Investigating HIV Spreading Mechanisms and Policy Alternatives in Russia: A System Dynamics Modeling Approach

Master's Thesis in System DynamicsGEO-SD351MASV-SYSDYINTL-KMDINTL-SVINTL-MNINTL-PSYKINTL-HFINTL-MEDINTL-JU

DECISION-ANALYTIC MODELS USING REINFORCEMENT LEARNING TO INFORM DYNAMIC SEQUENTIAL DECISIONS IN PUBLIC POLICY

We developed decision-analytic models specifically suited for long-term sequential decision-making in the context of large-scale dynamic stochastic systems, focusing on public policy investment decisions. We found that while machine learning and artificial intelligence algorithms provide the most suitable frameworks for such analyses, multiple challenges arise in its successful adaptation. We address three specific challenges in two public sectors, public health and climate policy, through the following three essays. In Essay I, we developed a reinforcement learning (RL) model to identify optimal sequence of testing and retention-in-care interventions to inform the national strategic plan “Ending the HIV Epidemic in the US”. The large dimensions of the solution space along with the computational complexity of the simulations over long analytic horizons create compounding computational challenges not suitable for solution algorithms. We show that reformulation of the problem by solving for proxy decision-metrics significantly reduces the solution space and ensures convergence to optimality. In Essay II, we developed a deep RL decision-analytic model for effective early control of infectious disease outbreaks, focusing on new or emerging outbreaks that do not yet have pharmaceutical intervention options. Using the COVID-19 pandemic as a test case, we evaluated the question of whether a lockdown is necessary, and if so, when it should be initiated, to what level (proportion lockdown), and how this should change over time, such that it minimizes both epidemic and economic burdens. A key component of this problem is decisions are jurisdictional, i.e., limited in geographical authority, but occurring in interacting environments, i.e., actions of one jurisdiction can influence the epidemic in other jurisdictions. We evaluated the above question in the context of two-geographical jurisdictions which make autonomous, independent decisions, cooperatively or non-cooperatively, but interact in the same environment through travel. In Essay III, focusing on the climate policy sector, we defined a cost-effectiveness metric called Levelized Cost of Carbon (LCC) that carefully accounts for the time-value of money and the time-value of emissions reduction. This metric is a simple tool that local government agencies can use to evaluate climate change projects alongside other issues they may face, such as safety, congestion, pollution, and political considerations. We also investigated the theoretical and practical implications and limitations of using a cost-effectiveness metric as an approach to rank projects

Applications of simulation within the healthcare context

This is a pre-print of an article published in Journal of the Operation Research Society. The definitive publisher-authenticated version Katsaliaki, K., Mustafee, N.,(2010). Applications of simulation within the healthcare context. Journal of the Operation Research Society. 62, 1431-1451 is available online at: http://www.palgrave-journals.com/jors/journal/v62/n8/full/jors201020a.htmlA large number of studies have applied simulation to a multitude of issues related to healthcare. These studies have been published over a number of unrelated publishing outlets, and this may hamper the widespread reference and use of such resources. In this paper we analyse existing research in healthcare simulation in order to categorise and synthesise it in a meaningful manner. Hence, the aim of this paper is to conduct a review of the literature pertaining to simulation research within healthcare in order to ascertain its current development. A review of approximately 250 high quality journal papers published between 1970 and 2007 on healthcare-related simulation research was conducted. The results present: a classification of the healthcare publications according to the simulation techniques they employ; the impact of published literature in healthcare simulation; a report on demonstration and implementation of the studies’ results; the sources of funding; and the software used. Healthcare planners and researchers will benefit from this study by having ready access to an indicative article collection of simulation techniques applied in healthcare problems that are clustered under meaningful headings. This study facilitates the understanding of the potential of different simulation techniques for solving diverse healthcare problems

Report on DIMACS Working Group Meeting: Mathematical Sciences Methods for the Study of Deliberate Releases of Biological Agents and their Consequences

55 pages, 1 article*Report on DIMACS Working Group Meeting: Mathematical Sciences Methods for the Study of Deliberate Releases of Biological Agents and their Consequences* (Castillo-Chavez, Carlos; Roberts, Fred S.) 55 page

Ready or Not? Protecting the Public's Health From Diseases, Disasters, and Bioterrorism, 2011

Highlights examples of preparedness programs and capacities at risk of federal budget cuts or elimination, examines state and local public health budget cuts, reviews ten years of progress and shortfalls, and outlines policy issues and recommendations

TB STIGMA – MEASUREMENT GUIDANCE

TB is the most deadly infectious disease in the world, and stigma continues to play a significant role in worsening the epidemic. Stigma and discrimination not only stop people from seeking care but also make it more difficult for those on treatment to continue, both of which make the disease more difficult to treat in the long-term and mean those infected are more likely to transmit the disease to those around them. TB Stigma – Measurement Guidance is a manual to help generate enough information about stigma issues to design and monitor and evaluate efforts to reduce TB stigma. It can help in planning TB stigma baseline measurements and monitoring trends to capture the outcomes of TB stigma reduction efforts. This manual is designed for health workers, professional or management staff, people who advocate for those with TB, and all who need to understand and respond to TB stigma

Outbreaks: Protecting Americans From Infectious Diseases 2014

This report examines a range of infectious disease concerns. The report highlights a series of 10 indicators in each state that, taken collectively, offer a composite snapshot of strengths and vulnerabilities across the health system. These indicators help illustrate the types of policy fundamentals that are important to have in place not just to prevent the spread of disease in the first place but also to detect, diagnose and respond to outbreaks. In addition, the report examines key areas of concern in the nation's ability to prevent and control infectious diseases and offers recommendations for addressing these gaps. The Outbreaks report provides the public, policymakers and a broad and diverse set of groups involved in public health and the healthcare system with an objective, nonpartisan, independent analysis of the status of infectious disease policies; encourages greater transparency and accountability of the system; and recommends ways to assure the public health and healthcare systems meet today's needs and work across borders to accomplish their goals

DATA ANALYTICS FOR CRISIS MANAGEMENT: A CASE STUDY OF SHARING ECONOMY SERVICES IN THE COVID-19 PANDEMIC

This dissertation study aims to analyze the role of data-driven decision-making in sharing economy during the COVID-19 pandemic as a crisis management tool. In the twenty-first century, when applying analytical tools has become an essential component of business decision-making, including operations on crisis management, data analytics is an emerging field. To carry out corporate strategies, data-driven decision-making is seen as a crucial component of business operations. Data analytics can be applied to benefit-cost evaluations, strategy planning, client engagement, and service quality. Data forecasting can also be used to keep an eye on business operations and foresee potential risks. Risk Management and planning are essential for allocating the necessary resources with minimal cost and time and to be ready for a crisis. Hidden market trends and customer preferences can help companies make knowledgeable business decisions during crises and recessions. Each company should manage operations and response during emergencies, a path to recovery, and prepare for future similar events with appropriate data management tools. Sharing economy is part of social commerce, that brings together individuals who have underused assets and who want to rent those assets short-term. COVID-19 has emphasized the need for digital transformation. Since the pandemic began, the sharing economy has been facing challenges, while market demand dropped significantly. Shelter-in-Place and Stay-at-Home orders changed the way of offering such sharing services. Stricter safety procedures and the need for a strong balance sheet are the key take points to surviving during this difficult health crisis. Predictive analytics and peer-reviewed articles are used to assess the pandemic\u27s effects. The approaches chosen to assess the research objectives and the research questions are the predictive financial performance of Uber & Airbnb, bibliographic coupling, and keyword occurrence analyses of peer-reviewed works about the influence of data analytics on the sharing economy. The VOSViewer Bibliometric software program is utilized for computing bibliometric analysis, RapidMiner Predictive Data Analytics for computing data analytics, and LucidChart for visualizing data