ABSTRACT
Background The role of schools as a source of infection and driver in the coronavirus-pandemic has been controversial and is still not completely clarified. To prevent harm and disadvantages for children and adolescents, but also adults, detailed data on school outbreaks is needed, especially when talking about open schools employing evidence-based safety concepts. Here, we investigated the first significant COVID-19 school outbreak in Hamburg, Germany, after the re-opening of schools in 2020. Methods Using clinical, laboratory, and contact data and spatial measures for epidemiological and environmental studies combined with whole-genome sequencing (WGS) analysis, we examined the causes and the course of the secondary school outbreak. Findings The potential index case was identified by epidemiological tracking and the lessons in classrooms with presumably high virus spreading rates and further infection chains in the setting. Sequence analysis of samples detected one sample of a different virus lineage and 25 virus genomes with almost identical sequences, of which 21 showed 100% similarity. Most infections occurred in connection with two lesson units of the primary case. Likely, 31 students (12-14 years old), two staff members, and three family members were infected in the school or the typical household. Interpretations Sequence analysis revealed an outbreak cluster with a single source that was epidemiologically identified as a member of the educational staff. In lesson units, two superspreading events of varying degrees with airborne transmission took place. These were influenced by several parameters including the exposure times, the use of respiratory masks while speaking and spatial or structural conditions at that time.
Subject(s)
COVID-19ABSTRACT
By May 2021, over 160 million SARS-CoV-2 diagnoses have been reported worldwide. Yet, the true number of infections is unknown and believed to exceed the reported numbers by several fold. National testing policies, in particular, can strongly affect the proportion of undetected cases. Here, we propose a novel method (GInPipe) that reconstructs SARS-CoV-2 incidence profiles within minutes, solely from publicly available, time-stamped viral genomes. We validated GInPipe against in silico generated outbreak data and elaborate phylodynamic analyses. We apply the method to reconstruct incidence histories from sequence data for Denmark, Scotland, Switzerland, and Victoria (Australia). GInPipe reconstructs the different pandemic waves robustly and remarkably accurate. We demonstrate how the method can be used to investigate the effects of changing testing policies on the probability to diagnose and report infected individuals. Specifically, we find that under-reporting was highest in mid 2020 in parts of Europe, coinciding with changes towards more liberal testing policies at times of low testing capacities. Due to the increased use of real-time sequencing, it is envisaged that GInPipe can complement established surveillance tools to monitor the SARS-CoV-2 pandemic. We anticipate that the method is particularly useful in settings where diagnostic and reporting infrastructures are insufficient. In ‘post-pandemic’ times, when diagnostic efforts are decreased, GInPipe may facilitate the detection of hidden infection dynamics.
ABSTRACT
In May 2021, over 160 million SARS-CoV-2 infections have been reported worldwide. Yet, the true amount of infections is unknown and believed to exceed the reported numbers by several fold, depending on national testing policies that can strongly affect the proportion of undetected cases. To overcome this testing bias and better assess SARS-CoV-2 transmission dynamics, we propose a genome-based computational pipeline, GInPipe, to reconstruct the SARS-CoV-2 incidence dynamics through time. After validating GInPipe against in silico generated outbreak data, as well as more complex phylodynamic analyses, we use the pipeline to reconstruct incidence histories in Denmark, Scotland, Switzerland, and Victoria (Australia) solely from viral sequence data. The proposed method robustly reconstructs the different pandemic waves in the investigated countries and regions, does not require phylodynamic reconstruction, and can be directly applied to publicly deposited SARS-CoV-2 sequencing data sets. We observe differences in the relative magnitude of reconstructed versus reported incidences during times with sparse availability of diagnostic tests. Using the reconstructed incidence dynamics, we assess how testing policies may have affected the probability to diagnose and report infected individuals. We find that under-reporting was highest in mid 2020 in all analysed countries, coinciding with liberal testing policies at times of low test capacities. Due to the increased use of real-time sequencing, it is envisaged that GInPipe can complement established surveillance tools to monitor the SARS-CoV-2 pandemic and evaluate testing policies. The method executes within minutes on very large data sets and is freely available as a fully automated pipeline from https://github.com/KleistLab/GInPipe.
Subject(s)
Severe Acute Respiratory SyndromeABSTRACT
In early 2020 COVID-19 turned into a global pandemic. Non-pharmaceutical interventions (NPIs), including the isolation of infected individuals, tracing and quarantine of exposed individuals are decisive tools to prevent onwards transmission and curb fatalities. Strategies that combine NPIs with SARS-CoV-2 testing may help to shorten quarantine durations while being non-inferior with respect to infection prevention. Thus, combined strategies can help reducing the socio-economic burden of SARS-CoV2 and generate greater public acceptance. We developed a software that enables policy makers to calculate the reduction in transmissibility through quarantine or isolation in combination with arbitrary testing strategies. The user chooses between three different modi [(i) isolation of infected individuals, (ii) management of potentially infected contacts and (iii) quarantine of incoming travelers], while having total flexibility in customizing testing strategies, as well as setting model parameters. The software enables decision makers to tailor calculations specifically to their questions and perform an assessment on the fly, based on current evidence on infection dynamics. Underneath, we analytically solve a stochastic transit compartment model of the infection time course, which captures temporal changes in test sensitivities, incubation- and infectious periods, as well as times to symptom onset using its default parameters. Using default parameters, we estimated that testing travelers at the point of entry reduces the risk about 4.69 (4.19,4.83) fold for PCR vs. 3.59 (3.22, 3.69) fold for based rapid diagnostic tests (RDT, 87% relative sensitivity) when combined with symptom screening. In comparison to 14 days of pure quarantine, 8 (PCR) vs. 10 (RDT) days of pre-test quarantine would be noninferior for incoming travelers as well as for contact person management. De-isolation of infected individuals 11 days after symptom onset reduces the risk by >99fold (7.68,>1012). This tool is freely available from: https://github.com/CovidStrategyCalculator/CovidStrategyCalculator
Subject(s)
COVID-19ABSTRACT
SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causes the infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformatics tools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection, understanding, and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to get insight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for the routine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemic and evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets and development of therapeutic strategies. For each tool, we briefly describe its use case and how it advances research specifically for SARS-CoV-2. All tools are freely available online, either through web applications or public code repositories.