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
wR,d and wG,d were the number of red and
green wards open on day d, the weekly numbers of ward days for week i,
denoted WR,i and WG,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 Ri and
Gi. 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 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.