Foreword
The COVID-19 pandemic has seen scientific
research move into public discourse in
unparalleled ways. Across the scientific
spectrum, researchers have stepped up, the
data science and AI community included, to
work alongside clinicians, policy makers and
the government at the heart of the response,
directly impacting on our daily lives.
Data science and AI is an inherently
interdisciplinary community, and our activities
at The Alan Turing Institute in response to the
pandemic reflect this. Our researchers have
developed algorithms to monitor pedestrian
density and ensure social distancing on the
streets of London; combined NHS datasets to
help answer clinical questions about the effects
of COVID-19; explored what makes people
vulnerable to health-related misinformation;
and improved the accuracy of the NHS
COVID-19 app. (See page 10 for more details of
our response.)
This report has been edited by four researchers
with backgrounds spanning AI, data science,
public policy, human rights and medicine. They
have synthesised the views of 96 attendees to
a series of workshops held at the end of 2020,
which aimed to provide a snapshot of the uses
of data science and AI during the pandemic,
and what we as a community can learn from
the experience.
While this represents a small part of what
will surely be a larger reflection exercise to
come, the central findings – issues of data
access and quality; inequality among both the
research community and wider society; and
communication difficulties between experts
and non-experts – contain valuable reflections
and suggestions for how the data science
and AI community might prepare for future
emergencies. Indeed, the Turing has already
begun a large-scale project
1
which aims to
boost societal, governmental and economic
resilience to shocks such as this pandemic.
Ahead of the G7 summit in the UK in June
2021, the leading scientific bodies of the G7
nations (the ‘Science 7’) recently published a
call for more ‘data readiness’ in preparation
for future health emergencies.
2
This is a
timely amplification of the message in the
Turing’s report about the need for increased
data access and sharing, at a time when the
pandemic continues to have catastrophic
impacts around the world.
My thanks to the editors for initiating and
delivering this report, and to all the workshop
theme leads and participants. We hope it
will be a valued contribution to the ongoing
discussions about the data science and AI
response, alongside notable reports from the
Centre for Data Ethics and Innovation,
3
the Ada
Lovelace Institute,
4
the Royal Society’s DELVE
initiative
5
and the Royal Statistical Society,
6
among others.
The Turing looks forward to continuing its
role to convene and deliver activities and
reflections in response to this pandemic, and in
preparation for other crises.
Adrian
Smith
Institute Director and Chief Executive
The Alan Turing Institute
Preface
At the end of 2019, a new highly infectious
virus, now known as severe acute respiratory
syndrome coronavirus 2 (SARS-CoV-2),
was identified as the underlying pathogen
for a series of unexplained pneumonias
(subsequently termed coronavirus disease
2019, or COVID-19), clustered in Wuhan,
China. By 30 January 2020, COVID-19 had
become so prevalent that the World Health
Organisation (WHO) declared it a Public Health
Emergency of International Concern – the
WHO’s highest level of alarm. As we finish
this report in spring 2021, the disease itself
has claimed over three million lives globally,
with more than 170 million confirmed cases,
and many more affected by the impacts of
lockdowns and the unprecedented disruption
to the global economy. The UK has now been
through two waves of the virus, with infections,
hospitalisations and fatalities in the second
wave exceeding those in the first.
While pandemics appear to have occurred
throughout human history, the COVID-19
pandemic is unique in one important respect.
It is the first pandemic to occur in the age of
data science and AI: the first pandemic in a
world of deep learning, ubiquitous computing,
smartphones, wearable technology and social
media. It is thus unsurprising that governments
across the globe, including the UK’s, looked
to data to inform their responses and help
navigate challenges. The goal was to limit the
spread of the disease and its medical, social
and economic consequences. As such, the
UK government stated that its policies were
“guided by the science”, and later that ending
lockdowns depended on “data, not dates”.
In response, many members of the UK’s data
science and AI community stepped forward,
spearheading initiatives that they hoped would
assist the domestic and international response.
These initiatives came from individual
academics, university research groups, the
healthcare sector, national institutes and
others. They involved not just experts on
virology and epidemiological modelling, but
also researchers studying, for example, the
s
1
ocial and economic consequences of non-
pharmaceutical interventions. The response
was remarkable for its breadth of engagement
across disciplines, as demonstrated by
the range of backgrounds of our workshop
participants and the diverse set of insights that
they generated.
Goals, origins and structure of the report
This report was commissioned by The Alan
Turing Institute with the aim of reflecting on
the UK's data science and AI response to the
pandemic. The Turing is the UK’s national
institute for data science and AI, and partners
with many of the UK’s leading universities and
research centres to advance the country’s
capacity and competitiveness in these areas,
with the overall mission of changing the world
for the better.
The Turing’s goals in undertaking this work
were twofold:
1.
To capture the initiatives and resources that
have been developed by the data science and
AI community during the pandemic.
2.
To gather the experiences and insights of
this community during the pandemic – what
worked well, what didn’t, and how we as
a community could respond better to this
pandemic and future emergencies.
The work has its origins in a one-day
conference 'AI and data science in the age
of COVID-19,'7 which was held virtually by the
Turing on 24 November 2020 and featured talks
from some of the leading voices in the UK’s
response to COVID-19. The free event attracted
over 1,700 registrants from 35 countries, from
academia, industry, the public sector and the
general public.
A series of themed, virtual workshops followed
in November and December 2020. The
invitation to participate in these workshops
was widely circulated within the UK academic
community, via social media and the Turing’s
network of partners and affiliates. The Turing
used a lightweight reviewing process to select
the participants, who are listed in Appendix A.
1
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2
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/media/about-us/international/g-science-statements/G7-data-for-international-health-emer-
gencies-31-03-2021.pdf
3
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ttps
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www
.go
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.
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vid-
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4
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www
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e.or
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summary/learning-
data-lessons
5
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delve.github.io/reports/2020/11/24/data-readiness-lessons-from-an-emergency.html
6
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7
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www
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turing.ac.
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ai-
and-data-science-age-covid-19
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