Candida auris is an emerging multidrug-resistant fungal pathogen associated with bloodstream, wound and other infections, especially in critically ill patients. C. auris carriage is persistent and is difficult to eradicate from the hospital environment.

We aimed to pilot admission screening for C. auris in intensive care units (ICUs) in England to estimate prevalence in the ICU population and to inform public health guidance.

Between May 2017 and April 2018, we screened admissions to eight adult ICUs in hospitals with no previous cases of C. auris, in three major cities. Swabs were taken from the nose, throat, axilla, groin, perineum, rectum and catheter urine, then cultured and identified using matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS). Patient records were linked to routine ICU data to describe and compare the demographic and health indicators of the screened cohort with a national cohort of ICU patients admitted between 2016 and 2017.

All C. auris screens for 921 adults from 998 admissions were negative. The upper confidence limit of the pooled prevalence across all sites was 0.4%. Comparison of the screened cohort with the national cohort showed it was broadly similar to the national cohort with respect to demographics and co-morbidities.

These findings imply that C. auris colonisation among patients admitted to ICUs in England is currently rare. We would not currently recommend widespread screening for C. auris in ICUs in England. Hospitals should continue to screen high-risk individuals based on local risk assessment.

Timely monitoring of COVID-19 impact on mortality is critical for rapid risk assessment and public health action.

Building upon well-established models to estimate influenza-related mortality, we propose a new statistical Attributable Mortality Model (AttMOMO), which estimates mortality attributable to one or more pathogens simultaneously (e.g. SARS-CoV-2 and seasonal influenza viruses), while adjusting for seasonality and excess temperatures.

Data from Nationwide Danish registers from 2014-week(W)W27 to 2020-W22 were used to exemplify utilities of the model, and to estimate COVID-19 and influenza attributable mortality from 2019-W40 to 2020-W20.

SARS-CoV-2 was registered in Denmark from 2020-W09. Mortality attributable to COVID-19 in Denmark increased steeply, and peaked in 2020-W14. As preventive measures and national lockdown were implemented from 2020-W12, the attributable mortality started declining within a few weeks. Mortality attributable to COVID-19 from 2020-W09 to 2020-W20 was estimated to 16.2 (95% confidence interval (CI): 12.0 to 20.4) per 100,000 person-years. The 2019/20 influenza season was mild with few deaths attributable to influenza, 3.2 (95% CI: 1.1 to 5.4) per 100,000 person-years.

AttMOMO estimates mortality attributable to several pathogens simultaneously, providing a fuller picture of mortality by COVID-19 during the pandemic in the context of other seasonal diseases and mortality patterns. Using Danish data, we show that the model accurately estimates mortality attributable to COVID-19 and influenza, respectively. We propose using standardised indicators for pathogen circulation in the population, to make estimates comparable between countries and applicable for timely monitoring.

During the COVID-19 pandemic, many countries have implemented physical distancing measures to reduce transmission of SARS-CoV-2.

To measure the actual reduction of contacts when physical distancing measures are implemented.

A cross-sectional survey was carried out in the Netherlands in 2016–17, in which participants reported the number and age of their contacts the previous day. The survey was repeated among a subsample of the participants in April 2020, after strict physical distancing measures were implemented, and in an extended sample in June 2020, after some measures were relaxed.

The average number of community contacts per day was reduced from 14.9 (interquartile range (IQR): 4–20) in the 2016–17 survey to 3.5 (IQR: 0–4) after strict physical distancing measures were implemented, and rebounded to 8.8 (IQR: 1–10) after some measures were relaxed. All age groups restricted their community contacts to at most 5, on average, after strict physical distancing measures were implemented. In children, the number of community contacts reverted to baseline levels after measures were eased, while individuals aged 70 years and older had less than half their baseline levels.

Strict physical distancing measures greatly reduced overall contact numbers, which likely contributed to curbing the first wave of the COVID-19 epidemic in the Netherlands. However, age groups reacted differently when measures were relaxed, with children reverting to normal contact numbers and elderly individuals maintaining restricted contact numbers. These findings offer guidance for age-targeted measures in future waves of the pandemic.