Social support for people living with HIV in rural America remains a considerably understudied aspect of HIV/AIDS prevention. People living with HIV/AIDS (PLHA) require extensive support in order to remain in care, and reduce their viral suppression, and other disease complications. Without support, the likelihood that PLHA will refrain from or drop out of treatment options is gravely heightened, which consequently poses a significant threat for efforts to eliminate HIV as a public health issue. Using a mixed-method approach to Social Network Analysis, this study examines the principal role that social support plays in a person’s likelihood to adhere to care and consequently, attain viral suppression. Specifically, it looks at the roles of the family, friends, partners/spouses, and healthcare providers. The study also explores how social relations serve as mediators to stigma and discrimination, especially for disproportionate groups. Closely linked to social support availability is the perceived level of significance of the type of support that is available to the subjects. The study therefore goes further to explore the subjects’ perception of the support they receive (emotional, informational, and instrumental) and their satisfaction with it. This is imperative in that it sheds light on the role that the subjects’ social relations plays in their retention in care. This research again takes an interdisciplinary approach by exploring the contribution of both communication and health communication strategies to effect behavioral change. It contributes to research on HIV/AIDS health equity, and infectious disease management. It also contributes to efforts to identify strategies to control the spread of HIV by proposing efficient ways to optimize social support through the stages of the Care Continuum and consequently, facilitate an increase in the number of people who attain viral suppression. Keywords: Social Network Analysis; Social Support; Rural HIV; PLHA; Stigma.

Studies of adaptive governance in social-ecological systems have identified common features that assist social actors in responding to environmental pressures. Among these features, multiple sources of ecological knowledge, trust, and networks between actors have been highlighted as properties that contribute to successful governance arrangements. However, studies in adaptive governance have also been critiqued using a political ecology approach. This is due to their under-theorization of political elements that can constrain or promote the formation of the features of adaptive governance. In particular, power dynamics between actors and the subsequent privileging of one source of knowledge over another might have an effect on governance arrangements. In China, environmental degradation is a serious issue. The Qinhuai River, located in the city of Nanjing, has experienced significant ecological decline over the last 30 years as urbanization pressures on the system increased. Over the same period, China has undergone changes in state-society relations, including allowing the formation of NGOs. Since the turn of the millennium, several NGOs have begun working on issues related to the Qinhuai River, including raising awareness and producing information on the environment. This study examines the features of adaptive governance in a critical light by situating them in the local political context of China. The relationship between NGOs, fishers who use the Qinhuai River and government are examined using Social Network Analysis and semi-structured interviews in order to understand the production of information, networking and trust between these actors. It is shown that the existing arrangements to include NGOs and fishers in the river’s governance activities are guided by a corporatist system of state-sanctioned representation. This is not conducive to adaptive governance arrangements, despite the increasing existence of ENGO networks and new sources of knowledge over the last decade. It is thus important that studies of adaptive governance take steps to contextualize their findings within the local political climate.

Increasing collaborative working between GPs and community pharmacists has recently become a high priority for the NHS. Previous research suggests that interaction is limited and problematic between the two professions, forming a barrier to service provision. This PhD aimed to explore the level, nature and process of interaction between GPs and community pharmacists, using a social network analysis approach.The study focused on four geographically different case study areas and 90 GPs and community pharmacists participated in total. A two-stage design was adopted. Firstly data were collected through a network questionnaire and analysed using social network analysis. Secondly, qualitative interviews were conducted to provide narrative to the network findings and analysed using the framework approach.The nature of contact was characterised as mostly indirect through brokers, de-personalised and non-reciprocal and seemingly at odds with collaborative behaviour. A misalignment in responses pointed to asymmetry in the relationship, representing little commonality, knowing and understanding of each other. Through social network analysis, individuals and dyads in possession of strong ties were identified. Strong ties were not the norm and were characterised by more personalised forms of reciprocal contact. Qualitative interviews provided insight into the processes of interaction between the two professional groups. An approach to the interaction, which involved pharmacists tactically managing the potential conflict in the interaction through use of deferential and sometimes subservient behaviour, was conceptualised as the ‘pharmacist-GP game’. Those pharmacists with strong ties to GPs also, at times, adopted aspects of this approach but also attempted to set themselves apart from other pharmacists in order to develop and maintain their strong ties with GPs. However, possession of strong ties did not always lead to capitalisation, and the benefits of possessing these were often viewed as efficiency and convenience gains rather than anything more wide-reaching. Often, more isolated GPs and pharmacists did not view strong ties as a necessity, with the benefits of these not considered rewarding enough for the time and effort required to achieve them. This effort-reward conflict was identified as an important constraint faced by GPs and pharmacists in relation to transforming these loose connections into more integrated networks. Other micro and macro level constraints were also identified and a series of accompanying recommendations made for future practice and research.

<p>Care of patients with chronic conditions is complicated and usually includes large number of healthcare providers. Understanding the team structure and networks of healthcare providers help to make informed decisions for health policy makers and design of wellness programs by identifying the influencers in the network. This work presents a novel approach to assess the collaboration of healthcare providers involved in the care of patients with chronic conditions and the impact on patient outcomes. </p> <p>In the first study, we assessed a patient population needs, preventive service utilization, and impact of an onsite clinic as an intervention on preventive service utilization patterns over a three-year period. Classification models were developed to identify groups of patients with similar characteristics and healthcare utilization. Logistic regression models identified patient factors that impacted their utilization of preventive health services in the onsite clinic vs. other providers. Females had higher utilizations compared to males. Type of insurance coverages, and presence of diabetes/hypertension were significant factors that impacted utilization. The first study framework helps to understand the patient population characteristics and role of specific providers (onsite clinic), however, it does not provide information about the teams of healthcare providers involved in the care process. </p> <p>Considering the high prevalence of diabetes in the patient cohort of study 1, in the second study, we followed the patient cohort with diabetes from study 1 and extracted their healthcare providers over a two-year period. A framework based on the social network analysis was presented to assess the healthcare providers’ networks and teams involved in the care of diabetes. The relations between healthcare providers were generated based on the patient sharing relations identified from the claims data. A multi-scale community detection algorithm was used to identify groups of healthcare providers more closely working together. Centrality measures of the social network identified the influencers in the overall network and each community. Mail-order and retail pharmacies were identified as central providers in the overall network and majority of communities. This study presented metrics and approach for assessment of provider collaboration. To study how these collaborative relations impact the patients, in the last study, we presented a framework to assess impacts of healthcare provider collaboration on patient outcomes. </p> <p>We focused on patients with diabetes, hypertension, and hyperlipidemia due to their similar healthcare needs and utilization. Similar to the second study, social network analysis and a multi-scale community detection algorithm were used to identify networks and communities of healthcare providers. We identified providers who were the majority source of care for patients over a three-year period. Regression models using generalized estimating equations were developed to assess the impact of majority source of care provider community-level centrality on patient outcomes. Higher connectedness (higher degree centrality) and higher access (higher closeness centrality) of the majority source of care provider were associated with reduced number of inpatient hospitalization and emergency department visits. </p> <p>This research proposed a framework based on the social network analysis that provides metrics for assessment of care team relations using large-scale health data. These metrics help implementation experts to identify influencers in the network for better design of care intervention programs. The framework is also useful for health services researchers to assess impact of care teams’ relations on patient outcomes. </p> <br> <p> </p>

abstract: Many existing applications of machine learning (ML) to cybersecurity are focused on detecting malicious activity already present in an enterprise. However, recent high-profile cyberattacks proved that certain threats could have been avoided. The speed of contemporary attacks along with the high costs of remediation incentivizes avoidance over response. Yet, avoidance implies the ability to predict - a notoriously difficult task due to high rates of false positives, difficulty in finding data that is indicative of future events, and the unexplainable results from machine learning algorithms. In this dissertation, these challenges are addressed by presenting three artificial intelligence (AI) approaches to support prioritizing defense measures. The first two approaches leverage ML on cyberthreat intelligence data to predict if exploits are going to be used in the wild. The first work focuses on what data feeds are generated after vulnerability disclosures. The developed ML models outperform the current industry-standard method with F1 score more than doubled. Then, an approach to derive features about who generated the said data feeds is developed. The addition of these features increase recall by over 19% while maintaining precision. Finally, frequent itemset mining is combined with a variant of a probabilistic temporal logic framework to predict when attacks are likely to occur. In this approach, rules correlating malicious activity in the hacking community platforms with real-world cyberattacks are mined. They are then used in a deductive reasoning approach to generate predictions. The developed approach predicted unseen real-world attacks with an average increase in the value of F1 score by over 45%, compared to a baseline approach. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2019

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Analysis of large network datasets has become increasingly important. Algorithms have been designed to find many kinds of structure, with numerous applications across the social and biological sciences. However, a tradeoff is always present between accuracy and scalability; otherwise promising techniques can be computationally infeasible when applied to networks with huge numbers of nodes and edges. One way of extending the reach of network analysis is to sparsify the graph by retaining only a subset of its edges. The reduced network could prove much more tractable. For this thesis, I propose a new sparsification algorithm that preserves the properties of a random walk on the network. Specifically, the algorithm finds a subset of edges that best preserves the stationary distribution of a random walk by minimizing the Kullback-Leibler divergence between a walk on the original and sparsified graphs. A highly efficient greedy search strategy is developed to optimize this objective. Experimental results are presented that test the performance of the algorithm on the influence maximization task. These results demonstrate that sparsification allows near-optimal solutions to be found in a small fraction of the runtime that would required using the full network. Two cases are shown where sparsification allows an influence maximization algorithm to be applied to a dataset that previous work had considered intractable.