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Master of Science / Department of Mechanical and Nuclear Engineering / Mohammad H. Hosni / Byron W. Jones / In 2010, over 786 million passengers flew on commercial flights in the United States according to the Bureau of Transportation Statistics (2011). With the average flight length over 1300 miles for domestic flights, this amounts to billions of hours spent aboard airliners by passengers each year. During these flights, diseases and other harmful contaminates, some malicious, can spread throughout aircraft cabins, harming passengers. Aircraft ventilation systems are designed to remove these harmful contaminates as quickly as possible to minimize spread in cabin air. Disruptions to the design airflow pattern can hinder the effectiveness of contamination removal efforts. A common form of this airflow disruption is longitudinal air movement through cabin aisles. To examine the effect of contaminate transport down aircraft aisles by a moving body, a motorized beverage cart is past by a contamination source as it traverses the length of the cabin aisle. An experimental study is performed in a mockup Boeing 767 cabin section consisting of eleven rows with seven seats per row. Carbon Dioxide (CO2) tracer gas is injected at a constant flow rate at a location of interest until concentrations in the cabin reach steady state. Ventilation equipment and flow rates representative of an actual aircraft are used for all experiments. Seats in the mockup are occupied by thermal manikins to simulate passenger heat load. A motorized beverage cart traverses the length of the cabin aisle passing by the injection location. The concentrations of tracer gas displaced by the cart are measured at locations throughout the cabin. Comparing these measurements to baseline readings taken with no cart movement, a map of the degree to which contaminant transport is affected by the beverage cart is calculated. The cabin mockup is supplied by 100% outdoor air through actual Boeing supply ductwork and linear diffusers along the cabin length above the aisles. The CO2 level is measured in the inlet air, measurement locations in the cabin, and exhaust air using nondispersive infrared (NDIR) sensors. Measured results are reported for all (54) seat locations downstream of the cart traverse/injection location for an injection location near the rear of the cabin. Analogous measurements are also conducted examining the effect of variations in cart speed and modified injection location. It was found the beverage cart movement had an effect of up to a 35% increase in tracer gas concentration relative to the local steady state concentration for several seat locations adjacent to the aisle. This increased concentration continued for only a few minutes in all cases, but was generally less than the steady state exposure one row closer to the injection location. Moving in the lateral direction away from the aisle, the variance in tracer gas concentration due to the cart movement diminished quickly. The significance of increased concentration for such short periods of time in comparison to the length of actual commercial flights may require further biological analysis. The data showed general tracer gas concentration increases due to cart movement in a small section of the cabin mockup which could warrant further analysis, but increases were generally insignificant when considering entire flight contamination exposure levels.

abstract: Land-use change has arguably been the largest contributor to the emergence of novel zoonotic diseases within the past century. However, the relationship between patterns of land-use change and the resulting landscape configuration on disease spread is poorly understood as current cross-species disease transmission models have not adequately incorporated spatial features of habitats. Furthermore, mathematical-epidemiological studies have not considered the role that land-use change plays in disease transmission throughout an ecosystem. This dissertation models how a landscape's configuration, examining the amount and shape of habitat overlap, contributes to cross-species disease transmission to determine the role that land-use change has on the spread of infectious diseases. To approach this, an epidemiological model of transmission between a domesticated and a wild species is constructed. Each species is homogeneously mixed in its respective habitat and heterogeneously mixed in the habitat overlap, where cross-species transmission occurs. Habitat overlap is modeled using landscape ecology metrics. This general framework is then applied to brucellosis transmission between elk and cattle in the Greater Yellowstone Ecosystem. The application of the general framework allows for the exploration of how land-use change has contributed to brucellosis prevalence in these two species, and how land management can be utilized to control disease transmission. This model is then extended to include a third species, bison, in order to provide insight to the indirect consequences of disease transmission for a species that is situated on land that has not been converted. The results of this study can ultimately help stakeholders develop policy for controlling brucellosis transmission between livestock, elk, and bison, and in turn, could lead to less disease prevalence, reduce associated costs, and assist in population management. This research contributes novelty by combining landscape ecology metrics with theoretical epidemiological models to understand how the shape, size, and distribution of habitat fragments on a landscape affect cross-species disease transmission. The general framework demonstrates how habitat edge in single patch impacts cross-species disease transmission. The application to brucellosis transmission in the Greater Yellowstone Ecosystem between elk, cattle, and bison is original research that enhances understanding of how land conversion is associated with enzootic disease spread. / Dissertation/Thesis / Doctoral Dissertation Applied Mathematics for the Life and Social Sciences 2020

The Scottish salmon industry is facing challenges in the control of aquatic infectious disease, as is the case in other countries such as Chile and Norway. Disease outbreaks can have an enormous economic impact and possibly affect wild fish populations. Disease transmission in an aquatic environment is complex and there are several transmission routes (vertical transmission, natural reservoirs, hydrodynamic transmission and long-distance movements). Effective control methods such as vaccines are not available in all cases and therefore disease prevention remains a priority. In livestock, epidemiological network models have been proven to be a highly useful tool to investigate the role of different transmission routes on the course of epidemics and have the potential to provide the same utility for aquatic networks. Understanding the complex contact network will result in more effective disease prevention, surveillance systems and control strategies. The aim of this thesis was to investigate the Scottish live fish movement network and its consequences for pathogen transmission between farms in order to develop and optimize control strategies for epidemics. The main objective of chapter 3 was to investigate the effect of different fallowing strategies on the spread of diseases with different transmission properties. A network model was constructed that included both local transmission and long-distance transmission. The basic structure of this network was a ring model where neighbours within a management area could infect each other and non-local transmission occurred at random. The results showed that when long-distance transmission was under reasonable control in comparison with local transmission risk, synchronized fallowing at the management area level was potentially a highly effective tool in the control of infectious diseases. Chapter 4 presents a detailed description of the number of live fish movements and their timing for Atlantic salmon (Salmo salar) in Scottish aquaculture. For this, movement records from 2002 to 2004 were provided by Marine Scotland, Aberdeen. Salmon are anadromous and have a freshwater (FW) and seawater phase (SW). Scottish live fish movements can be divided in FW-FW, FW-SW, SW-SW, SW-FW and “other” movements. The latter are mainly movements from and to research sites. This study showed that the contact structure and timing of live fish movements are seasonal and differ largely between production phases. Disease control measures should take these differences into account to optimize their strategies. In chapter 4, live fish movements were shown to be seasonal; therefore in chapter 5 the main aim was to quantify the effects of seasonality of live fish movements on the course of epidemics. The results showed that the sequence of salmon movements is important for the course of an epidemic. Seasonality is important when local transmission is higher than 0.05 per contact per week and when the movements are not clustered and when movements do not occur in a specific order based on the specific assumptions made in this model. In conclusion, this thesis described the complex live fish movement structure of salmon in Scotland and showed that biosecurity in SW farms is good but could be further improved if all management areas apply synchronized fallowing. The results of this study suggest that biosecurity between freshwater sites could be improved by the application of a system similar to management areas in SW farms.

Simulation of animal disease spread is essential for understanding and controlling the outbreak of disease among herds of livestock (in particular cattle and poultry). Using a computerized system or simulator, animal health professionals or epidemiologists often spend many hours determining the set of input parameters that most accurately represent a disease spread or an outbreak scenario. A parameter can be a simple boolean value, or a scientific or often hypothetically derived range of real numbers. Many times, an epidemiologist chooses a value provisionally in a random fashion and repeats the simulation until a viable solution is achieved. This tedious process is inefficient and lengthy. To assist and improve this laborious practice in a concise and timely manner, a Genetic Algorithm is employed to determine a population based solution consisting of input parameters using the North American Animal Disease Spread Model (NAADSM).

Background: With the global decline of honey bee populations, safeguarding these vital pollinators has become crucial. Varroa destructor mites are a primary threat, weakening bees and facilitating the spread of diseases, which can decimate colonies and disrupt ecosystems. This thesis investigates the application of a Wireless sensor network (WSN) for the monitoring and control of varroasis spread within bee colonies across large geographical areas. Objectives: The main objective of this research is to develop an integrated method that combines biological insights into varroasis with WSN functionalities for real-time disease monitoring and control. By doing so, the study aims to contribute to the development of a scalable and sustainable approach to apiculture and disease management. Methods: A multi-phase methodological approach was employed, encompassing the modelling of biological phenomena, formulation of WSN functionalities, and the design of a scalable WSN architecture. Simulation studies were conducted, followed by the development of a theoretical framework to support the practical application of the proposed WSN system. A key aspect of the methodology is the introduction of energy estimation models to evaluate the operational feasibility of the WSN. Results: The results indicate that the WSN is capable of dynamically adjusting its monitoring rate in response to changes in infection dynamics, effectively identifying and managing varroa mite populations. The system demonstrated adaptability to various infection rates, with the potential to improve the timely and targeted treatment of infested colonies. Energy consumption data further affirms the operational viability of the WSN. Conclusions: The study concludes that integrating WSNs with biological models is a viable solution for the real-time monitoring and management of varroasis. The proposed WSN system holds promise for enhancing the health and productivity of bee colonies on a broad scale, offering a novel contribution to the fields of apiculture and environmental monitoring.

<p dir="ltr">Antigenically variable pathogens (AVPs) pose a significant infectious disease burden, but vaccine development is extremely difficult due to their ability to quickly evolve beyond host immunity. Existing models of AVP spread have not been able to sufficiently account for host immune history, population mobility patterns, and pathogen evolutionary dynamics. This thesis aims at creating a computational framework built from the concept of antibody landscapes to overcome these issues, thereby increasing the understanding of how these pathogens spread and evolve in order to improve vaccine design.</p><p><br></p><p dir="ltr">Briefly, the proposed stochastic framework is built from "the ground up'' using principles of antibody landscapes, in which we begin by devising a mechanism to describe how the landscape changes due to repeated pathogen exposure. Extending this to a (sub)population-level permits integration into a meta-population model that is further parameterized by geographic influences. Virus evolution is driven by a statistically meaningful model of antigenic drift in the underlying antigenic space. While the framework is robust and, in principle, capable of modeling a variety of AVPs, we focus on influenza H3N2 as a case study due to its data availability and persistently low and unpredictable vaccine efficacy.</p><p><br></p><p dir="ltr">Experimental results demonstrate that we can statistically significantly predict various properties of H3N2 evolution and population level immunity, including prevalence level, the timing of emergence of new antigenic clusters, the positions of unseen strains in antigenic space, as well as the geographic locations where new strains and antigenic clusters emerge. Through analysis of the simulated outcomes, we identified a population level of immune protection against circulating strains (titre value of approximately 5 units), which when approached, seems to signal an upcoming antigenic drift. Using this insight, we propose a new vaccine strain selection strategy that shows notable improvements in vaccine effectiveness and stability. Additionally, we estimate that it could reduce annual morbidity by 73.4 ± 40.8 million (17% ± 9%) in the Northern Hemisphere and 56.7 ± 38.0 million (10% ± 6%) in the Southern Hemisphere. In summary, this novel framework can accurately replicate the interplay between pathogen evolution and population-level immune responses decades into the future from a mechanistic perspective, and be used to design improved vaccines.</p>

In spite of the remarkable development of modern medical treatment and technology, the threat of pandemic diseases such as anthrax, cholera, and SARS has not disappeared. As a part of emerging healthcare decision problems, many researchers have studied how to detect and contain disease outbreaks, and our research is aligned with this trend. This thesis mainly consists of two parts: epidemic simulation modeling for effective intervention strategies and spatiotemporal monitoring for outbreak detection. We developed a stochastic epidemic simulation model of a pandemic influenza virus (H1N1) to test possible interventions within a structured population. The possible interventions — such as vaccination, antiviral treatment, household prophylaxis, school closure and social distancing — are investigated in a large number of scenarios, including delays in vaccine delivery and low and moderate efficacy of the vaccine. Since timely and accurate detection of a disease outbreak is crucial in terms of preparation for emergencies in healthcare and biosurveillance, we suggest two spatiotemporal monitoring charts, namely, the SMCUSUM and RMCUSUM charts, to detect increases in the rate or count of disease incidents. Our research includes convenient methods to approximate the control limits of the charts. An analytical control limit approximation method for the SMCUSUM chart performs well under certain conditions on the data distribution and monitoring range. Another control limit approximation method for the RMCUSUM chart provides robust performance to various monitoring range, spatial correlation structures, and data distributions without intensive modeling of the underlying process.