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京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第20893号 / 理博第4345号 / 新制||理||1624(附属図書館) / 京都大学大学院理学研究科物理学・宇宙物理学専攻 / (主査)准教授 篠本 滋, 教授 佐々 真一, 教授 川上 則雄 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

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America is engulfed in an opioid epidemic. Whether this is depicted through the tens of thousands of lives claimed by the number of drug overdoses each year, the unprecedentedly high and increasing rates of opiate abuse, or the broadening demographic profile of the addict, it is clear that the current issue is one that requires serious attention. As informed by the negative attitudes toward drug addiction that have prevailed since the War on Drugs was declared, it is hypothesized that much of the contemporary predicament is a result of this misinformation that did not resolve, but exacerbated the drug crisis. Despite the concurrent emergence of evidence asserting that addiction is a disease, instead, the idea that drug addiction is a failure prevails. As with many brain diseases, drug addiction displays both pathological alterations in the transmission of signals within the neural circuitries and the morphological defects associated with non-random regions of the brain. The alteration that is observed during opioid tolerance is the desensitization of mu opioid receptors to dopamine, resulting in the need of increased dosage of opiates to achieve the same high. During opioid dependence, key changes that are seen in the locus ceruleus and the mesolimbic reward system increase both the likelihood of an overdose event and withdrawal when an exogenous opioid is present or absent, respectively. There are two models that describe additional changes that occur during the transition from frequent abuse to addiction: (1) the “Changed Set Point Model” and (2) the “Cognitive Deficits Model.” All three variants of the “Changed Set Point Model” portray a shift in the physiological set points of dopamine and glutamate levels in the reward system and regions that control it. The “Cognitive Deficits Model” theorizes that the modifications localized to the prefrontal cortex are responsible for the ultimate transition. Once the abuser is thrust into the addiction cycle, additional changes in the neural networks are observed. These changes are seen in each of the three phases: (1) Binge and Intoxication, (2) Withdrawal and Negative Affect, and (3) Preoccupation and Anticipation. In the first phase, a process called drug-induced neuroplasticity occurs, resulting in the amplification of signals originating from dopaminergic neurons. Next, during Withdrawal and Negative Affect phase, among other changes, the amygdala is shown to be re-wired in such a way that the addict is more sensitive to stress. And finally in the last phase, the changes that occur, secondary to processes similar to drug-induced, are indicated in the prefrontal cortex. The current FDA-approved medication-assisted therapies include methadone, buprenorphine, and naltrexone. The single outstanding abstinence-based treatment is the 12-step program. In the evaluation of medical and non-medical interventions the relative efficacies were measured using the metrics: (1) rates of abstinence achievement, (2) rates of opioid use, and (3) retention in treatment. Individually, all therapies show moderate success when measured against each metric, which further validates the brain disease model for addiction, and also indicates that the future direction of addressing the opioid epidemic should point at combination therapies. What is most imperative now is for there to be more widespread recognition of the brain disease model for addiction.

In this thesis, we will study basic mathematical epidemic models SIR, SIS, and SEIR. Then we will construct a modified model as a combination of SIR and SIS models. First, we will find the explicit solutions for the SIS model. and show no exact solution for the SIR model. Also, find the parametric solution for the SIR model and find a numerical solution by using Euler’s method. Then we find an approximate explicit form of the epidemic curve. Also, we will study parametric influences on the SIR and SIS models. Finally, we will suggest some recommendations to decrease the epidemic’s spread.

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In recent years, several methods have been proposed for real-time modeling and forecasting of the epidemic curve. These methods range from simple compartmental models to complex agent-based models. In this dissertation, we present a model-based reasoning approach to forecasting the epidemic curve and estimating underlying disease model parameters. The method is based on building an epidemic library consisting of past and simulated influenza outbreaks. During an influenza epidemic, we use a combination of statistical, optimization and modeling techniques to either assign the epidemic to one of the cases in the library or propose parameters for modeling the epidemic. The method is presented in four steps. First, we discuss a sensitivity analysis study evaluating how minute changes in the disease model parameters influence the dynamics of simulated epidemics. Next, we present a supervised classification method for predicting the epidemic curve. The epidemic curve is forecasted by matching the partial surveillance curve to at least one of the epidemics in the library. We expand on the classification approach by presenting a method which identifies epidemics similar or different from those in the library. Lastly, we discuss a simulation optimization method for estimating model parameters to forecast the epidemic curve of an epidemic distinct from those in the library. / Ph. D.

Many real word networks exhibit cascading phenomena, e.g., disease outbreaks in social contact networks, malware propagation in computer networks, failures in cyber-physical systems such as power grids. As they grow in size and complexity, their security becomes increasingly important. In this thesis, we address the problems of controlling cascading failures in various network settings. We address the cascading phenomena which are either natural (e.g., disease outbreaks) or malicious (e.g., cyber attacks). We consider the nodes of a network as being individually or collectively controlled by self-interested autonomous agents and study their strategic decisions in the presence of these failure cascades. There are many models of cascading failures which specify how a node would fail when some neighbors have failed, such as: (i) epidemic spread models in which the cascading can be viewed as a natural and stochastic process and (ii) cyber attack models where the cascade is driven by malicious intents. We present our analyses and algorithms for these models in two parts. Part I focuses on problems of controlling epidemic spread. Epidemic outbreaks are generally modeled as stochastic diffusion processes. In particular, we consider the SIS model on networks. There exist heuristic centralized approaches in the literature for containing epidemic spread in SIS/SIR models; however no rigorous performance bounds are known for these approaches. We develop algorithms with provable approximation guarantees that involve either protective intervention (e.g., vaccination) or link removal (e.g., unfriending). Our approach relies on the characterization of the SIS model in terms of the spectral radius of the network. The centralized approaches, however, are sometimes not feasible in practice. For example, targeted vaccination is often not feasible because of limited compliance to directives. This issue has been addressed in the literature by formulating game theoretic models for the containment of epidemic spread. However they generally assume simplistic propagation models or homogeneous network structures. We develop novel game formulations which rely on the spectral characterization of the SIS model. In these formulations, the failures start from a random set of nodes and propagate through the network links. Each node acts as a self-interested agent and makes strategic intervention decisions (e.g., taking vaccination). Each agent decides its strategy to optimize its payoff (modeled by some payoff function). We analyze the complexity of finding Nash equilibria (NE) and study the structure of NE for different networks in these game settings. Part II focuses on malware spread in networks. In cybersecurity literature malware spreads are often studied in the framework of ``attack graph" models. In these models, a node represents either a physical computing unit or a network configuration and an edge represents a physical or logical vulnerability dependency. A node gets compromised if a certain set of its neighbors are compromised. Attack graphs describe explicit scenarios in which a single vulnerability exploitation cascades further into the network exploiting inherent dependencies among the network components. Attack graphs are used for studying cascading effects in many cybersecurity applications, e.g., component failure in enterprise networks, botnet spreads, advanced persistent attacks. One distinct feature of cyber attack cascades is the stealthy nature of the attack moves. Also, cyber attacks are generally repeated. How to control stealthy and repeated attack cascades is an interesting problem. Dijk et. al.~cite{van2013flipit} first proposed a game framework called ``FlipIt" for reasoning about the stealthy interaction between a defender and an attacker over the control of a system resource. However, in cybersecurity applications, systems generally consists of multiple resources connected by a network. Therefore it is imperative to study the stealthy attack and defense in networked systems. We develop a generalized framework called ``FlipNet" which extends the work of Dijk et. al.~cite{van2013flipit} for network. We present analyses and algorithms for different problems in this framework. On the other hand, if the security of a system is limited to the vulnerabilities and exploitations that are known to the security community, often the objective of the system owner is to take cost-effective steps to minimize potential damage in the network. This problem has been formulated in the cybersecurity literature as hardening attack graphs. Several heuristic approaches have been shown in the litrature so far but no algorithmic analysis have been shown. We analyze the inherent vulnerability of the network and present approximation hardening algorithms. / Ph. D.

This study used a content analysis and a rhetorical analysis to examine the strategies the Chinese government utilized for handling post-crisis issues of the 2003 SARS epidemic. The content of several media outlets — Chinese Version of Xinhua News Agency, English Version of Xinhua News Agency, The Toronto Star, The New York Times, The Times (London) — were examined on the same issue in the post-crisis period from June 25, 2003 to September 9, 2003. Chinese media and Western media were examined to test the frame correlation between media and Chinese government discourses. The use of Chinese government as information sources in media coverage was explored. Chinese post-crisis management performance was evaluated through analysis of the use of Chinese government frames by mass media and the use of the Chinese government as a trusted information source. The results showed that the Chinese government used a renewal post-crisis communication theme through communication strategies of bolstering and transcendence. The content of Chinese media had a substantial relationship with frames of Chinese government. Chinese government was used as a believable source for Chinese media. The content of Western media had no relationship with frames of Chinese government. Chinese government was employed as a skeptical information source in coverage of Western media. / Master of Arts

Dans cette thèse nous contribuons à répondre aux questions sur les processus dynamiques sur réseaux temporels. En particulier, nous etudions l'influence des représentations de données sur les simulations des processus épidémiques, le niveau de détail nécessaire pour la représentation des données et sa dépendance des paramètres de la propagation de l'épidémie. Avec l'introduction de la matrice de distributions du temps de contacts nous espérons pouvoir améliorer dans le futur la précision des prédictions des épidémies et des stratégies d'immunisation en intégrant cette représentation des données aux modèles d'épidémies multi-échelles. De plus nous montrons comment les processus épidémiques dynamiques sont influencés par les propriétés temporelles des données. / In this thesis we contribute to provide insights into questions concerning dynamic epidemic processes on data-driven, temporal networks. In particular, we investigate the influence of data representations on the outcome of epidemic processes, shedding some light on the question how much detail is necessary for the data representation and its dependence on the spreading parameters. By introducing an improvement to the contact matrix representation we provide a data representation that could in the future be integrated into multi-scale epidemic models in order to improve the accuracy of predictions and corresponding immunization strategies. We also point out some of the ways dynamic processes are influenced by temporal properties of the data.