A. James Clark School of Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/1654
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item Second Wave Mechanics(2024) Fabbri, Anthony; Herrmann, Jeffrey W; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The COVID-19 pandemic experienced very well-documented "waves" of the virus's progression, which can be analyzed to predict future wave behavior. This thesis describes a data analysis algorithm for analyzing pandemic behavior and other, similar problems. This involves splitting the linear and sinusoidal elements of a pandemic in order to predict the behavior of future "waves" of infection from previous "waves" of infection, creating a very long-term prediction of a pandemic. Common wave shape patterns can also be identified, to predict the pattern of mutations that have recently occurred, but have not become popularly known as yet, to predict the remaining future outcome of the wave. By only considering the patterns in the data that could possibly have acted in tandem to generate the observed results, many false patterns can be eliminated, and, therefore, hidden variables can be estimated to a very high degree of probability. Similar mathematical relationships can reveal hidden variables in other underlying differential equations.Item Modeling Syndromic Surveillance and Outbreaks in Subpopulations(2020) Pettie, Christa; Herrmann, Jeffrey; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This research is motivated by the need to assist resource limited communities by enhancing the use of syndromic surveillance (SyS) systems and data. Public health agencies and academic researchers have developed and implemented SyS systems as a pattern recognition tool to detect a potential disease outbreak using pre-diagnostic data. SyS systems collect data from multiple types of sources: absenteeism records, over the counter medicine sales, chief complaints, web queries, and more. It could be expensive, however, to gather data from every available source; subsequently, gathering information about only some subpopulations may be a desirable option. This raises questions about the differences between subpopulation behavior and which subpopulations’ data would give the earliest, most accurate warning of a disease outbreak. To investigate the feasibility of using subpopulation data, this research will gather and organize SyS data by subpopulation (separated by population characteristics such as age or location) and identify how well the SyS data correlates to the real world disease progression. This research will study SyS how reports of Influenza-like-illness (ILI) in subpopulations represent the disease behavior. The first step of the research process is to understand how SyS is used in environments with varying levels of resources and what gaps are present in SyS modeling techniques. Various modeling techniques and applications are assessed, specifically the Susceptible Infected Recovered “SIR” model and associated modifications of that model. Through data analysis, well correlated subpopulations will be identified and compared to actual disease behavior and SyS data sets. A model referred to as ModSySIR will be presented that uses real world community data ideal for ease of use and implementation in a resource limited community. The highest level research objective is to provide a potential data analysis method and modeling approach to inform decision making for health departments using SyS systems that rely on fewer resources.Item A Public Health Modeling Based Approach to Information Security Quantification(2015) Condon, Edward; Cukier, Michel; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Modeling the occurrence of computer security incidents within a defined population of computers can be used to help understand some of factors contributing to risk and transmission of these incidents among the population. A better understanding of these factors can be used to determine appropriate intervention actions that can be applied to the population, which may also be evaluated through the application of models. Explanatory models attempt to include and account for various primary factors that affect the occurrence of computer security incidents. Models based on observed security incidents may also be used to evaluate interventions even when explanatory models may not exist or may be difficult to formulate or express for a particular incident type. Forecasting models can be used to project the occurrence of incidents in the future and these projections can be compared to actual observations before and after interventions are applied. The following aspects of modeling computer security incidents are explored: (1) the presentation and discussion of adapting some commonly used infectious disease models for modeling the spread of some types of computer security incidents along with applicable intervention actions; (2) an illustration of how these types of models could be applied to making resource allocation decisions regarding intervention efforts; (3) the presentation and comparison of models that can be used for tracking/forecasting security incidents and monitoring the impact of interventions; (4) the presentation of a method for estimating model features and parameter distributions from observed data; and (5) the exploration of some population characteristics and models for evaluating where to focus or target intervention actions. When resources for responding to or preventing computer security incidents are limited or constrained, the ability to accurately model and evaluate intervention actions can be a useful tool for making the most of these resources.