Operational, Tactical, and Strategic Planning for Effective Pandemic Response

This dissertation comprises three papers introducing strategies, models, and frameworks to guide pandemic response. The first paper uses a novel mathematical model to analyze the coordination between government and humanitarian non-governmental organizations (NGOs) in response to pandemics. This is a vital form of public-private partnership between governments as the primary source for the humanitarian supplies required during a crisis and aid organizations. This coordination involves the equitable distribution of personal protective equipment, including face masks and face shields among health workers, patients, and the public in hospitals. Considering social costs such as deprivation and equity costs in the model, in addition to the other important classic cost terms, enables managers to organize the best possible response when such outbreaks happen. The second paper introduces a decision support framework designed to assist healthcare managers, and clinical informatics specialists in analyzing and selecting the most appropriate consensus algorithm for their organization's blockchain-based health platforms, with a specific focus on managing pandemic-related information. Blockchain technology holds great potential in addressing pandemics by enhancing security and transparency in various aspects of pandemic tracking and mitigation while promoting public engagements by facilitating real-time exchange of electronic health information. By improving information sharing and coordination among healthcare organizations, it offers more effective response efforts and helps reduce the spread of viruses. However, the performance of consensus algorithms, which are a crucial component of blockchain architecture, can vary, posing a challenge in selecting the appropriate algorithm. To address this, the framework incorporates two techniques: data envelopment analysis (DEA) and the ranking distribution technique. DEA enables the analysis of efficiency without relying solely on expert judgment, providing a more objective assessment. The ranking distribution technique enhances differentiation among algorithms, providing decision-makers with a robust basis for selecting the most suitable blockchain architecture and its associated properties. The third paper focuses on the challenges of disseminating guidance-related information to the public during a pandemic, specifically the role of opinion leaders as reliable sources of information. The study determines the practical characteristics of pandemic opinion leaders on public attitudes using surveys and identifies domain-sensitive pandemic opinion leaders on Twitter based on the discovered characteristics using social network analysis and text mining. The framework's results show that pandemic opinion leaders are active in eight different domains on the Twitter platform. Results also demonstrate that trust is the most influential characteristic of pandemic opinion leaders, while expertise, uniqueness, innovation, and reputation also play important roles.