Computer Science Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2756
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Item Emergent behaviors in adaptive dynamical networks with applications to biological and social systems(2021) Alexander, Brandon Marc; Girvan, Michelle; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this thesis, we consider three network-based systems, focusing on emergent behaviors resulting from adaptive dynamical features. In our first investigation, we create a model for gene regulatory networks in which the network topology evolves over time to avoid instability of the regulatory dynamics. We consider and compare different rules of competitive edge addition that use topological and dynamical information from the network to determine how new network links are added. We find that aiming to keep connected components small is more effective at preventing widespread network failure than limiting the connections of genes with high sensitivity (i.e., potential for high variability across conditions). Finally, we compare our results to real data from several species and find a trend toward disassortativity over evolutionary time that is similar to our model for structure-based selection. In our second investigation, we introduce a bidirectional coupling between a phase synchronization model and a cascade model to produce our `sync-contagion' model. The sync-contagion model is well-suited to describe a system in which a contagious signal alerts individuals to realign their orientations, where `orientation' can be in the literal sense (such as a school of fish escaping the threat of a predator) or a more abstract sense (such as a `political orientation' that changes in response to a hot topic). We find that success in realigning the population towards some desired target orientation depends on the relative strengths of contagion spread and synchronization coupling. In our third and final investigation, we attempt to forecast the complex infection dynamics of the COVID-19 pandemic through a data-driven reservoir computing approach. We focus our attention on forecasting case numbers in the United States at the national and state levels. Despite producing adequate short-term predictions, we find that a simple reservoir computing approach does not perform significantly better than a linear extrapolation. The biggest challenge is the lack of data quantity normally required for machine learning success. We discuss methods to augment our limited data, such as through a `library-based' method or a hybrid modeling approach.Item An Agent-Based Modeling Approach to Reducing Pathogenic Transmission in Medical Facilities and Community Populations(2012) Barnes, Sean; Golden, Bruce; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The spread of infectious diseases is a significant and ongoing problem in human populations. In hospitals, the cost of patients acquiring infections causes many downstream effects, including longer lengths of stay for patients, higher costs, and unexpected fatalities. Outbreaks in community populations cause more significant problems because they stress the medical facilities that need to accommodate large numbers of infected patients, and they can lead to the closing of schools and businesses. In addition, epidemics often require logistical considerations such as where to locate clinics or how to optimize the distribution of vaccinations and food supplies. Traditionally, mathematical modeling is used to explore transmission dynamics and evaluate potential infection control measures. This methodology, although simple to implement and computationally efficient, has several shortcomings that prevent it from adequately representing some of the most critical aspects of disease transmission. Specifically, mathematical modeling can only represent groups of individuals in a homogenous manner and cannot model how transmission is affected by the behavior of individuals and the structure of their interactions. Agent-based modeling and social network analysis are two increasingly popular methods that are well-suited to modeling the spread of infectious diseases. Together, they can be used to model individuals with unique characteristics, behavior, and levels of interaction with other individuals. These advantages enable a more realistic representation of transmission dynamics and a much greater ability to provide insight to questions of interest for infection control practitioners. This dissertation presents several agent-based models and network models of the transmission of infectious diseases at scales ranging from hospitals to networks of medical facilities and community populations. By employing these methods, we can explore how the behavior of individual healthcare workers and the structure of a network of patients or healthcare facilities can affect the rate and extent of hospital-acquired infections. After the transmission dynamics are properly characterized, we can then attempt to differentiate between different types of transmission and assess the effectiveness of infection control measures.