Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies

Pandemic influenza has the epidemic potential to kill millions of people. While various preventive measures exist (i.a., vaccination and school closures), deciding on strategies that lead to their most effective and efficient use remains challenging. To this end, individual-based epidemiological models are essential to assist decision makers in determining the best strategy to curb epidemic spread. However, individual-based models are computationally intensive and it is therefore pivotal to identify the optimal strategy using a minimal amount of model evaluations. Additionally, as epidemiological modeling experiments need to be planned, a computational budget needs to be specified a priori. Consequently, we present a new sampling technique to optimize the evaluation of preventive strategies using fixed budget best-arm identification algorithms. We use epidemiological modeling theory to derive knowledge about the reward distribution which we exploit using Bayesian best-arm identification algorithms (i.e., Top-two Thompson sampling and BayesGap). We evaluate these algorithms in a realistic experimental setting and demonstrate that it is possible to identify the optimal strategy using only a limited number of model evaluations, i.e., 2-to-3 times faster compared to the uniform sampling method, the predominant technique used for epidemiological decision making in the literature. Finally, we contribute and evaluate a statistic for Top-two Thompson sampling to inform the decision makers about the confidence of an arm recommendation

Design Considerations and Data Communication Architecture for National Animal Identification and Traceability System in Nigeria

Wireless communication systems and their supporting infrastructure continue to play a vital role in contemporary daily activities. Due to the unprecedented levels of interconnectivity achieved between wireless devices in recent times, new insights and paradigms for the robust deployment and better utilization of wireless communication systems are always of interest to many countries for socio-economic development. Present-day Nigeria is faced with the challenge of insurgencies whose financing has been linked to proceeds from livestock theft or rustling according to many scholarly works and news reports. To mitigate rustling and the sales of stolen livestock via identification and traceability from ‘herds to markets to homes’, the design considerations and data communication architecture for national animal identification and traceability system in Nigeria (NAITS) is proposed in this paper for safer and improved livestock farming and production. Particularly, technical insight into the co-use of radio frequency identification (RFID) and fifth-generation new radio (5G NR) technologies for the implementation of NAITS are highlighted and discussed in this paper for a prospective technological policy plan and development in Nigeria

How Technology Impacts and Compares to Humans in Socially Consequential Arenas

One of the main promises of technology development is for it to be adopted by people, organizations, societies, and governments -- incorporated into their life, work stream, or processes. Often, this is socially beneficial as it automates mundane tasks, frees up more time for other more important things, or otherwise improves the lives of those who use the technology. However, these beneficial results do not apply in every scenario and may not impact everyone in a system the same way. Sometimes a technology is developed which produces both benefits and inflicts some harm. These harms may come at a higher cost to some people than others, raising the question: {\it how are benefits and harms weighed when deciding if and how a socially consequential technology gets developed?} The most natural way to answer this question, and in fact how people first approach it, is to compare the new technology to what used to exist. As such, in this work, I make comparative analyses between humans and machines in three scenarios and seek to understand how sentiment about a technology, performance of that technology, and the impacts of that technology combine to influence how one decides to answer my main research question.Comment: Doctoral thesis proposal. arXiv admin note: substantial text overlap with arXiv:2110.08396, arXiv:2108.12508, arXiv:2006.1262

Experimenting with sequential allocation procedures

In experiments that consider the use of subjects, a crucial part is deciding which treatment to allocate to which subject – in other words, constructing the treatment allocation procedure. In a classical experiment, this treatment allocation procedure often simply constitutes randomly assigning subjects to a number of different treatments. Subsequently, when all outcomes have been observed, the resulting data is used to conduct an analysis that is specified a priori. Practically, however, the subjects often arrive at an experiment one-by-one. This allows the data generating process to be viewed differently: instead of considering the subjects in a batch, intermediate data from previous interactions with other subjects can be used to influence the decisions of the treatment allocation in future interactions. A heavily researched formalization that helps developing strategies for sequentially allocating subjects is the multi-armed bandit problem. In this thesis, several methods are developed to expedite the use of sequential allocation procedures by (social) scientists in field experiments. This is done by building upon the extensive literature of the multi-armed bandit problem. The thesis also introduces and shows many (empirical) examples of the usefulness and applicability of sequential allocation procedures in practice

A chaos theory approach to understanding the impact of the COVID-19 pandemic on tourism businesses in Plateau State, Nigeria

Geography and Environmental Studie

African Handbook of Climate Change Adaptation

This open access book discusses current thinking and presents the main issues and challenges associated with climate change in Africa. It introduces evidences from studies and projects which show how climate change adaptation is being - and may continue to be successfully implemented in African countries. Thanks to its scope and wide range of themes surrounding climate change, the ambition is that this book will be a lead publication on the topic, which may be regularly updated and hence capture further works. Climate change is a major global challenge. However, some geographical regions are more severly affected than others. One of these regions is the African continent. Due to a combination of unfavourable socio-economic and meteorological conditions, African countries are particularly vulnerable to climate change and its impacts. The recently released IPCC special report "Global Warming of 1.5º C" outlines the fact that keeping global warming by the level of 1.5º C is possible, but also suggested that an increase by 2º C could lead to crises with crops (agriculture fed by rain could drop by 50% in some African countries by 2020) and livestock production, could damage water supplies and pose an additonal threat to coastal areas. The 5th Assessment Report produced by IPCC predicts that wheat may disappear from Africa by 2080, and that maize— a staple—will fall significantly in southern Africa. Also, arid and semi-arid lands are likely to increase by up to 8%, with severe ramifications for livelihoods, poverty eradication and meeting the SDGs. Pursuing appropriate adaptation strategies is thus vital, in order to address the current and future challenges posed by a changing climate. It is against this background that the "African Handbook of Climate Change Adaptation" is being published. It contains papers prepared by scholars, representatives from social movements, practitioners and members of governmental agencies, undertaking research and/or executing climate change projects in Africa, and working with communities across the African continent. Encompassing over 100 contribtions from across Africa, it is the most comprehensive publication on climate change adaptation in Africa ever produced

Countering Terrorism on Tomorrow’s Battlefield: Critical Infrastructure Security and Resiliency (NATO COE-DAT Handbook 2)

Every day, malicious actors target emerging technologies and medical resilience or seek to wreak havoc in the wake of disasters brought on by climate change, energy insecurity, and supply-chain disruptions. Countering Terrorism on Tomorrow’s Battlefield is a handbook on how to strengthen critical infrastructure resilience in an era of emerging threats. The counterterrorism research produced for this volume is in alignment with NATO’s Warfighting Capstone Concept, which details how NATO Allies can transform and maintain their advantage despite new threats for the next two decades. The topics are rooted in NATO’s Seven Baseline requirements, which set the standard for enhancing resilience in every aspect of critical infrastructure and civil society. As terrorists hone their skills to operate lethal drones, use biometric data to target innocents, and take advantage of the chaos left by pandemics and natural disasters for nefarious purposes, NATO forces must be prepared to respond and prevent terrorist events before they happen. Big-data analytics provides potential for NATO states to receive early warning to prevent pandemics, cyberattacks, and kinetic attacks. NATO is perfecting drone operations through interoperability exercises, and space is being exploited by adversaries. Hypersonic weapons are actively being used on the battlefield, and satellites have been targeted to take down wind farms and control navigation. This handbook is a guide for the future, providing actionable information and recommendations to keep our democracies safe today and in the years to come.https://press.armywarcollege.edu/monographs/1953/thumbnail.jp

New algorithms for solving high-dimensional time-dependent optimal control problems and their applications in infectious disease models

Doctor of PhilosophyDepartment of Industrial & Manufacturing Systems EngineeringChih-Hang 'John' WuInfectious diseases have been the primary cause of human death worldwide nowadays. The optimal control strategy for infectious disease has attracted increasing attention, becoming a significant issue in the healthcare domain. Optimal control of diseases can affect the progression of diseases and achieve high-quality healthcare. In previous studies, massive efforts on the optimal control of diseases have been made. However, some infectious diseases' mortality is still high and even developed into the second highest cause of mortality in the US. According to the limitations in existing research, this research aims to study the optimal control strategy via some industrial engineering techniques such as mathematical modeling, optimization algorithm, analysis, and numerical simulation. To better understand the optimal control strategy, two infectious disease models (epidemic disease, sepsis) are studied. Complex nonlinear time-series and high-dimensional infectious disease control models are developed to study the transmission and optimal control of deterministic SEIR or stochastic SIS epidemic diseases. In addition, a stochastic sepsis control model is introduced to study the progression and optimal control for sepsis system considering possible medical measurement errors or system uncertainty. Moreover, an improved complex nonlinear sepsis model is presented to more accurately study the sepsis progression and optimal control for sepsis system. In this dissertation, some analysis methods such as stability analysis, bifurcation analysis, and sensitivity analysis are utilized to help reader better understand the model behavior and the effectiveness of the optimal control. The significant contributions of this dissertation are developing or improving nonlinear complex disease optimal control models and proposing several effective and efficient optimization algorithms to solve the optimal control in those researched disease models, such as an optimization algorithm combining machine learning (EBOC), an improved Bayesian Optimization algorithm (IBO algorithm), a novel high-dimensional Bayesian Optimization algorithm combining dimension reduction and dimension fill-in (DR-DF BO algorithm), and a high-dimensional Bayesian Optimization algorithm combining Recurrent Neural Network (RNN-BO algorithm). Those algorithms can solve the optimal control solution for complex nonlinear time-series and high-dimensional systems. On top of that, numerical simulation is used to demonstrate the effectiveness and efficiency of the proposed algorithms