Architectural interventions to mitigate the spread of SARS-CoV-2 in emergency departments.

INTRODUCTION: Emergency departments (EDs) are a critical entry gate for infectious agents into hospitals. In this interdisciplinary study, we explore how infection prevention and control (IPC) architectural interventions mitigate the spread of emerging respiratory pathogens using the example of SARS-CoV-2 in a prototypical ED. METHODS: Using an agent-based approach, we integrate data on patients' and healthcare workers' (HCWs) routines and the architectural characteristics of key ED areas. We estimate the number of transmissions in the ED by modelling the interactions between and among patients and HCWs. Architectural interventions are guided towards the gradual separation of pathogen carriers, compliance with a minimum interpersonal distance, and deconcentrating airborne pathogens (higher air exchange rates (AERs)). Interventions are epidemiologically evaluated for their mitigation effects on diverse endpoints. RESULTS: Simulation results indicate that higher AERs in the ED (compared to baseline) may provide a moderate level of infection mitigation (incidence rate ratio (IRR) of 0.95 (95% CI 0.93 - 0.98)) while the overall burden decreases more when separating rooms in examination areas (IRR of 0.78 (95% CI 0.76 - 0.81)) or when increasing the size of the ED base (IRR of 0.79 (95% CI 0.78 - 0.81)). The reduction in SARS-CoV-2-associated nosocomial transmissions is largest when combining architectural interventions (IRR of 0.61 (95% CI 0.59 - 0.63)). CONCLUSIONS: These modelling results highlight the importance of IPC architectural interventions; they can be devised independently of profound knowledge of an emerging pathogen, focusing on technical, constructive, and functional components. These results may inform public health decision-makers and hospital architects on how IPC architectural interventions can be optimally used in healthcare premises.

Estimates of protection levels against SARS-CoV-2 infection and severe COVID-19 in Germany before the 2022/2023 winter season: the IMMUNEBRIDGE project.

PURPOSE: Despite the need to generate valid and reliable estimates of protection levels against SARS-CoV-2 infection and severe course of COVID-19 for the German population in summer 2022, there was a lack of systematically collected population-based data allowing for the assessment of the protection level in real time. METHODS: In the IMMUNEBRIDGE project, we harmonised data and biosamples for nine population-/hospital-based studies (total number of participants n = 33,637) to provide estimates for protection levels against SARS-CoV-2 infection and severe COVID-19 between June and November 2022. Based on evidence synthesis, we formed a combined endpoint of protection levels based on the number of self-reported infections/vaccinations in combination with nucleocapsid/spike antibody responses ("confirmed exposures"). Four confirmed exposures represented the highest protection level, and no exposure represented the lowest. RESULTS: Most participants were seropositive against the spike antigen; 37% of the participants ≥ 79 years had less than four confirmed exposures (highest level of protection) and 5% less than three. In the subgroup of participants with comorbidities, 46-56% had less than four confirmed exposures. We found major heterogeneity across federal states, with 4-28% of participants having less than three confirmed exposures. CONCLUSION: Using serological analyses, literature synthesis and infection dynamics during the survey period, we observed moderate to high levels of protection against severe COVID-19, whereas the protection against SARS-CoV-2 infection was low across all age groups. We found relevant protection gaps in the oldest age group and amongst individuals with comorbidities, indicating a need for additional protective measures in these groups.

Effect of risk status for severe COVID-19 on individual contact behaviour during the SARS-CoV-2 pandemic in 2020/2021-an analysis based on the German COVIMOD study.

BACKGROUND: One of the primary aims of contact restriction measures during the SARS-CoV-2 pandemic has been to protect people at increased risk of severe disease from the virus. Knowledge about the uptake of contact restriction measures in this group is critical for public health decision-making. We analysed data from the German contact survey COVIMOD to assess differences in contact patterns based on risk status, and compared this to pre-pandemic data to establish whether there was a differential response to contact reduction measures. METHODS: We quantified differences in contact patterns according to risk status by fitting a generalised linear model accounting for within-participant clustering to contact data from 31 COVIMOD survey waves (April 2020-December 2021), and estimated the population-averaged ratio of mean contacts of persons with high risk for a severe COVID-19 outcome due to age or underlying health conditions, to those without. We then compared the results to pre-pandemic data from the contact surveys HaBIDS and POLYMOD. RESULTS: Averaged across all analysed waves, COVIMOD participants reported a mean of 3.21 (95% confidence interval (95%CI) 3.14,3.28) daily contacts (truncated at 100), compared to 18.10 (95%CI 17.12,19.06) in POLYMOD and 28.27 (95%CI 26.49,30.15) in HaBIDS. After adjusting for confounders, COVIMOD participants aged 65 or above had 0.83 times (95%CI 0.79,0.87) the number of contacts as younger age groups. In POLYMOD, this ratio was 0.36 (95%CI 0.30,0.43). There was no clear difference in contact patterns due to increased risk from underlying health conditions in either HaBIDS or COVIMOD. We also found that persons in COVIMOD at high risk due to old age increased their non-household contacts less than those not at such risk after strict restriction measures were lifted. CONCLUSIONS: Over the course of the SARS-CoV-2 pandemic, there was a general reduction in contact numbers in the German population and also a differential response to contact restriction measures based on risk status for severe COVID-19. This differential response needs to be taken into account for parametrisations of mathematical models in a pandemic setting.