The COVID-19 pandemic has resulted in more than 440 million confirmed cases globally and almost 6 million reported deaths as of March 2022. Consequently, the world experienced grave repercussions to citizens’ lives, health, wellness, and the economy. In responding to such a disastrous global event, countermeasures are often implemented to slow down and limit the virus’s rapid spread. Meanwhile, disaster recovery, mitigation, and preparation measures have been taken to manage the impacts and losses of the ongoing and future pandemics. Data-driven techniques have been successfully applied to many domains and critical applications in recent years. Due to the highly interdisciplinary nature of pandemic management, researchers have proposed and developed data-driven techniques across various domains. However, a systematic and comprehensive survey of data-driven techniques for pandemic management is still missing. In this article, we review existing data analysis and visualization techniques and their applications for COVID-19 and future pandemic management with respect to four phases (namely, Response, Recovery, Mitigation, and Preparation) in disaster management. Data sources utilized in these studies and specific data acquisition and integration techniques for COVID-19 are also summarized. Furthermore, open issues and future directions for data-driven pandemic management are discussed.
A six-month-long Atlantic hurricane season impacts Florida residents every year and can result in devastating consequences, including loss of life, property damage, and business interruptions. Hurricane risk assessment and loss prediction are critical to various uses such as determining homeowner insurance premiums, regulating these premiums, conducting scenario analysis, conducting stress tests for companies, disaster management, and evaluating the benefits of disaster mitigation techniques. This article describes the Florida Public Hurricane Loss Model (FPHLM): a large-scale catastrophe model with massive databases and analytics tools for business and government decision-making. We will discuss the design and implementation of each component in FPHLM and explain the tools and techniques utilized to tackle challenges in data availability, data analytics, and the interface between the data, analytical techniques, and computing. Results are shown to validate the software system's effectiveness and reliability and illustrate some of the system's use cases.
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