Article Preview
TopIntroduction
The new type of coronaviruses (COVID-19) has had a devastating impact across the world. The pandemic has shaken health systems in many countries, disrupted socio-economic activities, and led to the closure of many learning and economic institutions. The European Parliament Report (2020) addressed the potential social and economic effects of the epidemic in several dimensions. The report emphasizes that health, agriculture, tourism, and travel are some of the sectors that will be directly and dramatically affected by the pandemic (Delivorias & Scholz, 2020). Critical decisions should be made about the short and long-term planning and management of the COVID-19 outbreak (Correia, 2020).
The research community and policymakers of governments and health management policies were unable to pay enough attention to the roles of high-techs in healthcare and adopt and benefit from them rapidly. Nevertheless, public administration research has included some valuable perspectives on governments’ position in implementing and managing new innovations of digital technology (Paulin, 2017; Ben Rehouma, 2019; Dickinson, 2018). Simon (1965) highlighted the significance of qualified data as a backbone of the decision-making process. Hurley and Wallace (1986) mentioned early development of AI and offered experts system as decision aids for public managers. Big data, machine learning, and deep learning techniques achieve faster and more optimal results compared to humans in processing existing data and making better policy decisions (Onder & Saygılı, 2018). AI has proved successful in establishing contextual relationships between various huge amounts of data. Therefore, AI applications and techniques are integrated in many aspects of the health sector such as disease diagnosis, drug development, and genome editing techniques (Chowdhury & Chakraborty, 2017). Moreover, AI can be used effectively in filtering patients’ past medical records and establishing correlations between patient similarities. AI based technology can also detect what types of care work better for a particular group of patients (IBM, 2020).However, it is not technology alone that will make a difference, but also the knowledge and collaboration of AI experts and government officials who make important policy decisions matter.
Most of the time, crises have been observed at the local or regional level, and crisis management literature generally tries to understand the management of crisis at the micro level: local or regional. However, the COVID-19 pandemic has surpassed national and continental levels to become a macro-global crisis. In other words, the uniqueness of COVID-19 pandemic crisis is that it has higher uncertainty and a higher degree of transboundary fatalities. Therefore, this article investigates the question of “How can AI contribute to crisis management policies to fight against the COVID-19 and its impacts?” To do this, different AI methods, applications, and tools used in different countries in fighting COVID-19 virus will be provided by integrating previous research and theories from literature about crisis management policies. The crisis management discussed in this article emerges from a review of the literature on the ‘‘AI’’ and ‘‘COVID-19’’. To review the literature regarding COVID-19 crisis management, electronic academic databases were searched by using keywords such as “AI”, “big data,” “crisis management,” “disease outbreak,” “prevention policy,” “digital governance,” “scenario evaluations”, “recovery” and the combinations of these keywords about COVID-19 pandemic. Fighting COVID-19 pandemic with AI will be considered in terms of crisis management framework in preparation, prevention, response, and recovery policies perspectives.