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Additional file 9. Rt estimates.

Additional file 3. Admission with infection levels.

Additional file 2. Datasets. Trust and case number differences; LoS distributions.

Additional file 1. Definitions.

Additional file 12. Grouped trust level analysis.

Additional file 4. Comparing COCIN and SUS by week.

Additional file 8. Infection to discharge calculations.

Additional file 7. Symptom onset to hospitalisation.

Additional file 5. Calculations of proportion undetected hospital-acquired SARS-CoV-2 infection.

Additional file 6. Parameterisation and additional methods.

Additional file 8. Infection to discharge calculations.

Additional file 11. Additional results.

Additional file 10. Uncertainty inclusion.

Additional file 9. Rt estimates.

Nosocomial transmission of SARS-CoV-2 is a key concern, and evaluating the effect of testing and infection prevention and control strategies is essential for guiding policy in this area. Using a within-hospital SEIR transition model of SARS-CoV-2 in a typical English hospital, we estimate that between 9 March 2020 and 17 July 2020 approximately 20% of infections in inpatients, and 73% of infections in healthcare workers (HCWs) were due to nosocomial transmission. Model results suggest that placing suspected COVID-19 patients in single rooms or bays has the potential to reduce hospital-acquired infections in patients by up to 35%. Periodic testing of HCWs has a smaller effect on the number of hospital-acquired COVID-19 cases in patients, but reduces infection in HCWs by as much as 37% and results in only a small proportion of staff absences (approx. 0.3% per day). This is considerably less than the 20–25% of staff that have been reported to be absent from work due to suspected COVID-19 and self-isolation. Model-based evaluations of interventions, informed by data collected so far, can help to inform policy as the pandemic progresses and help prevent transmission in the vulnerable hospital population.This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’.

The discussion of presidential mandates is as certain as a presidential election itself. Journalists inevitably discuss whether the president-elect has a popular mandate. Because they see elections as too complex to allow the public to send a unitary signal, political scientists are more skeptical of mandates. Mandates, however, have received new attention by scholars asking whether perceptions of mandate arise and lead representatives to act as if voters sent a policy directive. Two explanations have emerged to account for why elected officials might react to such perceptions. One focuses on the President’s strategic decision to declare a mandate, the second on how members of Congress read signals of changing preferences in the electorate from their own election results. We test these competing views to see which more accurately explains how members of Congress act in support of a perceived mandate. The results indicate that members respond more to messages about changing preferences than to the president’s mandate declaration.

Funder: Huo Family Foundation; doi: http://dx.doi.org/10.13039/501100022111, Background From March 2020 through August 2021, 97,762 hospital-onset SARS-CoV-2 infections were detected in English hospitals. Resulting excess length of stay (LoS) created a potentially substantial health and economic burden for patients and the NHS, but we are currently unaware of any published studies estimating this excess. Methods We implemented appropriate causal inference methods to determine the extent to which observed additional hospital stay is attributable to the infection rather than the characteristics of the patients. Hospital admissions records were linked to SARS-CoV-2 test data to establish the study population (7.5 million) of all non-COVID-19 admissions to English hospitals from 1st March 2020 to 31st August 2021 with a stay of at least two days. The excess LoS due to hospital-onset SARS-CoV-2 infection was estimated as the difference between the mean LoS observed and in the counterfactual where infections do not occur. We used inverse probability weighted Kaplan–Meier curves to estimate the mean survival time if all hospital-onset SARS-CoV-2 infections were to be prevented, the weights being based on the daily probability of acquiring an infection. The analysis was carried out for four time periods, reflecting phases of the pandemic differing with respect to overall case numbers, testing policies, vaccine rollout and prevalence of variants. Results The observed mean LoS of hospital-onset cases was higher than for non-COVID-19 hospital patients by 16, 20, 13 and 19 days over the four phases, respectively. However, when the causal inference approach was used to appropriately adjust for time to infection and confounding, the estimated mean excess LoS caused by hospital-onset SARS-CoV-2 was: 2.0 [95% confidence interval 1.8–2.2] days (Mar-Jun 2020), 1.4 [1.2–1.6] days (Sep–Dec 2020); 0.9 [0.7–1.1] days (Jan–Apr 2021); 1.5 [1.1–1.9] days (May–Aug 2021). Conclusions Hospital-onset SARS-CoV-2 is associated with a small but notable excess LoS, equivalent to 130,000 bed days. The comparatively high LoS observed for hospital-onset COVID-19 patients is mostly explained by the timing of their infections relative to admission. Failing to account for confounding and time to infection leads to overestimates of additional length of stay and therefore overestimates costs of infections, leading to inaccurate evaluations of control strategies.

Nosocomial transmission of SARS-CoV-2 is a key concern, and evaluating the effect of testing and infection prevention and control strategies is essential for guiding policy in this area. Using a within-hospital SEIR transition model of SARS-CoV-2 in a typical English hospital, we estimate that between 9 March 2020 and 17 July 2020 approximately 20% of infections in inpatients, and 73% of infections in healthcare workers (HCWs) were due to nosocomial transmission. Model results suggest that placing suspected COVID-19 patients in single rooms or bays has the potential to reduce hospital-acquired infections in patients by up to 35%. Periodic testing of HCWs has a smaller effect on the number of hospital-acquired COVID-19 cases in patients, but reduces infection in HCWs by as much as 37% and results in only a small proportion of staff absences (approx. 0.3% per day). This is considerably less than the 20–25% of staff that have been reported to be absent from work owing to suspected COVID-19 and self-isolation. Model-based evaluations of interventions, informed by data collected so far, can help to inform policy as the pandemic progresses and help prevent transmission in the vulnerable hospital population. This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’.