Impact of public health interventions to curb SARS-CoV-2 spread assessed by an evidence-educated Delphi panel and tailored SEIR model.

AIM: To use a Delphi-panel-based assessment of the effectiveness of different non-pharmaceutical interventions (NPI) in order to retrospectively approximate and to prospectively predict the SARS-CoV-2 pandemic progression via a SEIR model (susceptible, exposed, infectious, removed). METHODS: We applied an evidence-educated Delphi-panel approach to elicit the impact of NPIs on the SARS-CoV-2 transmission rate R0 in Germany. Effectiveness was defined as the product of efficacy and compliance. A discrete, deterministic SEIR model with time step of 1 day, a latency period of 1.8 days, duration of infectiousness of 5 days, and a share of the total population of 15% assumed to be protected by immunity was developed in order to estimate the impact of selected NPI measures on the course of the pandemic. The model was populated with the Delphi-panel results and varied in sensitivity analyses. RESULTS: Efficacy and compliance estimates for the three most effective NPIs were as follows: test and isolate 49% (efficacy)/78% (compliance), keeping distance 42%/74%, personal protection masks (cloth masks or other face masks) 33%/79%. Applying all NPI effectiveness estimates to the SEIR model resulted in a valid replication of reported occurrence of the German SARS-CoV-2 pandemic. A combination of four NPIs at consented compliance rates might curb the CoViD-19 pandemic. CONCLUSION: Employing an evidence-educated Delphi-panel approach can support SARS-CoV-2 modelling. Future curbing scenarios require a combination of NPIs. A Delphi-panel-based NPI assessment and modelling might support public health policy decision making by informing sequence and number of needed public health measures. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10389-021-01566-2.

Prevalence and risk factors for venous thromboembolic events in critically ill patients with SARS-CoV-2 infection: a prospective observational study.

BACKGROUND: The majority of prevalence studies on deep vein thrombosis (DVT) in severe COVID-19 patients are retrospective with DVT assessment based on clinical suspicion. Our aim was to prospectively and systematically estimate the occurrence of DVT in critically-ill mechanically-ventilated patients, and to identify potential risk factors for DVT occurrence and mortality. METHODS: All patients with COVID-19 admitted to our 45 beds in the Intensive Care Unit (ICU) between March 6, 2020, and April 18, 2020, requiring invasive ventilatory support were daily screened for DVT with lower extremities and jugular veins ultrasonography. Univariate and multivariable logistic regression models were performed in order to identify predictors of DVT and mortality. RESULTS: Seventy-six patients were included in the final analysis (56 men, mean age 67 years, median SOFA=7 points, median SAPS II=41 points, median PaO2/Fi02=10.8 kPa). The period prevalence of DVT was 40.8%. Thirty-one DVTs were diagnosed. Twenty-five DVTs (80.6% of total DVTs) were catheter-related, mainly in the jugular veins. Twenty-six DVTs (83.9%) occurred in patients receiving enhanced antithrombotic prophylaxis. No independent variable was predictive of DVT occurrence. Twenty-eight patients (36.8%) died during the ICU stay. Age and SOFA score were independently associated with mortality. CONCLUSIONS: A high number of critically-ill mechanically-ventilated COVID-19 patients developed a DVT. The majority of DVTs were catheter-related and occurred under intensive prophylactic anticoagulation. Routine ultrasound of the jugular veins should be suggested in this patient population, and in particular in presence of a central venous catheter.