[AISWorld] TMIS Call for Papers: Special Issue on Using AI and Data Science to Handle Pandemics and Related Disruptions

[kangzhao7 at gmail.com]

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CALL FOR PAPERS

 

ACM Transactions on 
Management Information Systems

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

Co-editors:

 <mailto:kang-zhao at uiowa.edu> Kang Zhao, The University of Iowa
 <mailto:kltsui at cityu.edu.hk> Kwok Leung Tsui, City University of Hong Kong
 <mailto:qingpeng.zhang at cityu.edu.hk> Qingpeng Zhang, City University of
Hong Kong
 <mailto:kelvintsoi at cuhk.edu.hk> Kelvin Tsoi, The Chinese University of Hong
Kong





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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 (
<https://orange.hosting.lsoft.com/trk/click?ref=znwrbbrs9_6-25f01x3232fbx045
804&> dl.acm.org/journal/tmis/author-guidelines) and submitted through the
TMIS portal (
<https://orange.hosting.lsoft.com/trk/click?ref=znwrbbrs9_6-25f01x31ba15x045
804&> mc.manuscriptcentral.com/tmis) with clear indications for the special
issue. 

Important Dates:

*	Initial submissions due: Sept 15, 2020 
*	First round of notifications: Nov 15, 2020 
*	Second submissions due: Dec 31, 2020 
*	Final decisions: Jan 31, 2021 

For questions and further information, please write to
<mailto:kang-zhao at uiowa.edu> kang-zhao at uiowa.edu. 

The  <mailto:pubs at acm.org> pubs at acm.org email address is not monitored.
Please do not reply to it directly. 

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