SARS-CoV-2 seroprevalence in Germany - a population based sequential study in five regions (preprint)

Prevalence of SARS-CoV-2 antibodies is an essential indicator to guide measures. Few population-based estimates are available in Germany. We determine seroprevalence allowing comparison between regions, time points, socio-demographic and health-related factors. MuSPAD is a sequential multi-local seroprevalence study. We randomly recruited adults in five counties with differing cumulative SARS-CoV-2 incidence July 2020 - February 2021. Serostatus was determined using Spike S1-specific IgG ELISA. We determined county-wise proportions of seropositivity. We assessed underestimation of infections, county and age specific infection fatality risks, and association of seropositivity with demographic, socioeconomic and health factors. We found seroprevalence of 2.4 % (95%CI: 1.8-3.1%) for Reutlingen in June 2020 (stage 1) which increased to 2.9% (95%CI: 2.1-3.8%) in October (stage 2), Freiburg stage 1 1.5% (95% CI: 1.1-2.1%) vs. 2.5% (95%CI: 1.8-3.4%), Aachen stage 1 2.3% (95% CI: 1.7-3.1%) vs. 5.4% (95%CI: 4.4-6.6%), Osnabrück 1.3% (95% CI: 1.0-1.9%) and Magdeburg in Nov/Dec 2020. 2.4% (95%CI 1.9-3.1%). Number needed to quarantine to have one infected person quarantined was 8.2. The surveillance detection ratio (SDR) between number of infections based on our results and number reported to health authorities ranged from 2.5-4.5. Participants aged 80+ had lower SDR. Infection fatality estimates ranged from 0.2-2.4%. Lower education was associated with higher, smoking with lower seropositivity. Seroprevalence remained low until December 2020 with high underdetection. The second wave from November 2020 to February 2021 resulted in additional 2-5% of the population being infected. Detected age specific differences of SDR should be taken into account in modelling and forecasting COVID-19 morbidity. Highlights Evidence before this study Seroepidemiological surveys on SARS-CoV-2 are a useful tool to track the transmission during the epidemic. We searched PubMed/the pre-print server medRxiv and included web-based reports from German health organizations using the keywords “seroprevalence”, “SARS-CoV-2”, “Germany” and similar other English and German terms in the period from January 1st, 2020 until March 2021. We identified 30 published studies in Germany which mostly report low SARS-CoV-2 seroprevalence (<5%). Most of these surveys were so-called hotspot studies which assessed seroprevalence after localized outbreaks or examined seroprevalence of specific population groups such as e.g. medical staff. Few studies are either population-based or blood donor-based, but do not allow comparisons between regions. To date, we only consider the Corona sub-study of the Rhineland study similar to MuSPAD. It reports a low SARS-CoV-2 seroprevalence (46/4755; 0.97%; 95% CI: 0.72−1.30). Based on this, almost the entire German population remained susceptible to a SARS-CoV-2 infection by the end of 2020. Added value of this study We provide the first comprehensive, high-precision multi-region population-based SARS-CoV-2 seroprevalence study with representative sampling following the WHO protocol in Germany. By measuring SARS-CoV-2 IgG, we explore immunity at regional and national level over time. We also assess risk factors and sample each region twice, which permits to monitor seroprevalence progression throughout the epidemic in different exemplary German regions. Implications of all the available evidence Our results show low seroprevalence (<3%) until Mid-December 2020 in all regions. While estimates in Reutlingen, Aachen, Freiburg and Osnabrück reflect low seroprevalence mostly after the first wave, the survey in Magdeburg cumulatively already represents the beginning of the second wave. The number needed to quarantine to ensure one infected person was quarantined was 8.2 in our study. We also show that for the first wave reported infections reflected overall around 25% of those actually infected rising to 40-50% in the second wave. A slightly raised infection risk could be shown for persons with lower education.

Navigating hospitals safely through the COVID-19 epidemic tide: predicting case load for adjusting bed capacity (preprint)

BackgroundThe pressures exerted by the pandemic of COVID-19 pose an unprecedented demand on health care services. Hospitals become rapidly overwhelmed when patients requiring life-saving support outpace available capacities. We here describe methods used by a university hospital to forecast caseloads and time to peak incidence. MethodsWe developed a set of models to forecast incidence among the hospital catchment population and describe the COVID-19 patient hospital care-path. The first forecast utilized data from antecedent allopatric epidemics and parameterized the care path model according to expert opinion (static model). Once sufficient local data were available, trends for the time dependent effective reproduction number were fitted and the care-path was parameterized using hazards for real patient admission, referrals, and discharge (dynamic model). ResultsThe static model, deployed before the epidemic, exaggerated the bed occupancy (general wards 116 forecasted vs 66 observed, ICU 47 forecasted vs 34 observed) and predicted the peak too late (general ward forecast April 9, observed April 8, ICU forecast April 19, observed April 8). After April 5, the dynamic model could be run daily and precision improved with increasing availability of empirical local data. ConclusionsThe models provided data-based guidance in the preparation and allocation of critical resources of a university hospital well in advance of the epidemic surge, despite overestimating the service demand. Overestimates should resolve when population contact pattern before and during restrictions can be taken into account, but for now they may provide an acceptable safety margin for preparing during times of uncertainty.