower body temperature than the farmers. The Waorani had an absence of signs of chronic diseases including vision and blood pressure that did not change markedly with age while Kichwa farmers suffered from both chronic diseases and physiological indicators of aging. In the Waorani forager population, there was an absence of many common regional infectious diseases, from helminthes to staphylococcus. Study design helped control for confounders (exercise, environment, genetic factors, non-phytochemical dietary intake). This study provides evidence of the major role total phytochemical dietary intake plays in human health, often not considered by policymakers and nutritional and agricultural scientists.
ContributorsLondon, Douglas (Author) / Tsuda, Takeyuki (Thesis advisor) / Beezhold, Bonnie L (Committee member) / Hruschka, Daniel (Committee member) / Eder, James (Committee member) / Arizona State University (Publisher)
Created2012
Description
Contact tracing was deployed widely during the COVID-19 pandemic to attempt to stop the spread of SARS Co-V-2. This dissertation investigates the research on contact tracing from a scientometric perspective and looks qualitatively at how case investigators and contact tracers conducted public health practice during the pandemic. Through approaching the public health practice of contact tracing from both a broad, top-down angle, and an on the ground experiential approach, this dissertation provides insight into the issues facing contact tracing as a public health tool.
ContributorsWhite, Alexandra C. (Author) / Jehn, Megan (Thesis advisor) / Hruschka, Daniel (Committee member) / Gaughan, Monica (Committee member) / Arizona State University (Publisher)
Created2022
Description
Latest estimates show that roughly 188 individuals in the United States die everyday due to an opioid-related overdose. This dissertation explores three avenues for
mitigating opioid use disorder (OUD) and the opioid epidemic in the United States (1.)
How can researchers and public health professionals identify areas most in need of
treatment for OUD in an easy-to-use and publicly accessible interface?; (2.) What do
practitioners see as opportunities for reducing barriers to treatment?; and (3.) Why do
differences in opioid mortality exist between demographic groups? To address question
one, I developed an interactive web-based to assist in identifying those counties with the
greatest unmet need of medically assisted treatment (MAT). To answer question two, I
conducted a study of stakeholders (medical providers, peer support specialists, public
health practitioners, etc.) in four New Mexico counties with high unmet need of MAT. to
identify cultural and structural barriers to MAT provision in underserved areas as well as
opportunities for improving access. To answer the third question. I conducted a
systematic review of peer-reviewed literature and government reports to identify how
previous research accounts for race/ethnic and sex disparities in opioid-related
mortality. While many opioid mortality studies show demographic differences, little is
known about why they exist. According to the findings of this systematic review, research
needs to go beyond identifying demographic differences in opioid-related mortality to
understand the reasons for those differences to reduce these inequities.
ContributorsDrake, Alexandria (Author) / Hruschka, Daniel (Thesis advisor) / Jehn, Megan (Committee member) / Scott, Mary Alice (Committee member) / Arizona State University (Publisher)
Created2023
Description
Statistical Methods have been widely used in understanding factors for clinical and public health data. Statistical hypotheses are procedures for testing pre-stated hypotheses. The development and properties of these procedures as well as their performance are based upon certain assumptions. Desirable properties of statistical tests are to maintain validity and to perform well even if these assumptions are not met. A statistical test that maintains such desirable properties is called robust. Mathematical models are typically mechanistic framework, used to study dynamic interactions between components (mechanisms) of a system, and how these interactions give rise to the changes in behavior (patterns) of the system as a whole over time.
In this thesis, I have developed a study that uses novel techniques to link robust statistical tests and mathematical modeling methods guided by limited data from developed and developing regions in order to address pressing clinical and epidemiological questions of interest. The procedure in this study consists of three primary steps, namely, data collection, uncertainty quantification in data, and linking dynamic model to collected data.
The first part of the study focuses on designing, collecting, and summarizing empirical data from the only national survey of hospitals ever conducted regarding patient controlled analgesia (PCA) practices among 168 hospitals across 40 states, in order to assess risks before putting patients on PCA. I used statistical relational models and exploratory data analysis to address the question. Risk factors assessed indicate a great concern for the safety of patients from one healthcare institution to other.
In the second part, I quantify uncertainty associated with data obtained from James A Lovell Federal Healthcare Center to primarily study the effect of Benign Prostatic Hypertrophy (BPH) on sleep architecture in patients with Obstructive Sleep Apnea (OSA). Patients with OSA and BPH demonstrated significant difference in their sleep architecture in comparison to patients without BPH. One of the ways to validate these differences in sleep architecture between the two groups may be to carry out a similar study that evaluates the effect of some other chronic disease on sleep architecture in patients with OSA.
Additionally, I also address theoretical statistical questions such as (1) how to estimate the distribution of a variable in order to retest null hypothesis when the sample size is limited, and (2) how changes on assumptions (like monotonicity and nonlinearity) translate into the effect of the independent variable on the outcome variable. To address these questions we use multiple techniques such as Partial Rank Correlation Coefficients (PRCC) based sensitivity analysis, Fractional Polynomials, and statistical relational models.
In the third part, my goal was to identify socio-economic-environment-related risk factors for Visceral Leishmaniasis (VL) and use the identified critical factors to develop a mathematical model to understand VL transmission dynamics when data is highly underreported. I primarily studied the role of age-specific- susceptibility and epidemiological quantities on the dynamics of VL in the Indian state of Bihar. Statistical results provided ideas on the choice of the modeling framework and estimates of model parameters.
In the conclusion, this study addressed three primary theoretical modeling-related questions (1) how to analyze collected data when sample size limited, and how modeling assumptions varies results of data analysis? (2) Is it possible to identify hidden associations and nonlinearity of these associations using such underpowered data and (3) how statistical models provide more reasonable structure to mathematical modeling framework that can be used in turn to understand dynamics of the system.
In this thesis, I have developed a study that uses novel techniques to link robust statistical tests and mathematical modeling methods guided by limited data from developed and developing regions in order to address pressing clinical and epidemiological questions of interest. The procedure in this study consists of three primary steps, namely, data collection, uncertainty quantification in data, and linking dynamic model to collected data.
The first part of the study focuses on designing, collecting, and summarizing empirical data from the only national survey of hospitals ever conducted regarding patient controlled analgesia (PCA) practices among 168 hospitals across 40 states, in order to assess risks before putting patients on PCA. I used statistical relational models and exploratory data analysis to address the question. Risk factors assessed indicate a great concern for the safety of patients from one healthcare institution to other.
In the second part, I quantify uncertainty associated with data obtained from James A Lovell Federal Healthcare Center to primarily study the effect of Benign Prostatic Hypertrophy (BPH) on sleep architecture in patients with Obstructive Sleep Apnea (OSA). Patients with OSA and BPH demonstrated significant difference in their sleep architecture in comparison to patients without BPH. One of the ways to validate these differences in sleep architecture between the two groups may be to carry out a similar study that evaluates the effect of some other chronic disease on sleep architecture in patients with OSA.
Additionally, I also address theoretical statistical questions such as (1) how to estimate the distribution of a variable in order to retest null hypothesis when the sample size is limited, and (2) how changes on assumptions (like monotonicity and nonlinearity) translate into the effect of the independent variable on the outcome variable. To address these questions we use multiple techniques such as Partial Rank Correlation Coefficients (PRCC) based sensitivity analysis, Fractional Polynomials, and statistical relational models.
In the third part, my goal was to identify socio-economic-environment-related risk factors for Visceral Leishmaniasis (VL) and use the identified critical factors to develop a mathematical model to understand VL transmission dynamics when data is highly underreported. I primarily studied the role of age-specific- susceptibility and epidemiological quantities on the dynamics of VL in the Indian state of Bihar. Statistical results provided ideas on the choice of the modeling framework and estimates of model parameters.
In the conclusion, this study addressed three primary theoretical modeling-related questions (1) how to analyze collected data when sample size limited, and how modeling assumptions varies results of data analysis? (2) Is it possible to identify hidden associations and nonlinearity of these associations using such underpowered data and (3) how statistical models provide more reasonable structure to mathematical modeling framework that can be used in turn to understand dynamics of the system.
ContributorsGonzalez, Beverly, 1980- (Author) / Castillo-Chavez, Carlos (Thesis advisor) / Mubayi, Anuj (Thesis advisor) / Nuno, Miriam (Committee member) / Arizona State University (Publisher)
Created2015
Description
Background: This study examines how pro-vaccine flu messages, guided by the Extended Parallel Process Model (EPPM), affect parents’ intentions to vaccinate their children.
Methods: Parents of children six months to five years old (N = 975) were randomly exposed to one of four high-threat/high-efficacy messages (narrative, statistical, combined, control) and completed a follow-up survey. Differences between message conditions were assessed with one-way ANOVAs, and binary logistic regressions were used to show how constructs predicted intentions.
Results: There were no significant differences in the ANOVA results at p = .05 for EPPM variables or risk EPPM variables. There was a significant difference between message conditions for perceived manipulation (p = 0.026), authority, (p = 0.024), character (p = 0.037), attention (p < .000), and emotion (p < .000). The EPPM model and perceptions of message model (positively), and the risk EPPM model and fear control model (negatively), predicted intentions to vaccinate. Significant predictor variables in each model at p < .05 were severity (aOR = 1.83), response efficacy (aOR = 4.33), risk susceptibility (aOR = 0.53), risk fear (aOR = 0.74), issue derogation (aOR = 0.63), perceived manipulation (aOR = 0.64), character (aOR = 2.00), and personal relevance (aOR = 1.88). In a multivariate model of the significant predictors, only response efficacy significantly predicted intentions to vaccinate (aOR = 3.43). Compared to the control, none of the experimental messages significantly predicted intentions to vaccinate. The narrative and combined conditions significantly predicted intentions to search online (aOR = 2.37), and the combined condition significantly predicted intentions to talk to family/friends (aOR = 2.66).
Conclusions: The EPPM may not be effective in context of a two-way threat. Additional constructs that may be useful in the EPPM model are perceptions of the message and fear control variables. One-shot flu vaccine messages will be unlikely to directly influence vaccination rates; however they may increase information-seeking behavior. The impact of seeking more information on vaccination uptake requires further research. Flu vaccine messages should be presented in combined form. Future studies should focus on strategies to increase perceptions of the effectiveness of the flu vaccine.
Methods: Parents of children six months to five years old (N = 975) were randomly exposed to one of four high-threat/high-efficacy messages (narrative, statistical, combined, control) and completed a follow-up survey. Differences between message conditions were assessed with one-way ANOVAs, and binary logistic regressions were used to show how constructs predicted intentions.
Results: There were no significant differences in the ANOVA results at p = .05 for EPPM variables or risk EPPM variables. There was a significant difference between message conditions for perceived manipulation (p = 0.026), authority, (p = 0.024), character (p = 0.037), attention (p < .000), and emotion (p < .000). The EPPM model and perceptions of message model (positively), and the risk EPPM model and fear control model (negatively), predicted intentions to vaccinate. Significant predictor variables in each model at p < .05 were severity (aOR = 1.83), response efficacy (aOR = 4.33), risk susceptibility (aOR = 0.53), risk fear (aOR = 0.74), issue derogation (aOR = 0.63), perceived manipulation (aOR = 0.64), character (aOR = 2.00), and personal relevance (aOR = 1.88). In a multivariate model of the significant predictors, only response efficacy significantly predicted intentions to vaccinate (aOR = 3.43). Compared to the control, none of the experimental messages significantly predicted intentions to vaccinate. The narrative and combined conditions significantly predicted intentions to search online (aOR = 2.37), and the combined condition significantly predicted intentions to talk to family/friends (aOR = 2.66).
Conclusions: The EPPM may not be effective in context of a two-way threat. Additional constructs that may be useful in the EPPM model are perceptions of the message and fear control variables. One-shot flu vaccine messages will be unlikely to directly influence vaccination rates; however they may increase information-seeking behavior. The impact of seeking more information on vaccination uptake requires further research. Flu vaccine messages should be presented in combined form. Future studies should focus on strategies to increase perceptions of the effectiveness of the flu vaccine.
ContributorsHall, Sarah (Author) / Jehn, Megan (Thesis advisor) / Mongeau, Paul (Committee member) / Hruschka, Daniel (Committee member) / Margolis, Eric (Committee member) / Arizona State University (Publisher)
Created2015
Description
The health situation of indigenous peoples is comparable to that of the world's poorest populations, but with the additional burdens of social and cultural marginalization, geographic and cultural barriers to accessing health services, and, in some areas, appropriation of land and natural resources. Cultural transmission (the transfer of beliefs, ideas, and behaviors from one culture to another) from outsider health institutions should presumably aid in closing this health gap by transferring knowledge, practices, and infrastructure to prevent and treat disease. This study examines the biosocial construction of the disease ecology of tuberculosis (TB) in indigenous communities of the Paraguayan Chaco with varying degrees of cultural transmission from outside institutions (government, religious, and NGOs), to determine the influence of cultural transmission on local disease ecologies. Using a biocultural epidemiological framework for the analysis of human infectious disease ecology, this study employed an interdisciplinary, mixed methods approach to examine the interactions of host, pathogen, and the environment in the Paraguayan Chaco. Three case studies examining aspects of TB disease ecology in indigenous communities are presented: (1) The effective cultural transmission of biomedical knowledge to isolated communities, (2) Public health infrastructure, hygiene, and the prevalence of intestinal parasites: co-morbidities that promote the progression to active TB disease, and (3) Community-level risk factors for TB and indigenous TB burden. Findings from the case studies suggest that greater influence from outside institutions was not associated with greater adoption of biomedical knowledge of TB. The prevalence of helminthiasis was unexpectedly low, but infection with giardia was common, even in a community with cleaner water sources. Communities with a health post were more likely to report active adult TB, while communities with more education were less likely to report active pediatric TB, suggesting that healthcare access is the major determinant of TB detection. More research is needed on the role of non-indigenous community residents and other measures of acculturation or integration in TB outcomes, especially at the household level. Indigenous TB burden in the Chaco is disproportionately high, and better understanding of the mechanisms that produce higher incidence and prevalence of the disease is needed.
ContributorsVansteelandt, Amanda (Author) / Hurtado, Ana Magdalena (Thesis advisor) / Stone, Anne (Thesis advisor) / Hruschka, Daniel (Committee member) / Rojas de Arias, Antonieta (Committee member) / Arizona State University (Publisher)
Created2014
Description
Suicide is one of the fastest-growing and least-understood causes of death, particularly in low and middle income countries (LMIC). In low-income settings, where the technical capacity for death surveillance is limited, suicides may constitute a significant portion of early deaths, but disappear as they are filtered through reporting systems shaped by social, cultural, and political institutions. These deaths become unknown and unaddressed. This dissertation illuminates how suicide is perceived, contested, experienced, and interpreted in institutions ranging from the local (i.e., family, community) to the professional (i.e., medical, law enforcement) in Nepal, a country purported to have one of the highest suicide rates in the world. Drawing on a critical medical anthropology approach, I bridge public health and anthropological perspectives to better situate the problem of suicide within a greater social-political context. I argue that these complex, contestable deaths, become falsely homogenized, or lost. During 18 months of fieldwork in Nepal, qualitative, data tracing, and psychological autopsy methodologies were conducted. Findings are shared through three lenses: (1) health policy and world systems; (2) epidemiology and (3) socio-cultural. The first investigates how actors representing familial, legal, and medical institutions perceive, contest, and negotiate suicide documentation, ultimately failing to accurately capture a leading cause of death. Using epidemiologic perspectives, surveillance data from medical and legal agencies are analyzed and pragmatic approaches to better detect and prevent suicidal death in the Nepali context are recommended. The third lens provides perceived explanatory models for suicide. These narratives offer important insights into the material, social, and cultural factors that shape suicidal acts in Nepal. Findings are triangulated to inform policy, prevention, and intervention approaches to reduce suicidal behavior and improve health system capabilities to monitor violent deaths. These approaches go beyond typical psychological investigations of suicide by situating self-inflicted death within broader familial, social, and political contexts. Findings contribute to cultural anthropological theories related to suicide and knowledge production, while informing public health solutions. Looking from the margins towards centers of power, this dissertation explicates how varying institutional numbers can obfuscate and invalidate suffering experienced at local levels.
ContributorsHagaman, Ashley (Author) / Wutich, Amber (Thesis advisor) / Hruschka, Daniel (Committee member) / Kohrt, Brandon (Committee member) / Arizona State University (Publisher)
Created2017
Description
This dissertation explores the impact of environmental dependent risk on disease dynamics within a Lagrangian modeling perspective; where the identity (defined by place of residency) of individuals is preserved throughout the epidemic process. In Chapter Three, the impact of individuals who refuse to be vaccinated is explored. MMR vaccination and birth rate data from the State of California are used to determine the impact of the anti-vaccine movement on the dynamics of growth of the anti-vaccine sub-population. Dissertation results suggest that under realistic California social dynamics scenarios, it is not possible to revert the influence of anti-vaccine
contagion. In Chapter Four, the dynamics of Zika virus are explored in two highly distinct idealized environments defined by a parameter that models highly distinctive levels of risk, the result of vector and host density and vector control measures. The underlying assumption is that these two communities are intimately connected due to economics with the impact of various patterns of mobility being incorporated via
the use of residency times. In short, a highly heterogeneous community is defined by its risk of acquiring a Zika infection within one of two "spaces," one lacking access to health services or effective vector control policies (lack of resources or ignored due to high levels of crime, or poverty, or both). Low risk regions are defined as those with access to solid health facilities and where vector control measures are implemented routinely. It was found that the better connected these communities are, the existence of communities where mobility between risk regions is not hampered, lower the overall, two patch Zika prevalence. Chapter Five focuses on the dynamics of tuberculosis (TB), a communicable disease, also on an idealized high-low risk set up. The impact of mobility within these two highly distinct TB-risk environments on the dynamics and control of this disease is systematically explored. It is found that collaboration and mobility, under some circumstances, can reduce the overall TB burden.
contagion. In Chapter Four, the dynamics of Zika virus are explored in two highly distinct idealized environments defined by a parameter that models highly distinctive levels of risk, the result of vector and host density and vector control measures. The underlying assumption is that these two communities are intimately connected due to economics with the impact of various patterns of mobility being incorporated via
the use of residency times. In short, a highly heterogeneous community is defined by its risk of acquiring a Zika infection within one of two "spaces," one lacking access to health services or effective vector control policies (lack of resources or ignored due to high levels of crime, or poverty, or both). Low risk regions are defined as those with access to solid health facilities and where vector control measures are implemented routinely. It was found that the better connected these communities are, the existence of communities where mobility between risk regions is not hampered, lower the overall, two patch Zika prevalence. Chapter Five focuses on the dynamics of tuberculosis (TB), a communicable disease, also on an idealized high-low risk set up. The impact of mobility within these two highly distinct TB-risk environments on the dynamics and control of this disease is systematically explored. It is found that collaboration and mobility, under some circumstances, can reduce the overall TB burden.
ContributorsMoreno Martínez, Victor Manuel (Author) / Castillo-Chavez, Carlos (Thesis advisor) / Kang, Yun (Committee member) / Mubayi, Anuj (Committee member) / Arizona State University (Publisher)
Created2018
Description
Neglected tropical diseases (NTDs) comprise of diverse communicable diseases that affect mostly the developing economies of the world, the “neglected” populations. The NTDs Visceral Leishmaniasis (VL) and Soil-transmitted Helminthiasis (STH) are among the top contributors of global mortality and/or morbidity. They affect resource-limited regions (poor health-care literacy, infrastructure, etc.) and patients’ treatment behavior is irregular due to the social constraints. Through two case studies, VL in India and STH in Ghana, this work aims to: (i) identify the additional and potential hidden high-risk population and its behaviors critical for improving interventions and surveillance; (ii) develop models with those behaviors to study the role of improved control programs on diseases’ dynamics; (iii) optimize resources for treatment-related interventions.
Treatment non-adherence is a less focused (so far) but crucial factor for the hindrance in WHO’s past VL elimination goals. Moreover, treatment non-adherers, hidden from surveillance, lead to high case-underreporting. Dynamical models are developed capturing the role of treatment-related human behaviors (patients’ infectivity, treatment access and non-adherence) on VL dynamics. The results suggest that the average duration of treatment adherence must be increased from currently 10 days to 17 days for a 28-day Miltefosine treatment to eliminate VL.
For STH, children are considered as a high-risk group due to their hygiene behaviors leading to higher exposure to contamination. Hence, Ghana, a resource-limited country, currently implements a school-based Mass Drug Administration (sMDA) program only among children. School staff (adults), equally exposed to this high environmental contamination of STH, are largely ignored under the current MDA program. Cost-effective MDA policies were modeled and compared using alternative definitions of “high-risk population”. This work optimized and evaluated how MDA along with the treatment for high-risk adults makes a significant improvement in STH control under the same budget. The criticality of risk-structured modeling depends on the infectivity coefficient being substantially different for the two adult risk groups.
This dissertation pioneers in highlighting the cruciality of treatment-related risk groups for NTD-control. It provides novel approaches to quantify relevant metrics and impact of population factors. Compliance with the principles and strategies from this study would require a change in political thinking in the neglected regions in order to achieve persistent NTD-control.
Treatment non-adherence is a less focused (so far) but crucial factor for the hindrance in WHO’s past VL elimination goals. Moreover, treatment non-adherers, hidden from surveillance, lead to high case-underreporting. Dynamical models are developed capturing the role of treatment-related human behaviors (patients’ infectivity, treatment access and non-adherence) on VL dynamics. The results suggest that the average duration of treatment adherence must be increased from currently 10 days to 17 days for a 28-day Miltefosine treatment to eliminate VL.
For STH, children are considered as a high-risk group due to their hygiene behaviors leading to higher exposure to contamination. Hence, Ghana, a resource-limited country, currently implements a school-based Mass Drug Administration (sMDA) program only among children. School staff (adults), equally exposed to this high environmental contamination of STH, are largely ignored under the current MDA program. Cost-effective MDA policies were modeled and compared using alternative definitions of “high-risk population”. This work optimized and evaluated how MDA along with the treatment for high-risk adults makes a significant improvement in STH control under the same budget. The criticality of risk-structured modeling depends on the infectivity coefficient being substantially different for the two adult risk groups.
This dissertation pioneers in highlighting the cruciality of treatment-related risk groups for NTD-control. It provides novel approaches to quantify relevant metrics and impact of population factors. Compliance with the principles and strategies from this study would require a change in political thinking in the neglected regions in order to achieve persistent NTD-control.
ContributorsThakur, Mugdha (Author) / Mubayi, Anuj (Thesis advisor) / Hurtado, Ana M (Committee member) / Paaijmans, Krijn (Committee member) / Michael, Edwin (Committee member) / Arizona State University (Publisher)
Created2020
Description
The severity of the health and economic devastation resulting from outbreaks of viruses such as Zika, Ebola, SARS-CoV-1 and, most recently, SARS-CoV-2 underscores the need for tools which aim to delineate critical disease dynamical features underlying observed patterns of infectious disease spread. The growing emphasis placed on genome sequencing to support pathogen outbreak response highlights the need to adapt traditional epidemiological metrics to leverage this increasingly rich data stream. Further, the rapidity with which pathogen molecular sequence data is now generated, coupled with advent of sophisticated, Bayesian statistical techniques for pathogen molecular sequence analysis, creates an unprecedented opportunity to disrupt and innovate public health surveillance using 21st century tools. Bayesian phylogeography is a modeling framework which assumes discrete traits -- such as age, location of sampling, or species -- evolve according to a continuous-time Markov chain process along a phylogenetic tree topology which is inferred from molecular sequence data.
While myriad studies exist which reconstruct patterns of discrete trait evolution along an inferred phylogeny, attempts to translate the results of phyloegographic analyses into actionable metrics that can be used by public health agencies to direct the development of interventions aimed at reducing pathogen spread are conspicuously absent from the literature. In this dissertation, I focus on developing an intuitive metric, the phylogenetic risk ratio (PRR), which I use to translate the results of Bayesian phylogeographic modeling studies into a form actionable by public health agencies. I apply the PRR to two case studies: i) age-associated diffusion of influenza A/H3N2 during the 2016-17 US epidemic and ii) host associated diffusion of West Nile virus in the US. I discuss the limitations of this (and Bayesian phylogeographic) approaches when studying non-geographic traits for which limited metadata is available in public molecular sequence databases and statistically principled solutions to the missing metadata problem in the phylogenetic context. Then, I perform a simulation study to evaluate the statistical performance of the missing metadata solution. Finally, I provide a solution for researchers whom are interested in using the PRR and phylogenetic UTMs in their own genomic epidemiological studies yet are deterred by the idiosyncratic, error-prone processes required to implement these methods using popular Bayesian phylogenetic inference software packages. My solution, Build-A-BEAST, is a publicly available, object-oriented system written in python which aims to reduce the complexity and idiosyncrasy of creating XML files necessary to perform the aforementioned analyses. This dissertation extends the conceptual framework of Bayesian phylogeographic methods, develops a method to translates the output of phylogenetic models into an actionable form, evaluates the use of priors for missing metadata, and, finally, provides a solution which eases the implementation of these methods. In doing so, I lay the foundation for future work in disseminating and implementing Bayesian phylogeographic methods for routine public health surveillance.
While myriad studies exist which reconstruct patterns of discrete trait evolution along an inferred phylogeny, attempts to translate the results of phyloegographic analyses into actionable metrics that can be used by public health agencies to direct the development of interventions aimed at reducing pathogen spread are conspicuously absent from the literature. In this dissertation, I focus on developing an intuitive metric, the phylogenetic risk ratio (PRR), which I use to translate the results of Bayesian phylogeographic modeling studies into a form actionable by public health agencies. I apply the PRR to two case studies: i) age-associated diffusion of influenza A/H3N2 during the 2016-17 US epidemic and ii) host associated diffusion of West Nile virus in the US. I discuss the limitations of this (and Bayesian phylogeographic) approaches when studying non-geographic traits for which limited metadata is available in public molecular sequence databases and statistically principled solutions to the missing metadata problem in the phylogenetic context. Then, I perform a simulation study to evaluate the statistical performance of the missing metadata solution. Finally, I provide a solution for researchers whom are interested in using the PRR and phylogenetic UTMs in their own genomic epidemiological studies yet are deterred by the idiosyncratic, error-prone processes required to implement these methods using popular Bayesian phylogenetic inference software packages. My solution, Build-A-BEAST, is a publicly available, object-oriented system written in python which aims to reduce the complexity and idiosyncrasy of creating XML files necessary to perform the aforementioned analyses. This dissertation extends the conceptual framework of Bayesian phylogeographic methods, develops a method to translates the output of phylogenetic models into an actionable form, evaluates the use of priors for missing metadata, and, finally, provides a solution which eases the implementation of these methods. In doing so, I lay the foundation for future work in disseminating and implementing Bayesian phylogeographic methods for routine public health surveillance.
ContributorsVaiente, Matteo (Author) / Scotch, Matthew (Thesis advisor) / Mubayi, Anuj (Committee member) / Liu, Li (Committee member) / Arizona State University (Publisher)
Created2020