No description available.

Due to an increased amount of applications that can be modeled as large-scale, there has been growing interest in using simple methods for optimization that require low iteration cost as well as limited memory storage. We will be concerned with optimization problems for networked systems with high dimensions, focusing on applications in crowdsourcing and pandemic control. What makes these problems complex is that the objectives relate to aspects of the evolution of the dynamics of the system. We develop first-order optimization methods with low iteration complexity for such applications in this dissertation work. In the first part of this work, we consider the adversarial crowdsourcing problem. We reduce this problem to the robust rank-one matrix completion problem, and we propose a new first-order algorithm with theoretical guarantees. These results are then applied to the problem of classification from crowdsourced data under the assumption that while the majority of the workers are governed by the standard single-coin David-Skene model, some of the workers can deviate arbitrarily from this model. Extensive experimental results show our algorithm outperforms all other state-of-the-art methods in such an adversarial scenario. In the second part of the work, we consider the optimal lockdown problem for pandemic control. As a common strategy of contagious disease containment, lockdowns will inevitably weaken the economy. Here we propose a mathematical framework with first-order methods to achieve pandemic control through an optimal stabilizing non-uniform lockdown, where our goal is to reduce the economic activity as little as possible while decreasing the number of infected individuals at a prescribed rate. We demonstrate the power of this framework by analyzing a model of COVID-19 spread in the 62 counties of New York State. We find that an optimal stabilizing lockdown based on epidemic status in April 2020 would have reduced economic activity more stringently outside of New York City compared to within it, even though the epidemic was much more prevalent in New York City at that point. In the third part of the work, we consider the optimal vaccine allocation issue for pandemic control, where our goal is to send the infections to zero as soon as possible with a fixed number of vaccines. To achieve this, we propose a mathematical framework for classical epidemic models as well as a COVID-19 model. Moreover, we also analyzed the epidemic model used in [Bubar et al., 2021], and compared our method with the strategies in [Bubar et al., 2021]. We found that it is better to offer vaccines to younger people when the basic reproduction number R0 is moderately above one.

Thesis (M.A.)--University of Missouri-Columbia, 2006. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (February 5, 2007) Includes bibliographical references.

No description available.

No description available.

No description available.

Master of Science / Department of Electrical and Computer Engineering / Caterina Maria Scoglio / Japanese Encephalitis (JE) is a vector-borne disease transmitted by mosquitoes and maintained in birds and pigs. An interconnected network model is proposed to examine the possible epidemiology of JE in the USA. Proposed JE model is an individual-level network model that explicitly considers the feral pig population and implicitly considers mosquitoes and birds in specific areas of Florida, North Carolina, and South Carolina. The virus transmission among feral pigs within a small geographic area (<60 sq mi areas) are modeled using two network topologies— fully connected and Erdos-Renyi networks. Connections between locations situated in different states (interstate links) are created with limited probability and based on fall and spring bird migration patterns. Simulation results obtained from the network models support the use of the Erdos-Renyi network because maximum incidence occurs during the fall migration period which is similar to the peak incidence of the closely related West Nile virus (WNV), another virus in the Japanese Encephalitis group (Flaviviridae) that is transmitted by both birds and mosquitoes. Simulation analysis suggested two important mitigation strategies: for low mosquito vectorial capacity, insecticidal spraying of infected areas reduces transmission and limits the outbreak to a single geographic area. Alternatively, in high mosquito vectorial capacity areas, birds rather than mosquitoes need to be removed/controlled.

Despite the array of medical advances of our modern day society, infectious diseases still plague millions of people worldwide. Malaria, in particular, causes substantial suffering and death throughout both developed and developing countries. Aside from the socioeconomic challenges presented by the disease's prevalence in impoverished nations, one of the major difficulties scientists have encountered while attempting to eradicate the disease is the parasite's ability to become resistant to new drugs and methods of treatment. In an effort to better understand the dynamics of malaria, we analyze a mathematical model that accounts for both the treatment aspect as well as the drug resistance that accompanies it. Simulations demonstrating the effects of treatment rates and the level of resistance are studied and discussed in hopes of shedding additional light on the characteristics of this devastating epidemic.

Master of Science / Department of Electrical and Computer Engineering / Karen Garrett / Bala Natarajan / Caterina Scoglio / In this thesis, we propose a statistical model to predict disease dispersal in dynamic networks. We model the process of disease spreading using discrete time Markov chain. In this case, the vector of probability of infection is the state vector and every element of the state vector is a continuous variable between zero and one. In discrete time Markov chains, state probability vectors in each time step depends on state probability vector in the previous time step and one step transition probability matrix. The transition probability matrix can be time variant or time invariant. If this matrix’s elements are functions of elements of vector state probability in previous step, the corresponding Markov chain is non linear dynamical system. However, if those elements are independent of vector state probability, the corresponding Markov chain is a linear dynamical system. We especially focus on the dispersal of soybean rust. In our problem, we have a network of US counties and we aim at predicting that which counties are more likely to get infected by soybean rust during a year based on observations of soybean rust up to that time as well as corresponding observations to previous years. Other data such as soybean and kudzu densities in each county, daily wind data, and distance between counties helps us to build the model. The rapid growth in the number of Internet users in recent years has led malware generators to exploit this potential to attack computer users around the word. Internet users are frequent targets of malicious software every day. The ability of malware to exploit the infrastructures of networks for propagation determines how detrimental they can be to the network’s security. Malicious software can make large outbreaks if they are able to exploit the structure of the Internet and interactions between users to propagate. Epidemics typically start with some initial infected nodes. Infected nodes can cause their healthy neighbors to become infected with some probability. With time and in some cases with external intervention, infected nodes can be cured and go back to a healthy state. The study of epidemic dispersals on networks aims at explaining how epidemics evolve and spread in networks. One of the most interesting questions regarding an epidemic spread in a network is whether the epidemic dies out or results in a massive outbreak. Epidemic threshold is a parameter that addresses this question by considering both the network topology and epidemic strength.

This study analyses the influence the HIV/AIDS epidemic has had on the development of public policy in Australia, and evaluates the effect this policy is having on preventing the spread of this disease. According to Waites (1993 p.7) Australia's integrated reaction to the issue of HIV/AIDS has been amongst the finest in the world. This has been due to the cooperation of government health authorities and the first affected groups, especially the gay male population in urban areas. The purpose of this study is to investigate how, and if, this integrated response to policy development occurred, and then discover whether or not the outcome has resulted in the implementation of effective public policy. The method chosen to conduct this study was to apply a variety of models to the research. The epidemic, Posner, political, and the implementation and evaluation models have been developed and applied in order to gain more understanding of a particular public policy process. These models are used as A framework to analyse some of the complexities involved; the intention is to separate out some of the complex interactions. The paradigms chosen to direct this study in policy analysis do not encompass all the aspects, nor provide all the answers, because there are multiple complex political, sociological and economic issues within the HIV/AIDS policy process. The influence of the HIV/AIDS epidemic on the development of public policy in Australia has been remarkable. This has been due to the active responses from government health authorities, the gay lobby, the media, community groups, committees, and specific individuals. These include Neal Blewett a former Federal Minister for Health, a previous Queensland Minister for Health. and Professor Penington. At times there have been divergent opinions, but the integrated Australian approach involved in developing the HIV/AIDS National strategy has ensured that this public policy has received extensive community support Australia wide (National Evaluation Steering Committee 1993 p.83). The effect of the National Strategy and associated public policy is difficult to evaluate. The cost of the human and financial resources involved is also hard to justify, due to the unknown factors still involved. Evidence suggests that the HIV/AIDS epidemic has stabilised. Yet no one really knows how many individuals are currently infected with HIV. The future progress of the HIV/AIDS epidemic is somewhat of an enigma, and there is no known cure.