Estimating the Course of the COVID-19 Pandemic in Germany via Spline-based Hierarchical Modelling of Death Counts (preprint)

The effective reproduction number is a key figure in context of the COVID-19 pandemic, which is typically estimated based on daily confirmed cases. Here, we consider a retrospective modelling approach for estimating effective reproduction numbers based on death counts during the first year of the pandemic in Germany. The proposed Bayesian hierarchical model incorporates splines to estimate reproduction numbers flexibly over time while adjusting for varying effective infection fatality rates. The approach also provides estimates of dark figures regarding undetected infections over time. Results for Germany illustrate that estimates based on death counts are often similar to classical estimates based on confirmed cases. However, considering death counts is more robust against shifts in testing policies: in particular, confirmed cases indicate a spike in the effective reproduction number linked to a local super-spreading event in June 2020, whereas our model does not estimate a spike but reduced dark figures of infections. During the second wave of infections, classical estimates suggest a flattening trend of infections following the "lockdown light" in November 2020, while our results indicate that infections continued to rise until the "second lockdown" in December 2020. This observation is associated with more stringent testing criteria introduced concurrently with the "lockdown light", which is reflected in subsequently increasing dark figures of infections estimated by our model. In light of progressive vaccinations, shifting the focus from modelling confirmed cases to reported deaths with the possibility to incorporate effective infection fatality rates might be of increasing relevance for the future surveillance of the pandemic.

Estimating the course of the COVID-19 pandemic in Germany via spline-based hierarchical modelling of death counts (preprint)

The effective reproduction number is a key figure to monitor the course of the COVID-19 pandemic. In this study we consider a retrospective modelling approach for estimating the effective reproduction number based on death counts during the first year of the pandemic in Germany. The proposed Bayesian hierarchical model incorporates splines to estimate reproduction numbers flexibly over time while adjusting for varying effective infection fatality rates. The approach also provides estimates of dark figures regarding undetected infections over time. Results for Germany illustrate that estimated reproduction numbers based on death counts are often similar to classical estimates based on confirmed cases. However, considering death counts proves to be more robust against shifts in testing policies: during the second wave of infections, classical estimation of the reproduction number suggests a flattening/ decreasing trend of infections following the "lockdown light" in November 2020, while our results indicate that true numbers of infections continued to rise until the "second lockdown" in December 2020. This observation is associated with more stringent testing criteria introduced concurrently with the "lockdown light", which is reflected in subsequently increasing dark figures of infections estimated by our model. These findings illustrate that the retrospective viewpoint can provide additional insights regarding the course of the pandemic. In light of progressive vaccinations, shifting the focus from modelling confirmed cases to reported deaths with the possibility to incorporate effective infection fatality rates might be of increasing relevance for the future surveillance of the pandemic.

Estimating effective infection fatality rates during the course of the COVID-19 pandemic in Germany (preprint)

The infection fatality rate (IFR) of the Coronavirus Disease 2019 (COVID-19) is one of the most discussed figures in the context of this pandemic. Using German COVID-19 surveillance data and age-group specific IFR estimates from multiple international studies, this work investigates time-dependent variations in effective IFR over the course of the pandemic. Three different methods for estimating (effective) IFRs are presented: (a) population-averaged IFRs based on the assumption that the infection risk is independent of age and time, (b) effective IFRs based on the assumption that the age distribution of confirmed cases approximately reflects the age distribution of infected individuals, and (c) effective IFRs accounting for age- and time-dependent dark figures of infections. Results show that effective IFRs in Germany are estimated to vary over time, as the age distributions of confirmed cases and estimated infections are changing during the course of the pandemic. In particular during the first and second waves of infections in spring and autumn/winter 2020, there has been a pronounced shift in the age distribution of confirmed cases towards older age groups, resulting in larger effective IFR estimates. The temporary increase in effective IFR during the first wave is estimated to be smaller but still remains when adjusting for age- and time-dependent dark figures. A comparison of effective IFRs with observed CFRs indicates that a substantial fraction of the time-dependent variability in observed mortality can be explained by changes in the age distribution of infections. Furthermore, a vanishing gap between effective IFRs and observed CFRs is apparent after the first infection wave, while a moderately increasing gap can be observed during the second wave. Further research is warranted to obtain timely age-stratified IFR estimates.

Structured ICU Resource Management in a Pandemic is Associated with Good Outcome in Critically Ill COVID-19 Patients (preprint)

Introduction: On February 25 th , 2020, the first two patients were tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Tyrol, Austria. Based on alarming reports from the neighboring region Lombardy in Italy, rapid measures were taken to ensure adequate intensive care unit (ICU) preparedness for a surge of critically ill coronavirus disease 2019 (COVID-19) patients. Methods: A coordinated county wide step-up approach ensured adequate ICU bed availability for COVID-19 patients avoiding shortage of mechanical ventilation capacity. All patients admitted to an ICU with confirmed or strongly suspected COVID-19 in the region of Tyrol, Austria were recorded in the Tyrolean COVID-19 Intensive Care Registry. Data were censored on July 17 th , 2020. Results: From March 9 th , 2020 to July 17 th , 2020, 106 critically ill patients with COVID-19 were admitted to an ICU. Median age was 64 (interquartile range [IQR], 54-74) years and the majority of patients were male (76 patients [71.7%]). Median simplified acute physiology score III (SAPS III) was 56 (IQR, 49-64) points. The median duration from appearance of first symptoms to ICU admission was 8 (IQR, 5-11) days. Frequently observed comorbidities were arterial hypertension in 71 patients (67.0%), cardiovascular (45 patients [42.5%]) and renal comorbidities (21 patients [19.8%]). Invasive mechanical ventilation was required in 72 patients (67.9%), 6 patients (5.6%) required extracorporeal membrane oxygenation treatment. Renal replacement therapy was necessary in 21 patients (19.8%). Median ICU length of stay (LOS) was 18 (IQR, 5-31) days, median hospital LOS was 27 (IQR, 13-49) days.ICU mortality was 21.7% (23 patients), while only one patient (0.9%) died after ICU discharge on a general ward (hospital mortality 22,6%). As of July 17 th , 2020, two patients are still hospitalized, one in an ICU, one on a general ward. Conclusions: Critically ill COVID-19 patients admitted to an ICU in the region of Tyrol, Austria, showed a high severity of disease often requiring complex treatments with increased lengths of ICU- and hospital stay. Despite that, we found ICU and hospital mortality in this cohort to be remarkably low. Adaptive surge response providing sufficient ICU resources presumably has contributed to the overall favorable outcome.

Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks (preprint)

Due to the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there is an urgent need for novel therapies and drugs. We conducted a large-scale virtual screening for small molecules that are potential CoV-2 inhibitors. To this end, we utilized "ChemAI", a deep neural network trained on more than 220M data points across 3.6M molecules from three public drug-discovery databases. With ChemAI, we screened and ranked one billion molecules from the ZINC database for favourable effects against CoV-2. We then reduced the result to the 30,000 top-ranked compounds, which are readily accessible and purchasable via the ZINC database. Additionally, we screened the DrugBank using ChemAI to allow for drug repurposing, which would be a fast way towards a therapy. We provide these top-ranked compounds of ZINC and DrugBank as a library for further screening with bioassays at https://github.com/ml-jku/sars-cov-inhibitors-chemai.