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This paper proposes a data-driven framework for quantifying disaster vulnerability using social media analytics, repurposing a previously collected Twitter dataset originally intended for evacuation behavior analysis. After refining the dataset to isolate signals of distress and need, a category based classification strategy is introduced in which thematic dictionaries guide the grouping of Tweets based on the semantic similarity of their embeddings. Focusing on Hurricane Dorian, a compound disaster during the COVID-19 pandemic characterized by high distress and negative sentiment, a weighted amplification factor is incorporated that prioritizes Tweet categories based on the immediacy of impact on human life, while normalizing by Tweet volume and population density. The resulting Media Impact Index (MII) is calculated at the Census Block Group (CBG) level for the United States. To demonstrate the cross-cultural flexibility of the pipeline, the same methodology is applied to Typhoon Hagibis in Japan, with a comparable vulnerability index generated at the district level. The findings suggest that the proposed framework can provide emergency management agencies with a scalable and adaptable tool for identifying and prioritizing vulnerable regions in diverse types of disasters and sociocultural contexts. 

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. 

From the start, the airline industry has remarkably connected countries all over the world through rapid long-distance transportation, helping people overcome geographic barriers. Consequently, this has ushered in substantial economic growth, both nationally and internationally. The airline industry produces vast amounts of data, capturing a diverse set of information about their operations, including data related to passengers, freight, flights, and much more. Analyzing air travel data can advance the understanding of airline market dynamics, allowing companies to provide customized, efficient, and safe transportation services. Due to big data challenges in such a complex environment, the benefits of drawing insights from the air travel data in the airline industry have not yet been fully explored. This article aims to survey various components and corresponding proposed data analysis methodologies that have been identified as essential to the inner workings of the airline industry. We introduce existing data sources commonly used in the papers surveyed and summarize their availability. Finally, we discuss several potential research directions to better harness airline data in the future. We anticipate this study to be used as a comprehensive reference for both members of the airline industry and academic scholars with an interest in airline research.