-------- Forwarded Message -------- Subject: [AISWorld] Deadline extension--ACM TMIS Special Issue on Using AI and Data Science to Handle Pandemics and Related Disruptions Date: Tue, 8 Sep 2020 11:38:13 -0500 From: Kang Zhao kangzhao7@gmail.com To: aisworld@lists.aisnet.org

*ACM Transactions on Management Information Systems*

*Special Issue on Using AI and Data Science to Handle Pandemics and Related Disruptions*

*Co-editors:*

Kang Zhao kang-zhao@uiowa.edu, The University of Iowa Qingpeng Zhang qingpeng.zhang@cityu.edu.hk, City University of Hong Kong Kelvin Tsoi kelvintsoi@cuhk.edu.hk, The Chinese University of Hong Kong Sean Yuan sean.yuan@cityu.edu.hk, City University of Hong Kong

Human beings have fought against pandemics for centuries. With millions of infections, the ongoing pandemic of COVID-19 has seriously disrupted many aspects of people's life and caused hundreds of thousands of deaths around the world. Many researchers are studying the novel coronavirus and its impacts around the clock. In a short period of time, there have been a large number of studies published by various parts of the biomedical and health communities addressing the pandemic from the perspectives of biology, medicine, as well as epidemiology and public health.

The purpose of this special issue is to reach out to the broader scientific community, including those who come from management, social science, policy and/or MIS settings, as well as those who come from computer science, informatics, and other information technology-oriented settings. We seek your contributions that may help the world respond and adjust to pandemics and disruptions they cause. We are particularly interested in research that builds on recent rapid advancements in applying AI and Data Science across many application and domain areas.

The devastating COVID-19 pandemic has resulted in the creation of a large amount of related data, including case tracking data, hospital admission data, news and social media data, human mobility data, as well as data reported in scholarly articles. Many of these COVID-19 related datasets include important relational, temporal and/or geographical features. The availability of such rich datasets provides enormous opportunities for researchers to better understand, monitor and forecast the pandemic.

This special issue seeks articles that *leverage AI and Data Science* to better manage risks and disruptions caused by pandemics. Submissions that analyze how pandemics impact business organizations, industry sectors, as well as the economy and society overall are also welcomed. We especially encourage researchers to put forth innovative and sound ways to plan for or adapt to future pandemics as a result of COVID-19 beyond the immediate near-term horizon.

Topics of interests include, but are not limited to:

- Disease diagnosis and tracking - Predictive models for disease spread - Simulations of disease transmissions and interventions - Disease forecasting and surveillance - Awareness of the disease and of personal protections - AI and data science infrastructures for disease surveillance and control - Discovery and management of knowledge about pandemics or infectious diseases - Supply chain management during and/or after a pandemic - The impact of pandemic-related disruptions on business operations and processes - Decision support for enforcing and lifting quarantine measures - Risk assessment and management for the resumption of economic and social activities

*Submission Guidelines:*

All submissions will follow ACM TMIS guidelines ( dl.acm.org/journal/tmis/author-guidelines https://orange.hosting.lsoft.com/trk/click?ref=znwrbbrs9_6-25f01x3232fbx045007&) and submitted through the TMIS portal (mc.manuscriptcentral.com/tmis https://orange.hosting.lsoft.com/trk/click?ref=znwrbbrs9_6-25f01x31ba15x045007&) with clear indications for the special issue.

*Important Dates:*

- Initial submissions due: Sept 30, 2020 - First round of notifications: Nov 30, 2020 - Second submissions due: Jan 15, 2021 - Final decisions: Feb 15, 2021

For questions and further information, please write to kang-zhao@uiowa.edu. _______________________________________________ AISWorld mailing list AISWorld@lists.aisnet.org