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\begin{document}

\title{FFP3 respirators protect healthcare workers against infection~ with
SARS-CoV-2}



\author[1]{Mark Ferris}%
\author[1]{Rebecca Ferris}%
\author[1]{Chris Workman}%
\author[2]{Eoin O'Connor}%
\author[1,3]{David A Enoch}%
\author[1]{Emma Goldesgeyme}%
\author[1]{Natalie Quinnell}%
\author[1]{Parth Patel}%
\author[1]{Jo Wright}%
\author[1]{Geraldine Martell}%
\author[1]{Christine Moody}%
\author[1]{Ashley Shaw}%
\author[4,5]{Christopher J.R. Illingworth}%
\author[1,6,7,8]{Nicholas J. Matheson}%
\author[1,6,9]{Michael P. Weekes}%
\affil[1]{Cambridge University NHS Hospitals Foundation Trust, Cambridge, UK}%
\affil[2]{School of Clinical Medicine, University of Cambridge, Cambridge, UK}%
\affil[3]{Clinical Microbiology \& Public Health Laboratory, Public Health England, Cambridge, UK}%
\affil[4]{MRC Biostatistics Unit, University of Cambridge, East Forvie Building, Forvie Site, Robinson Way, Cambridge, CB2 0SR, UK}%
\affil[5]{Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, CB3 0WA, UK}%
\affil[6]{Department of Medicine, University of Cambridge, Cambridge, UK}%
\affil[7]{Cambridge Institute of Therapeutic Immunology and Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, University of Cambridge, UK}%
\affil[8]{NHS Blood and Transplant, Cambridge, UK}%
\affil[9]{Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK}%


\vspace{-1em}



  
  \date{March 13, 2024}


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\textbf{Introduction}

Consistent with World Health Organization (WHO) advice {[}1{]}, UK
Infection Protection Control guidance recommends that healthcare workers
(HCWs) caring for patients with coronavirus disease 2019 (COVID-19)
should use fluid resistant surgical masks type IIR (FRSMs) as
respiratory protective equipment (RPE), unless aerosol generating
procedures (AGPs) are being undertaken or are likely, when a filtering
face piece 3 (FFP3) respirator should be used {[}2{]}. In a recent
update, an FFP3 respirator is recommended if ``an unacceptable risk of
transmission remains following rigorous application of the hierarchy of
control'' {[}3{]}. Conversely, guidance from the Centers for Disease
Control and Prevention (CDC) recommends that HCWs caring for patients
with COVID-19 should use an N95 or higher level respirator {[}4{]}. WHO
guidance suggests that a respirator, such as FFP3, may be used for HCWs
in the absence of AGPs if availability or cost is not an issue {[}1{]}.

A recent systematic review undertaken for PHE concluded that: ``patients
with SARS-CoV-2 infection who are breathing, talking or coughing
generate both respiratory droplets and aerosols, but FRSM (and where
required, eye protection) are considered to provide adequate staff
protection'' {[}5{]}. Nevertheless, FFP3 respirators are more effective
in preventing aerosol transmission than FRSMs, and observational data
suggests that they may improve protection for HCWs {[}6{]}. It has
therefore been suggested that respirators should be considered as a
means of affording the best available protection {[}7{]}, and some
organisations have decided to provide FFP3 (or equivalent) respirators
to HCWs caring for COVID-19 patients, despite a lack of mandate from
local or national guidelines {[}8{]}.

Data from the HCW testing programme at Cambridge University Hospitals
NHS Foundation Trust (CUHNFT) during the first wave of the UK severe
acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic indicated
a higher incidence of infection amongst HCWs caring for patients with
COVID-19, compared with those who did not {[}9{]}. Subsequent studies
have confirmed this observation {[}10, 11{]}. This disparity persisted
at CUHNFT in December 2020, despite control measures consistent with PHE
guidance and audits indicating good compliance. The CUHNFT infection
control committee therefore implemented a change of RPE for staff on
``red'' (COVID-19) wards from FRSMs to FFP3 respirators. In this study,
we analyse the incidence of SARS-CoV-2 infection in HCWs before and
after this transition.

\textbf{Results}

The total number of cases of SARS-CoV-2 infection amongst HCWs at CUHNFT
increased throughout the study period, in keeping with the rising
incidence of SARS-CoV-2 in the community (\textbf{Figure 1 and Figure
1--source data 1} ). Similar proportions of cases were ascertained by
symptomatic testing and asymptomatic screening on both green and red
wards (\textbf{Figure 1--figure supplement 1} ).

Prior to the change in RPE, cases per ward day were higher on red
compared with green wards in seven out of eight weeks analysed (p=0.016,
Wilcoxon signed-rank test, \textbf{Figure 2} and \textbf{Table 1} ).
Following the change in RPE, the incidence of infection on red and green
wards was similar, and not statistically different (p=0.5, Wilcoxon
signed-rank test, \textbf{Figure 2} and \textbf{Table 1} ). Strikingly,
there was a strong positive correlation between the incidence of
SARS-CoV-2 in the community and the number of cases per ward day on
green (R\textsuperscript{2}=0.88) but not red wards
(R\textsuperscript{2} = 0.01) (\textbf{Figure 2--figure supplement 1} ).
Taken together, these results suggest that the majority of cases among
HCWs on green wards were caused by community-acquired infection, whereas
cases among HCWs on red wards were caused by both community-acquired
infection and direct, ward-based infection from patients with COVID-19,
effectively mitigated by the use of FFP3 respirators.

\textbf{Figure 1: Comparison between total number of cases amongst HCWs
and community incidence of SARS-CoV-2.}

\textbf{}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Presentation2/Presentation2}

\end{center}
\end{figure}

Community incidence is shown for the East of England, UK, derived from
https://coronavirus.data.gov.uk/details/cases, with raw data shown
in~\textbf{Figure 1--source data 1.}

\textbf{}

\textbf{Figure 2: Weekly cases per ward day amongst HCWs on red and
green wards prior to and after the change in RPE.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Table 1: Weekly numbers of cases amongst HCWs on red and green
wards, and cases per ward day.}\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllll@{}}
\toprule
Week & Week start & Red cases & Red ward days & Red cases per ward day &
Green cases & Green ward days & Green cases per ward day & Excluded
cases & Total & Community\tabularnewline
\midrule
\endhead
1 & 02/11/2020 & 0 & 7 & 0 & 5 & 245 & 0.02 & 16 & 21 &
7876\tabularnewline
2 & 09/11/2020 & 2 & 7 & 0.286 & 7 & 245 & 0.028 & 33 & 42 &
9499\tabularnewline
3 & 16/11/2020 & 1 & 12 & 0.083 & 3 & 240 & 0.013 & 26 & 30 &
7998\tabularnewline
4 & 23/11/2020 & 1 & 14 & 0.071 & 6 & 238 & 0.025 & 24 & 31 &
7203\tabularnewline
5 & 30/11/2020 & 3 & 14 & 0.214 & 6 & 238 & 0.025 & 20 & 29 &
9441\tabularnewline
6 & 07/12/2020 & 5 & 14 & 0.357 & 10 & 238 & 0.042 & 33 & 48 &
16535\tabularnewline
7 & 14/12/2020 & 1 & 14 & 0.071 & 7 & 238 & 0.029 & 41 & 49 &
31219\tabularnewline
8 & 21/12/2020 & 3 & 14 & 0.214 & 10 & 238 & 0.042 & 56 & 69 &
37259\tabularnewline
9 & 28/12/2020 & 2 & 21 & 0.095 & 20 & 231 & 0.087 & 58 & 80 &
50110\tabularnewline
10 & 04/01/2021 & 4 & 29 & 0.138 & 34 & 223 & 0.152 & 70 & 108 &
41663\tabularnewline
11 & 11/01/2021 & 5 & 50 & 0.1 & 34 & 202 & 0.168 & 63 & 102 &
31341\tabularnewline
\bottomrule
\end{longtable}

Weeks following the change in RPE are highlighted in grey. Community
incidence (total cases per week) is shown for the East of England, UK,
with raw data shown in \textbf{Figure 1--source data 1} .

\par\null

To further quantify the risk of infection for HCWs working on red and
green wards, we generated a simple mathematical model. According to this
model, the total risk of infection is divided into a risk from
community-based exposure, and a risk from direct, ward-based exposure to
patients. The risk from direct exposure on red wards was allowed to vary
upon the introduction of FFP3 respirators, and was fitted to a maximum
likelihood model. Inferred parameters and their confidence intervals are
shown in \textbf{Table 2} . Our model produced a qualitatively close fit
to the observed numbers of cases (\textbf{Figures 3A-B} ).

The inferred risk of direct infection from working on a green ward was
low throughout the study period, and consistently lower than the risk of
community-based exposure, which increased in proportion to rising levels
of community incidence (\textbf{Figure 3C} ). By contrast, the risk of
direct infection from working on a red ward before the change in RPE was
considerably higher than the risk of community-based exposure, and
approximately 47-fold greater than the corresponding risk from working
on a green ward (confidence interval {[}7.92, [?]{]}). Thus, whilst almost
all cases on green wards were likely caused by infection in the
community, cases on red wards at the beginning of the study period were
attributed mainly to direct, ward-based exposure (\textbf{Figures 3D-E}
). Critically, our model further suggests that the introduction of FFP3
respirators provided 100\% (confidence interval {[}31.3\%, 100\%{]})
protection against direct, ward-based COVID-19 infection (\textbf{Table
2} , r2/r1).



\textbf{Figure 3: Mathematical modelling of the risks of infection for
HCWs on red and green wards.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

(\textbf{A-B} ) Comparison of modelled and actual cases. The model
(black dashed line) aimed to reproduce the risks of infection among HCWs
per ward day \textbf{(A)} on green wards (green solid line) and
(\textbf{B)}on red wards (red solid line). (\textbf{C)} Risks inferred
from the model. HCWs were vulnerable to COVID-19 infection from exposure
to individuals in the community, with this risk increasing with
community incidence (grey line). HCWs working on green wards faced a
consistent, low risk of infection from direct, ward-based exposure
(green line). HCWs working on red wards initially faced a much higher
risk of infection from direct, ward-based exposure, falling to zero
(confidence interval {[}0, 0.0804{]}) upon the introduction of FFP3
respirators. In this figure risks are expressed per ward day; a risk of
0.1 indicates that a particular source of risk would be expected to
cause one HCW from the multiple staff present on a ward to develop an
infection every 10 days that the ward was in operation. (\textbf{D-E)}
Proportion of community-acquired cases. Proportion of infections on
(\textbf{D} ) green and (\textbf{E} ) red wards inferred to have arisen
via exposure to individuals in the community (green line, green wards;
red line, red wards; confidence intervals shaded).

\par\null

\textbf{Table 2: Statistics and parameter ratios inferred from the
model}\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Statistic & Model parameter & Maximum likelihood estimate & Confidence
interval\tabularnewline
\midrule
\endhead
Force of community-based infection per community case & k & 2.62 x
10\textsuperscript{-6} & {[}1.97 x 10\textsuperscript{-6}, 3.20 x
10\textsuperscript{-6}{]}\tabularnewline
Force of direct infection per ward day (green ward) & g & 2.86 x
10\textsuperscript{-3} & {[}0, 1.43 x
10\textsuperscript{-2}{]}\tabularnewline
Force of direct infection per ward day (red ward, pre-FFP3) &
r\textsubscript{1} & 0.135 & {[}0.0648, 0.232{]}\tabularnewline
Force of direct infection per ward day (red ward, post-FFP3) &
r\textsubscript{2} & 0 & {[}0, 0.0804{]}\tabularnewline
Relative direct risk on red wards post- versus pre-FFP3 &
r\textsubscript{2}/r\textsubscript{1} & 0 & {[}0,
0.687{]}\tabularnewline
Relative direct risk on red ward versus green ward pre-FFP3 &
r\textsubscript{1}/g & 47.0 & {[}7.92, [?])\tabularnewline
Relative direct risk on red ward versus green ward post-FFP3 &
r\textsubscript{2}/g & 0 & {[}0, [?])\tabularnewline
\bottomrule
\end{longtable}

\par\null

\textbf{Discussion}

HCWs may be exposed to SARS-CoV-2 from contacts in the community, from
contacts with other HCWs, and from contacts with patients. In this
study, we developed a mathematical model to evaluate the relative
magnitudes of these risks, based on data collected during the second
wave of the SARS-CoV-2 pandemic in the UK (Nov 2020--Jan 2021).

Whilst using FRSMs, the majority of infections among HCWs working on red
wards could be attributed to direct exposure to patients with COVID-19.
In contrast, the majority of infections among HCWs working on green
wards arose from the community. After the change in RPE, cases
attributed to ward-based exposure fell significantly, with FFP3
respirators providing 31-100\% protection (and most likely 100\%)
against infection from patients with COVID-19. In keeping with previous
observations {[}9-11{]}, our findings therefore suggest that the use of
FRSMs as RPE was insufficient to protect HCWs against infection from
patients with COVID-19. Conversely, excess infections amongst HCWs
caring for patients with COVID-19 may be prevented by the use of FFP3
respirators, in combination with other PPE and infection control
measures.

During the study period, the incidence of SARS-CoV-2 in England
increased {[}17{]}, with spread of the more transmissible B.1.1.7
variant {[}18{]}. By the ninth week of the study, 79\% of cases in
Cambridgeshire were caused by this variant {[}19{]}. Our observations on
the use of FFP3 respirators (weeks 9-11) were therefore made at a time
when the B.1.1.7 variant predominated, suggesting that they are robust
to any associated increase in SARS-CoV-2 transmissibility in a hospital
setting attributable to this variant.

Potential confounders of our observations, should they have changed over
the course of the study, include: (a) rates of natural immunity amongst
HCWs on red and green wards; however, the frequency of prior SARS-CoV-2
infections was low within CUHNFT {[}11{]}; (b) rates of vaccination of
HCWs on red and green wards; however, the proportion of high-risk HCWs
at CUHNFT offered vaccination prior to 08/01/21 was very low; (c)
frequency of screening amongst HCWs on red and green wards; however, the
proportion of cases ascertained by symptomatic testing and asymptomatic
screening was similar in both settings; (d) compliance with infection
control measures on red and green wards.

This observational study includes a small number of cases in a single
Trust, and there may be alternative explanations for the different
patterns of infection observed before and after the change in RPE.
Nonetheless, our data highlight a need for further study into the
appropriate level of RPE for HCWs caring for patients with COVID-19, as
well as other respiratory viruses. In accordance with the precautionary
principle, we propose a revision of RPE recommendations until more
definitive information is available.

\textbf{Methods}

\emph{Study design and participants}

CUHNFT is a tertiary hospital in the UK with approximately 1,000 beds.
During the pandemic, wards were categorised as ``red'', ``amber'' or
``green''. Patients with confirmed COVID-19 were cared for on red wards,
and patients who had negative SARS-CoV-2 tests and no clinical features
of COVID-19 on green wards. Patients awaiting test results, who had
clinical features of COVID-19 but a negative test result, or who may
have been exposed to SARS-CoV-2 were cared for on amber wards. RPE for
staff on red wards was changed from FRSMs to FFP3 respirators on
22/12/20. HCWs on green wards continued to wear FRSMs. HCWs on all wards
also wore eye protection.

A comprehensive PCR-based HCW screening programme is established at
CUHNFT, with symptomatic testing offered as required and asymptomatic
testing offered to all HCWs weekly {[}9, 12{]}. From 22/12/20,
twice-weekly swabbing was offered on red wards and on wards where the
most vulnerable patients were cared for. Cases were identified from a
database of all positive results, which additionally encompasses
positive results from community testing. This recorded the date of swab,
onset of symptoms (if present) and in which clinical area the HCW
worked.

The start of the study period was taken to be 02/11/20, coinciding with
an increase in community incidence of SARS-CoV-2 infection and formal
implementation of weekly asymptomatic screening for all staff members.
New infections on or prior to 27/12/20 were attributed to exposure
before the change in RPE. Infections detected later than this date were
attributed to exposure after the change in RPE. This timing was chosen
to reflect the median incubation period of SARS-CoV-2 (5.1 days), with
27/12/20 falling five days after the change in RPE {[}13, 14{]}. Since
staff testing was not conducted at weekends, eight complete weeks were
assessed in total prior to the change in RPE (Table 1).

A programme of SARS-CoV-2 vaccination using the BNT162b2 COVID-19
vaccine commenced at CUHNFT on 08/12/20 {[}15{]}. In line with UK
national guidance, the programme initially prioritised local residents
over the age of 80. However, some HCWs who had been identified as at
high risk from SARS-CoV-2 infection were also vaccinated, and were
additionally prevented from working on red wards. From 08/01/21 the
programme switched to vaccinating HCWs, with initial priority being
given to staff on red wards. To avoid the potential for confounding, the
end of the study period was therefore taken to be 15/01/21, since
minimal effect is expected in the first seven days after the first dose
of vaccine {[}16{]}.

Because of the rising number of admissions to CUHNFT with COVID-19, the
number of red wards was increased from one at the beginning of November
2020 to seven by the week starting 11/01/21. Six wards therefore changed
from green to red during the period of data collection. Of 609 positive
results over the entire study period, 169 (27.8\%) were included in this
study. Exclusions encompassed HCWs who were not ward-based or worked
between different wards with different red/amber/green status (269/609,
44.2\% of positive results), HCW working on amber wards (9/609, 1.5\%),
non-clinical staff (141/609, 23.1\%), and staff working in critical care
areas (21/609, 3.5\%), where different RPE was used throughout
(\textbf{Table 1} ).

If a staff member tested positive within five days of their ward
changing colour, their case was classified according to the red/green
status of their ward five days before their positive test (to allow for
the incubation period, as above).

\emph{General statistical analysis}

To account for changes in the number of red and green wards, weekly
numbers of red or green ward days were calculated. Where
w\textsubscript{R,d} and w\textsubscript{G,d} were the number of red and
green wards open on day d, the weekly numbers of ward days for week i,
denoted W\textsubscript{R,i} and W\textsubscript{G,i}, were calculated
as the sums of the number of each type of ward open on the days of that
respective week.

\begin{equation}
W_{G,i}=\sum_{d\in i}w_{G,d}\nonumber \\
 \end{equation}

and

\begin{equation}
W_{R,i}=\sum_{d\in i}w_{R,d}\nonumber \\
 \end{equation}

The number of HCWs per ward was similar for red and green wards. For the
purposes of comparison, we calculated the weekly number of cases amongst
HCWs on red or green wards per red or green ward day, respectively
(Table 1); we denote these weekly case numbers as R\textsubscript{i} and
G\textsubscript{i}. Wilcoxon's signed rank test was used to compare case
rates between HCWs on red or green wards before and after the change of
RPE (a non-parametric paired test).

Details of community incidence were calculated from publicly available
data describing the East of England region of the UK
(https://coronavirus.data.gov.uk/details/cases, data downloaded on
12/06/21), and were calculated as the sum of the number of cases
reported in each week of the study. Raw data are shown in \textbf{Figure
1--source data 1} . Correlations between cases per ward day and
community incidence were calculated using the Wolfram Mathematica
software package, version 12.1.0.0.

\emph{Mathematical modelling}

In order to quantify the effect of the change in RPE upon cases in red
wards, we developed a mathematical model, considering the numbers of
cases observed among HCWs as arising from a combination of ward-specific
infection risks, which relate directly to working on a red or green
ward, and non-ward-specific risks, which include infections arising from
the community. We first wrote expressions for the infection risk facing
workers in red and green wards on week i. For HCWs on green wards we
write

\begin{equation}
\lambda_{i}^{G}=\left(kC_{i-1}+g\right)W_{G,i}\nonumber \\
 \end{equation}

while for HCWs on red wards we write

\begin{equation}
\lambda_{i}^{R}=\begin{matrix}\left(kC_{i-1}+r_{1}\right)W_{R,i}\ \ \ \ \ i<9\\
\left(kC_{i-1}+r_{2}\right)W_{R,i}\ \ \ \ \ i\geq 9\\
\end{matrix}\nonumber \\
 \end{equation}

Here the term k is a constant, while the value
C\textsubscript{i-1}describes the number of observed cases in the local
community in the previous week. Our use of data from the previous week
reflects a generation time for SARS-CoV-2 of approximately seven days
{[}20{]}; we assumed that HCWs diagnosed with COVID-19 infection during
this study would have been infected by individuals who were diagnosed in
the previous week. The model parameters g, r\textsubscript{1}, and
r\textsubscript{2} describe ward-specific infection risks; FFP3 masks
were used on red wards from week 9 onwards.

Model parameters were optimised using a likelihood framework,
identifying the maximum value of the term; here the number of cases on
each type of ward each week, G\textsubscript{i} and R\textsubscript{i},
were represented as emissions from a Poisson distribution with parameter
equal to the total risk of infection.

\begin{equation}
L=\sum_{i}\left[\log{\frac{{\lambda_{i}^{G}}^{G_{i}}}{G_{i}!}\ }+\log\frac{{\lambda_{i}^{R}}^{R_{i}}}{R_{i}!}\right]\nonumber \\
 \end{equation}

Confidence intervals for each parameter were then obtained using the
likelihood function. Constrained likelihood optimisations were performed
in which the likelihood was optimised subject to a fixed value of the
parameter in question. Confidence intervals were defined as the region
of parameter space in which the likelihood L was within 2 units of the
maximum. Similarly, constrained optimisation was used to identify a
confidence interval for parameter ratios such as
r\textsubscript{2}/r\textsubscript{1}, which describes the relative risk
to HCWs on red wards with, as opposed to prior to, the introduction of
FFP3.

\emph{Ethics statement}

This study was conducted as a service evaluation of the CUHNFT staff
testing services and PPE policy (CUHNFT clinical project ID3738). As a
study of healthcare-associated infections, this investigation is exempt
from requiring ethical approval under Section 251 of the NHS Act 2006
(see also the NHS Health Research Authority algorithm, available at
http://www.hra-decision-tools.org.uk/research/, which concludes that no
formal ethical approval is required).

\textbf{Acknowledgements}

This research was funded in part by a Wellcome Trust Senior Clinical
Research Fellowship {[}108070/Z/15/Z{]} and grants from Addenbrooke's
Charitable Trust and the NIHR Cambridge Biomedical Research Centre to
MPW. NJM was funded by an MRC Clinician Scientist Fellowship
{[}MR/P008801/1{]} and NHSBT workpackage {[}WPA15-02{]}.~CJRI was
supported by UKRI through the JUNIPER modelling consortium {[}grant
number MR/V038613/1{]} and by the Medical Research Council
{[}MC\_UU\_00002/11{]}. For the purpose of open access, the authors have
applied a CC-BY public copyright licence to any author accepted
manuscript version arising from this submission. We would like to thank
everyone involved in the development and operation of the SARS-CoV-2
testing programme at CUH and the members of staff who have participated.
We would also like to thank the Infection Control and Fit Testing teams.

\textbf{Competing interests}

The authors declare no competing interests.

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\textbf{Figure supplements}

\textbf{Figure 1--figure supplement 1. Proportion of cases ascertained
by symptomatic testing and asymptomatic screening on green and red
wards}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 2--figure supplement 1. Relationships between cases per
ward day and community incidence}

\textbf{}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Presentation3/Presentation3}

\end{center}
\end{figure}

Cases per ward day amongst HCWs on green wards (\textbf{A} ) were
strongly correlated with the number of community cases identified the
previous week (p-value \textless{} 5x10\textsuperscript{-5}, Pearson
correlation test), suggesting that infection in the community explains
cases amongst HCWs on these wards. Conversely, cases per ward day
amongst HCWs on red wards (\textbf{B)} did not correlate with the
community incidence (p-value \textgreater{} 0.7, Pearson correlation
test). R\textsuperscript{2} values shown in the figures are coefficients
of determination arising from linear regression calculations performed
using the Mathematica software package (version 12.1.0.0).

\textbf{Source data}

\textbf{Figure 1--source data 1. Raw case numbers for the East of
England region during the period of study}\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
Area Type & Area Name & Area Code & Date & New cases by date & Weekly
sum of cases\tabularnewline
\midrule
\endhead
region & East of England & E12000006 & 17/01/2021 & 3011
&\tabularnewline
region & East of England & E12000006 & 16/01/2021 & 3006
&\tabularnewline
region & East of England & E12000006 & 15/01/2021 & 4471
&\tabularnewline
region & East of England & E12000006 & 14/01/2021 & 4589 &
31341\tabularnewline
region & East of England & E12000006 & 13/01/2021 & 4907 &
32065\tabularnewline
region & East of England & E12000006 & 12/01/2021 & 5227 &
33124\tabularnewline
region & East of England & E12000006 & 11/01/2021 & 6130 &
33881\tabularnewline
region & East of England & E12000006 & 10/01/2021 & 3735 &
35093\tabularnewline
region & East of England & E12000006 & 09/01/2021 & 4065 &
36433\tabularnewline
region & East of England & E12000006 & 08/01/2021 & 5228 &
38441\tabularnewline
region & East of England & E12000006 & 07/01/2021 & 5801 &
41663\tabularnewline
region & East of England & E12000006 & 06/01/2021 & 6247 &
44429\tabularnewline
region & East of England & E12000006 & 05/01/2021 & 7235 &
47652\tabularnewline
region & East of England & E12000006 & 04/01/2021 & 9352 &
46446\tabularnewline
region & East of England & E12000006 & 03/01/2021 & 6501 &
47120\tabularnewline
region & East of England & E12000006 & 02/01/2021 & 7288 &
49626\tabularnewline
region & East of England & E12000006 & 01/01/2021 & 4022 &
53511\tabularnewline
region & East of England & E12000006 & 31/12/2020 & 6475 &
50110\tabularnewline
region & East of England & E12000006 & 30/12/2020 & 8753 &
50158\tabularnewline
region & East of England & E12000006 & 29/12/2020 & 11120 &
48606\tabularnewline
region & East of England & E12000006 & 28/12/2020 & 5951 &
46320\tabularnewline
region & East of England & E12000006 & 27/12/2020 & 6549 &
44180\tabularnewline
region & East of England & E12000006 & 26/12/2020 & 5736 &
41282\tabularnewline
region & East of England & E12000006 & 25/12/2020 & 1736 &
36518\tabularnewline
region & East of England & E12000006 & 24/12/2020 & 4335 &
37259\tabularnewline
region & East of England & E12000006 & 23/12/2020 & 5855 &
35301\tabularnewline
region & East of England & E12000006 & 22/12/2020 & 6356 &
32961\tabularnewline
region & East of England & E12000006 & 21/12/2020 & 6692 &
36360\tabularnewline
region & East of England & E12000006 & 20/12/2020 & 4591 &
36645\tabularnewline
region & East of England & E12000006 & 19/12/2020 & 3396 &
35505\tabularnewline
region & East of England & E12000006 & 18/12/2020 & 5135 &
33485\tabularnewline
region & East of England & E12000006 & 17/12/2020 & 4620 &
31219\tabularnewline
region & East of England & E12000006 & 16/12/2020 & 4715 &
29375\tabularnewline
region & East of England & E12000006 & 15/12/2020 & 4336 &
28224\tabularnewline
region & East of England & E12000006 & 14/12/2020 & 4426 &
25781\tabularnewline
region & East of England & E12000006 & 13/12/2020 & 2747 &
23475\tabularnewline
region & East of England & E12000006 & 12/12/2020 & 2245 &
21013\tabularnewline
region & East of England & E12000006 & 11/12/2020 & 2692 &
18765\tabularnewline
region & East of England & E12000006 & 10/12/2020 & 2314 &
16535\tabularnewline
region & East of England & E12000006 & 09/12/2020 & 2253 &
15089\tabularnewline
region & East of England & E12000006 & 08/12/2020 & 2088 &
14031\tabularnewline
region & East of England & E12000006 & 07/12/2020 & 2196 &
12718\tabularnewline
region & East of England & E12000006 & 06/12/2020 & 1301 &
11742\tabularnewline
region & East of England & E12000006 & 05/12/2020 & 1187 &
10927\tabularnewline
region & East of England & E12000006 & 04/12/2020 & 1379 &
10122\tabularnewline
region & East of England & E12000006 & 03/12/2020 & 1338 &
9441\tabularnewline
region & East of England & E12000006 & 02/12/2020 & 1438 &
8908\tabularnewline
region & East of England & E12000006 & 01/12/2020 & 1283 &
8528\tabularnewline
region & East of England & E12000006 & 30/11/2020 & 1515 &
8280\tabularnewline
region & East of England & E12000006 & 29/11/2020 & 768 &
7970\tabularnewline
region & East of England & E12000006 & 28/11/2020 & 807 &
7735\tabularnewline
region & East of England & E12000006 & 27/11/2020 & 1131 &
7481\tabularnewline
region & East of England & E12000006 & 26/11/2020 & 1028 &
7203\tabularnewline
region & East of England & E12000006 & 25/11/2020 & 1203 &
7238\tabularnewline
region & East of England & E12000006 & 24/11/2020 & 1029 &
7233\tabularnewline
region & East of England & E12000006 & 23/11/2020 & 1237 &
7158\tabularnewline
region & East of England & E12000006 & 22/11/2020 & 803 &
7307\tabularnewline
region & East of England & E12000006 & 21/11/2020 & 802 &
7354\tabularnewline
region & East of England & E12000006 & 20/11/2020 & 1056 &
7642\tabularnewline
region & East of England & E12000006 & 19/11/2020 & 1177 &
7998\tabularnewline
region & East of England & E12000006 & 18/11/2020 & 1250 &
8108\tabularnewline
region & East of England & E12000006 & 17/11/2020 & 1317 &
8289\tabularnewline
region & East of England & E12000006 & 16/11/2020 & 1593 &
8594\tabularnewline
region & East of England & E12000006 & 15/11/2020 & 913 &
8794\tabularnewline
region & East of England & E12000006 & 14/11/2020 & 983 &
9153\tabularnewline
region & East of England & E12000006 & 13/11/2020 & 1361 &
9293\tabularnewline
region & East of England & E12000006 & 12/11/2020 & 1377 &
9499\tabularnewline
region & East of England & E12000006 & 11/11/2020 & 1609 &
9666\tabularnewline
region & East of England & E12000006 & 10/11/2020 & 1457 &
9690\tabularnewline
region & East of England & E12000006 & 09/11/2020 & 1799 &
9438\tabularnewline
region & East of England & E12000006 & 08/11/2020 & 1080 &
9176\tabularnewline
region & East of England & E12000006 & 07/11/2020 & 1007 &
8612\tabularnewline
region & East of England & E12000006 & 06/11/2020 & 1109 &
8301\tabularnewline
region & East of England & E12000006 & 05/11/2020 & 1115 &
7876\tabularnewline
region & East of England & E12000006 & 04/11/2020 & 1045 &
7432\tabularnewline
region & East of England & E12000006 & 03/11/2020 & 1146 &
7147\tabularnewline
region & East of England & E12000006 & 02/11/2020 & 1374 &
7030\tabularnewline
region & East of England & E12000006 & 01/11/2020 & 636 &
6889\tabularnewline
region & East of England & E12000006 & 31/10/2020 & 722 &
6863\tabularnewline
region & East of England & E12000006 & 30/10/2020 & 992 &
6653\tabularnewline
region & East of England & E12000006 & 29/10/2020 & 974 &
6423\tabularnewline
region & East of England & E12000006 & 28/10/2020 & 1019
&\tabularnewline
region & East of England & E12000006 & 27/10/2020 & 936 &\tabularnewline
region & East of England & E12000006 & 26/10/2020 & 1144
&\tabularnewline
\bottomrule
\end{longtable}

\selectlanguage{english}
\FloatBarrier
\end{document}

