Coronavirus disease (COVID-19)

Robust data on SARS-CoV-2 population seroprevalence supplement surveillance data in providing evidence for public health action.

To conduct a SARS-CoV-2 population-based seroprevalence survey in Ireland.

Using a cross-sectional study design, we selected population samples from individuals aged 12–69 years in counties Dublin and Sligo using the Health Service Executive Primary Care Reimbursement Service database as a sampling frame. Samples were selected with probability proportional to the general population age–sex distribution, and by simple random sampling within age–sex strata. Antibodies to SARS-CoV-2 were detected using the Abbott Architect SARS-CoV-2 IgG Assay and confirmed using the Wantai Assay. We estimated the population SARS-CoV-2 seroprevalence weighted for age, sex and geographic area.

Participation rates were 30% (913/3,043) and 44% (820/1,863) in Dublin and Sligo. Thirty-three specimens had detectable SARS-CoV-2 antibodies (1.9%). We estimated weighted seroprevalences of 3.12% (95% confidence interval (CI): 2.05–4.53) and 0.58% (95% CI: 0.18–1.38) for Dublin and Sligo, and 1.69% (95% CI: 1.13–2.41) nationally. This equates to an estimated 59,482 (95% CI: 39,772–85,176) people aged 12–69 years nationally having had infection with SARS-CoV-2, 3.0 (95% CI: 2.0–4.3) times higher than confirmed notifications. Ten participants reported a previous laboratory-confirmed SARS-CoV-2 -infection; eight of these were antibody-positive. Twenty-five antibody-positive participants had not reported previous laboratory-confirmed infection.

The majority of people in Ireland are unlikely to have been infected with SARS-CoV-2 by June–July 2020. Non-pharmaceutical public health measures remained key pending widespread availability of vaccination, and effective treatments.

Numerous CE-marked SARS-CoV-2 antigen rapid diagnostic tests (Ag RDT) are offered in Europe, several of them with unconfirmed quality claims.

We performed an independent head-to-head evaluation of the sensitivity of SARS-CoV-2 Ag RDT offered in Germany.

We addressed the sensitivity of 122 Ag RDT in direct comparison using a common evaluation panel comprised of 50 specimens. Minimum sensitivity of 75% for panel specimens with a PCR quantification cycle (Cq) ≤ 25 was used to identify Ag RDT eligible for reimbursement in the German healthcare system.

The sensitivity of different SARS-CoV-2 Ag RDT varied over a wide range. The sensitivity limit of 75% for panel members with Cq ≤ 25 was met by 96 of the 122 tests evaluated; 26 tests exhibited lower sensitivity, few of which failed completely. Some RDT exhibited high sensitivity, e.g. 97.5 % for Cq < 30.

This comparative evaluation succeeded in distinguishing less sensitive from better performing Ag RDT. Most of the evaluated Ag RDT appeared to be suitable for fast identification of acute infections associated with high viral loads. Market access of SARS-CoV-2 Ag RDT should be based on minimal requirements for sensitivity and specificity.

Many countries have attempted to mitigate and control COVID-19 through non-pharmaceutical interventions, particularly with the aim of reducing population movement and contact. However, it remains unclear how the different control strategies impacted the local phylodynamics of the causative SARS-CoV-2 virus.

We aimed to assess the duration of chains of virus transmission within individual countries and the extent to which countries exported viruses to their geographical neighbours.

We analysed complete SARS-CoV-2 genomes to infer the relative frequencies of virus importation and exportation, as well as virus transmission dynamics, in countries of northern Europe. We examined virus evolution and phylodynamics in Denmark, Finland, Iceland, Norway and Sweden during the first year of the COVID-19 pandemic.

The Nordic countries differed markedly in the invasiveness of control strategies, which we found reflected in transmission chain dynamics. For example, Sweden, which compared with the other Nordic countries relied more on recommendation-based rather than legislation-based mitigation interventions, had transmission chains that were more numerous and tended to have more cases. This trend increased over the first 8 months of 2020. Together with Denmark, Sweden was a net exporter of SARS-CoV-2. Norway and Finland implemented legislation-based interventions; their transmission chain dynamics were in stark contrast to their neighbouring country Sweden.

Sweden constituted an epidemiological and evolutionary refugium that enabled the virus to maintain active transmission and spread to other geographical locations. Our analysis reveals the utility of genomic surveillance where monitoring of active transmission chains is a key metric.

In the SARS-CoV-2 pandemic, viral genomes are available at unprecedented speed, but spatio-temporal bias in genome sequence sampling precludes phylogeographical inference without additional contextual data.

We applied genomic epidemiology to trace SARS-CoV-2 spread on an international, national and local level, to illustrate how transmission chains can be resolved to the level of a single event and single person using integrated sequence data and spatio-temporal metadata.

We investigated 289 COVID-19 cases at a university hospital in Munich, Germany, between 29 February and 27 May 2020. Using the ARTIC protocol, we obtained near full-length viral genomes from 174 SARS-CoV-2-positive respiratory samples. Phylogenetic analyses using the Auspice software were employed in combination with anamnestic reporting of travel history, interpersonal interactions and perceived high-risk exposures among patients and healthcare workers to characterise cluster outbreaks and establish likely scenarios and timelines of transmission.

We identified multiple independent introductions in the Munich Metropolitan Region during the first weeks of the first pandemic wave, mainly by travellers returning from popular skiing areas in the Alps. In these early weeks, the rate of presumable hospital-acquired infections among patients and in particular healthcare workers was high (9.6% and 54%, respectively) and we illustrated how transmission chains can be dissected at high resolution combining virus sequences and spatio-temporal networks of human interactions.

Early spread of SARS-CoV-2 in Europe was catalysed by superspreading events and regional hotspots during the winter holiday season. Genomic epidemiology can be employed to trace viral spread and inform effective containment strategies.

Many countries have implemented population-wide interventions to control COVID-19, with varying extent and success. Many jurisdictions have moved to relax measures, while others have intensified efforts to reduce transmission.

We aimed to determine the time frame between a population-level change in COVID-19 measures and its impact on the number of cases.

We examined how long it takes for there to be a substantial difference between the number of cases that occur following a change in COVID-19 physical distancing measures and those that would have occurred at baseline. We then examined how long it takes to observe this difference, given delays and noise in reported cases. We used a susceptible-exposed-infectious-removed (SEIR)-type model and publicly available data from British Columbia, Canada, collected between March and July 2020.

It takes 10 days or more before we expect a substantial difference in the number of cases following a change in COVID-19 control measures, but 20–26 days to detect the impact of the change in reported data. The time frames are longer for smaller changes in control measures and are impacted by testing and reporting processes, with delays reaching ≥ 30 days.

The time until a change in control measures has an observed impact is longer than the mean incubation period of COVID-19 and the commonly used 14-day time period. Policymakers and practitioners should consider this when assessing the impact of policy changes. Rapid, consistent and real-time COVID-19 surveillance is important to minimise these time frames.