Reconstruction of SARS-CoV-2 outbreaks in a primary school using epidemiological and genomic data

Mathematical modelling studies have shown that repetitive screening can be used to mitigate SARS-CoV-2 transmission in primary schools while keeping schools open. However, not much is known about how transmission progresses within schools and whether there is a risk of importation to households. During the academic year 2020-2021, a prospective surveillance study using repetitive screening was conducted in a primary school and associated households in Liège (Belgium). SARS-CoV-2 screening was performed via throat washing either once or twice a week. We used genomic and epidemiological data to reconstruct the observed school outbreaks using two different models. The outbreaker2 model combines information on the generation time and contact patterns with a model of sequence evolution. For comparison we also used SCOTTI, a phylogenetic model based on the structured coalescent. In addition, we performed a simulation study to investigate how the accuracy of estimated positivity rates in a school depends on the proportion of a school that is sampled in a repetitive screening strategy. We found no difference in SARS-CoV-2 positivity between children and adults and children were not more often asymptomatic compared to adults. Both models for outbreak reconstruction revealed that transmission occurred mainly within the school environment. Uncertainty in outbreak reconstruction was lowest when including genomic as well as epidemiological data. We found that observed weekly positivity rates are a good approximation to the true weekly positivity rate, especially in children, even when only 25% of the school population is sampled. These results indicate that, in addition to reducing infections as shown in modelling studies, repetitive screening in school settings can lead to a better understanding of the extent of transmission in schools during a pandemic and importation risk at the community level.

Reconstruction of a large-scale outbreak of SARS-CoV-2 infection in Iceland informs vaccination strategies

OBJECTIVES

The spread of SARS-CoV-2 is dependent on several factors, both biological and behavioural. The effectiveness of nonpharmaceutical interventions can be attributed largely to changes in human behaviour, but quantifying this effect remains challenging. Reconstructing the transmission tree of the third wave of SARS-CoV-2 infections in Iceland using contact tracing and viral sequence data from 2522 cases enables us to directly compare the infectiousness of distinct groups of persons.

METHODS

The transmission tree enables us to model the effect that a given population prevalence of vaccination would have had on the third wave had one of three different vaccination strategies been implemented before that time. This allows us to compare the effectiveness of the strategies in terms of minimizing the number of cases, deaths, critical cases, and severe cases.

RESULTS

We found that people diagnosed outside of quarantine (Rˆ=1.31) were 89% more infectious than those diagnosed while in quarantine (Rˆ=0.70) and that infectiousness decreased as a function of time spent in quarantine before diagnosis, with people diagnosed outside of quarantine being 144% more infectious than those diagnosed after ≥3 days in quarantine (Rˆ=0.54). People of working age, 16 to 66 years (Rˆ=1.08), were 46% more infectious than those outside of that age range (Rˆ=0.74).

DISCUSSION

We found that vaccinating the population in order of ascending age or uniformly at random would have prevented more infections per vaccination than vaccinating in order of descending age, without significantly affecting the expected number of deaths, critical cases, or severe cases.

MeltingPlot, a user-friendly online tool for epidemiological investigation using High Resolution Melting data

Background

The rapid identification of pathogen clones is pivotal for effective epidemiological control strategies in hospital settings. High Resolution Melting (HRM) is a molecular biology technique suitable for fast and inexpensive pathogen typing protocols. Unfortunately, the mathematical/informatics skills required to analyse HRM data for pathogen typing likely limit the application of this promising technique in hospital settings.

Results

MeltingPlot is the first tool specifically designed for epidemiological investigations using HRM data, easing the application of HRM typing to large real-time surveillance and rapid outbreak reconstructions. MeltingPlot implements a graph-based algorithm designed to discriminate pathogen clones on the basis of HRM data, producing portable typing results. The tool also merges typing information with isolates and patients metadata to create graphical and tabular outputs useful in epidemiological investigations and it runs in a few seconds even with hundreds of isolates. Availability: https://skynet.unimi.it/index.php/tools/meltingplot/.

Conclusions

The analysis and result interpretation of HRM typing protocols can be not trivial and this likely limited its application in hospital settings. MeltingPlot is a web tool designed to help the user to reconstruct epidemiological events by combining HRM-based clustering methods and the isolate/patient metadata. The tool can be used for the implementation of HRM based real time large scale surveillance programs in hospital settings.