Matching Items (2)
Description
Public risk communication (i.e. public emergency warning) is an integral component of public emergency management. Its effectiveness is largely based on the extent to which it elicits appropriate public response to minimize losses from an emergency. While extensive studies have been conducted to investigate individual responsive process to emergency risk information, the literature in emergency management has been largely silent on whether and how emergency impacts can be mitigated through the effective use of information transmission channels for public risk communication. This dissertation attempts to answer this question, in a specific research context of 2009 H1N1 influenza outbreak in Arizona. Methodologically, a prototype agent-based model is developed to examine the research question. Along with the specific disease spread dynamics, the model incorporates individual decision-making and response to emergency risk information. This simulation framework synthesizes knowledge from complexity theory, public emergency management, epidemiology, social network and social influence theory, and both quantitative and qualitative data found in previous studies. It allows testing how emergency risk information needs to be issued to the public to bring desirable social outcomes such as mitigated pandemic impacts. Simulation results generate several insightful propositions. First, in the research context, emergency managers can reduce the pandemic impacts by increasing the percent of state population who use national TV to receive pandemic information to 50%. Further increasing this percent after it reaches 50% brings only marginal effect in impact mitigation. Second, particular attention is needed when emergency managers attempt to increase the percent of state population who believe the importance of information from local TV or national TV, and the frequency in which national TV is used to send pandemic information. Those measures may reduce the pandemic impact in one dimension, while increase the impact in another. Third, no changes need to be made on the percent of state population who use local TV or radio to receive pandemic information, and the frequency in which either channel is used for public risk communication. This dissertation sheds light on the understanding of underlying dynamics of human decision-making during an emergency. It also contributes to the discussion of developing a better understanding of information exchange and communication dynamics during a public emergency and of improving the effectiveness of public emergency management practices in a dynamic environment.
ContributorsZhong, Wei (Author) / Lan, Zhiyong (Thesis advisor) / Kim, Yushim (Committee member) / Corley, Elizabeth (Committee member) / Lant, Timothy (Committee member) / Jehn, Megan (Committee member) / Arizona State University (Publisher)
Created2012
Description
Resistance to existing anti-cancer drugs poses a key challenge in the field of medical oncology, in that it results in the tumor not responding to treatment using the same medications to which it responded previously, leading to treatment failure. Adaptive therapy utilizes evolutionary principles of competitive suppression, leveraging competition between drug resistant and drug sensitive cells, to keep the population of drug resistant cells under control, thereby extending time to progression (TTP), relative to standard treatment using maximum tolerated dose (MTD). Development of adaptive therapy protocols is challenging, as it involves many parameters, and the number of parameters increase exponentially for each additional drug. Furthermore, the drugs could have a cytotoxic (killing cells directly), or a cytostatic (inhibiting cell division) mechanism of action, which could affect treatment outcome in important ways. I have implemented hybrid agent-based computational models to investigate adaptive therapy, using either a single drug (cytotoxic or cytostatic), or two drugs (cytotoxic or cytostatic), simulating three different adaptive therapy protocols for treatment using a single drug (dose modulation, intermittent, dose-skipping), and seven different treatment protocols for treatment using two drugs: three dose modulation (DM) protocols (DM Cocktail Tandem, DM Ping-Pong Alternate Every Cycle, DM Ping-Pong on Progression), and four fixed-dose (FD) protocols (FD Cocktail Intermittent, FD Ping-Pong Intermittent, FD Cocktail Dose-Skipping, FD Ping-Pong Dose-Skipping). The results indicate a Goldilocks level of drug exposure to be optimum, with both too little and too much drug having adverse effects. Adaptive therapy works best under conditions of strong cellular competition, such as high fitness costs, high replacement rates, or high turnover. Clonal competition is an important determinant of treatment outcome, and as such treatment using two drugs leads to more favorable outcome than treatment using a single drug. Switching drugs every treatment cycle (ping-pong) protocols work particularly well, as well as cocktail dose modulation, particularly when it is feasible to have a highly sensitive measurement of tumor burden. In general, overtreating seems to have adverse survival outcome, and triggering a treatment vacation, or stopping treatment sooner when the tumor is shrinking seems to work well.
ContributorsSaha, Kaushik (Author) / Maley, Carlo C (Thesis advisor) / Forrest, Stephanie (Committee member) / Anderson, Karen S (Committee member) / Cisneros, Luis H (Committee member) / Arizona State University (Publisher)
Created2023