ABSTRACT
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 AUTHOR]
ABSTRACT
The outbreak of coronavirus disease (COVID-19) has swept across more than 180 countries and territories since late January 2020. As a worldwide emergency response, governments have taken various measures and implemented policies, such as self-quarantine, travel restrictions, work from home, and regional lockdown, to control the rapid spread of this epidemic. The common intention of these countermeasures is to restrict human mobility because COVID-19 is a highly contagious disease that is spread by human-to-human transmission. Medical experts and policy makers have expressed the urgency of being able to effectively evaluate the effects of human restriction policies with the aid of big data and information technology. Thus, in this study, based on big human mobility data and city POI data, we designed an interactive visual analytics system named EpiMob (Epidemic Mobility). The system interactively simulates the changes in human mobility and the number of infected people in response to the implementation of a certain restriction policy or combination of policies (e.g., regional lockdown, telecommuting, screening). Users can conveniently designate the spatial and temporal ranges for different mobility restriction policies, and the result reflecting the infection situation under different policies is dynamically displayed and can be flexibly compared. We completed multiple case studies of the largest metropolitan area in Japan (i.e., Greater Tokyo Area) and conducted interviews with domain experts to demonstrate that our system can provide illustrative insight by measuring and comparing the effects of different human mobility restriction policies for epidemic control.