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Pandemics are disruptive. Thus, there is a need to prepare and plan
actions in advance for identifying, assessing, and responding to
such events to manage uncertainty and support sustainable
livelihood and wellbeing. A detailed assessment of a continuously
evolving situation needs to take place, and several aspects must be
brought together and examined before the declaration of a pandemic
even happens. Various health organizations; crisis management
bodies; and authorities at local, national, and international
levels are involved in the management of pandemics. There is no
better time to revisit current approaches to cope with these new
and unforeseen threats. As countries must strike a fine balance
between protecting health, minimizing economic and social
disruption, and respecting human rights, there has been an emerging
interest in lessons learned and specifically in revisiting past and
current pandemic approaches. Such approaches involve strategies and
practices from several disciplines and fields including healthcare,
management, IT, mathematical modeling, and data science. Using data
science to advance in-situ practices and prompt future directions
could help alleviate or even prevent human, financial, and
environmental compromise, and loss and social interruption via
state-of-the-art technologies and frameworks. Data Science
Advancements in Pandemic and Outbreak Management demonstrates how
strategies and state-of-the-art IT have and/or could be applied to
serve as the vehicle to advance pandemic and outbreak management.
The chapters will introduce both technical and non-technical
details of management strategies and advanced IT, data science, and
mathematical modelling and demonstrate their applications and their
potential utilization within the identification and management of
pandemics and outbreaks. It also prompts revisiting and critically
reviewing past and current approaches, identifying good and bad
practices, and further developing the area for future adaptation.
This book is ideal for data scientists, data analysts, infectious
disease experts, researchers studying pandemics and outbreaks, IT,
crisis and disaster management, academics, practitioners,
government officials, and students interested in applicable
theories and practices in data science to mitigate, prepare for,
respond to, and recover from future pandemics and outbreaks.
Pandemics are disruptive. Thus, there is a need to prepare and plan
actions in advance for identifying, assessing, and responding to
such events to manage uncertainty and support sustainable
livelihood and wellbeing. A detailed assessment of a continuously
evolving situation needs to take place, and several aspects must be
brought together and examined before the declaration of a pandemic
even happens. Various health organizations; crisis management
bodies; and authorities at local, national, and international
levels are involved in the management of pandemics. There is no
better time to revisit current approaches to cope with these new
and unforeseen threats. As countries must strike a fine balance
between protecting health, minimizing economic and social
disruption, and respecting human rights, there has been an emerging
interest in lessons learned and specifically in revisiting past and
current pandemic approaches. Such approaches involve strategies and
practices from several disciplines and fields including healthcare,
management, IT, mathematical modeling, and data science. Using data
science to advance in-situ practices and prompt future directions
could help alleviate or even prevent human, financial, and
environmental compromise, and loss and social interruption via
state-of-the-art technologies and frameworks. Data Science
Advancements in Pandemic and Outbreak Management demonstrates how
strategies and state-of-the-art IT have and/or could be applied to
serve as the vehicle to advance pandemic and outbreak management.
The chapters will introduce both technical and non-technical
details of management strategies and advanced IT, data science, and
mathematical modelling and demonstrate their applications and their
potential utilization within the identification and management of
pandemics and outbreaks. It also prompts revisiting and critically
reviewing past and current approaches, identifying good and bad
practices, and further developing the area for future adaptation.
This book is ideal for data scientists, data analysts, infectious
disease experts, researchers studying pandemics and outbreaks, IT,
crisis and disaster management, academics, practitioners,
government officials, and students interested in applicable
theories and practices in data science to mitigate, prepare for,
respond to, and recover from future pandemics and outbreaks.
Disaster management is a dynamic and fluid area, which requires the
involvement of expertise from different authorities and
organizations. There is a need to prepare and plan in advance
actions in response to disaster related events in order to support
sustainable livelihood by protecting lives, property and the
environment. Advanced ICTs for Disaster Management and Threat
Detection: Collaborative and Distributed Frameworks demonstrates
how strategies and state-of-the-art ICT have and/or could be
applied to serve as a vehicle to advance disaster management
approaches, decisions and practices. This book provides both a
conceptual and practical guidance to disaster management while also
identifying and developing effective and efficient approaches,
mechanisms, and systems using emerging technologies to support an
effective operation. This state-of-the-art reference collection
attempts to prompt the future direction for disaster managers to
identify applicable theories and practices in order to mitigate,
prepare for, respond to and recover from various foreseen and/or
unforeseen disasters.
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