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It is widely known that IS/IT plays an important role in healthcare, clinical decision support, emergency response and preparedness, and disaster information management and planning (Ryoo and Choi, 2006; Fichman et al. 2011; Yang et al. 2012; Van De Walle et al. 2014; Chen et al. 2019). For example, data mining techniques can provide various data-driven classification systems (Lee & Siau, 2001) for addressing the risk associated with different groups of prostate cancer patients (Churilov et al. 2005). From the post-analysis of major extreme events, it is revealed that information sharing is critical for effective emergency responses (Chen et al. 2013). In large-scale emergencies, IS/IT solutions have been developed to enhance inter-agency flows of information, communication, and coordination (Aedo et al. 2010). However, IS/IT emergency management solutions may not be optimally designed for pandemic cases when the situation is constantly changing and evolving, and decisions need to be made in real-time with incomplete and dirty data. Further, trust, privacy, and ethical issues cannot be ignored during a crisis (Wang and Siau, 2019a, b; Siau and Wang, 2020). For example, the ethical issues related to access to medical services and devices when the hospitals are overwhelmed. We need more case studies, computing models, empirical studies, theoretical articles, mixed-method approaches, and advanced methodologies to understand, explain, predict, and manage pandemic crises such as COVID-19 (Gefen et al. 2011; Shiau and Chau, 2016; Sarker et al. 2018a,b; Chen et al. 2019; Shiau et al. 2019a; Hair et al. 2019; Khan et al. 2019; Chinazzi et al. 2020; Harrison et al. 2020; Shiau et al. 2020). Advanced technologies such as data analytics, data science, artificial intelligence, and machine learning can play a critical role in pandemic crisis management as well.