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 THE IMPACT OF MULTIPLE SPATIAL LEVELS OF THE BUILT ENVIRONMENT ON NONMOTORIZED TRAVEL BEHAVIOR AND HEALTH(2019) Mahmoudi, Jina; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Over the past several decades, the primacy of the automobile in American travel culture has led to rising congestion and energy consumption levels, rampant air pollution, sprawled urban designs, pervasiveness of sedentary behaviors and lifestyles, and prevalence of many health problems. Nonmotorized modes of travel such as walking and bicycling are sustainable alternatives to the automobile and suitable remedies to the adverse environmental, economic, and health effects of automobile dependency. As research continues to reveal the many benefits of nonmotorized travel modes, identification of the factors that influence people’s levels of walking and bicycling has become essential in developing transportation planning policies and urban designs that nurture these activities, and thereby promote public health. Among such factors are the built environment characteristics of the place of residence. To date, research on the impact of the built environment on nonmotorized travel behavior has been focused on neighborhood-level factors. Nonetheless, people do not stay within their neighborhoods; they live and work at a regional scale and travel to different places and distances each day to access various destinations. Little is known, however, about the impact of built environment factors at larger scales including those representing the overall built environment of metropolitan areas on nonmotorized travel behavior and health status of residents. Guided by the principles of the ecological model of behavior, this dissertation systematically tests the impact of the built environment at hierarchical spatial scales on nonmotorized travel behavior and health outcomes. Advanced statistical techniques have been employed to develop integrated models allowing comprehensive examination of the complex interrelationships between the built environment, nonmotorized travel, and health. Through inclusion of built environment factors from larger spatial scales, this research sheds light on the overlooked impact of the macro-level built environment on nonmotorized travel and health. The findings indicate that built environment factors at various spatial scales—including the metropolitan area—can influence nonmotorized travel behavior and health outcomes of residents. Thus, to promote walking and bicycling and public health, more effective policies are those that include multilevel built environment and land use interventions and consider the overall physical form of urban areas.Item VENTILATION IMPACT ON AIRBORNE TRANSMISSION OF RESPIRATORY ILLNESS IN STUDENT DORMITORIES(2018) Jenkins, Sara T; Srebric, Jelena; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This work presents a study of the effect of ventilation rates on the bioaerosols that cause upper respiratory illness. A network of 147 sensors was placed in a pair of dormitories on a college campus to measure carbon dioxide concentrations over two semesters. The concentration results served as input into multi-zone ventilation models of the two buildings, which had different heating, ventilation, and air conditioning (HVAC) systems. The dormitory with a central mechanical ventilation system had, as expected, a higher turnover of fresh air compared to the other, which relied on exhaust fans and infiltration. This well-ventilated building also contained far fewer occupants with recorded upper respiratory illness incidence in comparison to the poorly ventilated building. The central ventilation system increased dorm room ventilation rates by 500%, while decreasing respiratory illness incidence by over 85%. Comparative studies have shown similar findings with increased ventilation reducing incidence of upper respiratory illness by an order of magnitude.Item ANALYSIS, QUANTIFICATION AND SIMULATION OF THE RISK FROM AIRBORNE INFLUENZA(2016) YAN, JING; Ehrman, Sheryl H; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Despite the development of effective vaccines, influenza still remains as a global concern. For appropriate public health intervention, it is crucial to accurately determine the routes of transmission. Influenza is believed to have three primary modes of transmission: big droplet, direct contact and aerosol particles. Considerable evidence points to both aerosol and droplet transmission routes as being significant. Because of the limitation of sampling and analysis, the quantitative dynamics of the aerosol mode of transmission are not completely understood. In this dissertation I have characterized the physical and biological collection efficiency of a novel exhaled breath aerosol collector named Gesundheit II (G-II). The device was proven to successfully collect and preserve infectivity with different types of influenza virus. I have also been involved in epidemiological data analysis, experimental quantification and numerical modeling. On experimental quantification, I have been part of a multi-member team that has conducted a study of characterization of respiratory droplets from influenza infected individuals at the University of Maryland campus during the flu seasons of 2012-2013. The exhaled breath was collected with the G-II for accurate quantification of the influenza virus. 218 pairs of fine (< 5 µm) and coarse (≥ 5µm) exhaled breath samples were obtained from 142 subjects and analyzed. The relationship between culturability, coughing frequency, and symptoms were investigated. The high rate of RNA detection and the frequent recovery of influenza virus by culture from fine aerosol samples suggest a contribution of fine particle aerosols in the transmission of influenza. Given these new findings, to understand the risk of influenza infection from these finer droplets, we have modified an existing mathematical risk analysis model and studied the effect of these droplets on subjects in presence or absence of a respiratory protective device (RPD). Two of the major enhancements in our model are (1) the ability to account for subject-to-subject variability over a wide range of age groups and (2) the heterogeneous population was introduced into the model with some infectees or susceptibles not wearing RPDs.Item Exploring Linkages between Travel Behavior and Health with Person-Level Data from Smartphone Applications(2013) Vemulapati, Sapeksha Virinchi; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In the past, scholars have explored different variables and linked them with the individual's travel behavior. This study explores the linkages between an individual's health and his/her everyday travel behavior. In order to capture accurate and comprehensive travel behavior information, a smartphone application is developed that can track user location for long periods without the need of user intervention. Focus is placed on designing the application to have minimum respondent burden and long-standing battery life of the smart device. Subjects are recruited through a web survey designed to collect information about the individual's healthy living habits. Data from the application is regressed against the health measure data acquired from the survey. Results show that active modes of travel are positively associated with the person's general health measures. The feasibility of this platform as a data collection method is highlighted while explaining the limitations due to the sample distribution and size.