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Review 1: "Modelling the Impact of Reopening Schools in Early 2021 in the Presence of the New SARS-CoV-2 Variant and with Roll-out of Vaccination Against COVID-19"

Published onApr 14, 2022
Review 1: "Modelling the Impact of Reopening Schools in Early 2021 in the Presence of the New SARS-CoV-2 Variant and with Roll-out of Vaccination Against COVID-19"
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key-enterThis Pub is a Review of
Modelling the impact of reopening schools in the UK in early 2021 in the presence of the alpha variant and with roll-out of vaccination against SARS-CoV-2

BackgroundFollowing the resurgence of the COVID-19 epidemic in the UK in late 2020 and the emergence of the alpha (also known as B117) variant of the SARS-CoV-2 virus, a third national lockdown was imposed from January 4, 2021. Following the decline of COVID-19 cases over the remainder of January 2021, the question of when and how to reopen schools became an increasingly pressing one in early 2021. This study models the impact of a partial national lockdown with social distancing measures enacted in communities and workplaces under different strategies of reopening schools from March 8, 2021 and compares it to the impact of continual full national lockdown remaining until April 19, 2021.MethodsWe used our previously published agent-based model, Covasim, to model the emergence of the alpha variant over September 1, 2020 to January 31, 2021 in presence of Test, Trace and Isolate (TTI) strategies. We extended the model to incorporate the impacts of the roll-out of a two-dose vaccine against COVID-19, with 200,000 daily vaccine doses prioritised by age starting with people 75 years or older, assuming vaccination offers a 95% reduction in disease acquisition risk and a 30% reduction in transmission risk. We used the model, calibrated until January 25, 2021, to simulate the impact of a full national lockdown (FNL) with schools closed until April 19, 2021 versus four different partial national lockdown (PNL) scenarios with different elements of schooling open: 1) staggered PNL with primary schools and exam-entry years (years 11 and 13) returning on March 8, 2021 and the rest of the schools years on March 15, 2020; 2) full-return PNL with both primary and secondary schools returning on March 8, 2021; 3) primary-only PNL with primary schools and exam critical years (years 11 and 13) going back only on March 8, 2021 with the rest of the secondary schools back on April 19, 2021 and 4) part-rota PNL with both primary and secondary schools returning on March 8, 2021 with primary schools remaining open continuously but secondary schools on a two-weekly rota-system with years alternating between a fortnight of face-to-face and remote learning until April 19, 2021. Across all scenarios, we projected the number of new daily cases, cumulative deaths and effective reproduction number R until April 30, 2021.ResultsOur calibration across different scenarios is consistent with alpha variant being around 60% more transmissible than the wild type. We find that strict social distancing measures, i.e. national lockdowns, were essential in containing the spread of the virus and controlling hospitalisations and deaths during January and February 2021. We estimated that a national lockdown over January and February 2021 would reduce the number of cases by early March to levels similar to those seen in October 2020, with R also falling and remaining below 1 over this period. We estimated that infections would start to increase when schools reopened, but found that if other parts of society remain closed, this resurgence would not be sufficient to bring R above 1. Reopening primary schools and exam critical years only or having primary schools open continuously with secondary schools on rotas was estimated to lead to lower increases in cases and R than if all schools opened. Without an increase in vaccination above the levels seen in January and February, we estimate that R could have increased above 1 following the reopening of society, simulated here from April 19, 2021.FindingsOur findings suggest that stringent measures were integral in mitigating the increase in cases and bringing R below 1 over January and February 2021. We found that it was plausible that a PNL with schools partially open from March 8, 2021 and the rest of the society remaining closed until April 19, 2021 would keep R below 1, with some increase evident in infections compared to continual FNL until April 19, 2021. Reopening society in mid-April, without an increase in vaccination levels, could push R above 1 and induce a surge in infections, but the effect of vaccination may be able to control this in future depending on the transmission blocking properties of the vaccines.

RR:C19 Evidence Scale rating by reviewer:

  • Strong. The main study claims are very well-justified by the data and analytic methods used. There is little room for doubt that the study produced has very similar results and conclusions as compared with the hypothetical ideal study. The study’s main claims should be considered conclusive and actionable without reservation.



In the recent medRxiv paper titled "Modelling the impact of reopening schools in early 2021 in the presence of the new SARS-CoV-2 variant and with the roll-out of vaccination against COVID19", the authors use a previously published individual-level epidemiological model of SARS-CoV2 transmission called CovaSim over a representative and large-scale contact network to simulate and compare five different school reopening strategies in the UK starting from March 8, 2021. The simulation model has been successfully calibrated to past epidemiological data for the UK and incorporates both the effects of the vaccine's roll-out and the dynamic increase in the virus's transmissibility due to the gradual increase in the proportion of cases with the new UK strain. The authors find that UK schools could partially reopen from March 8th and have an R effective below 1 provided that a partial national lockdown is maintained until April 19, 2021, where the rest of the society remaining closed. However, reopening society too soon (before mid-April) could lead to an R effective greater than 1. The results are sensitive to the efficacy of the vaccine in reducing transmissions.

This research paper is reliable. The general approach, methods, and data used are solid, and the claims made by the authors based on the simulation results are well-supported. The evidence and arguments presented in this manuscript support advancement of COVID-19 understanding within society. The manuscript is very well-structured and well-written, and the research work is clearly and accurately presented.

However, although the results are reliable, they are not particularly surprising. Moreover, based on Figures 2 and 3, which compare the five scenarios, there seems to be a high degree of uncertainty due to the stochastic variability in the simulation models' independent realizations. The differences across the scenarios do not seem to be as clear-cut. It is also unclear how much of this variability is due to the uncertainty in the input model parameters' range. It would have been useful if the authors had included a table of the key parameters that have the most leverage on the epidemiological model outputs and the range of their values used. For example, the authors assume that the vaccine offers a 95% reduction in disease acquisition and 10% reduction of transmission-blocking. It is unclear where the latter value comes from, and my understanding is that the efficacy of the vaccine in reducing transmissions is still uncertain, especially to the new variants. However, the authors are very cautious in interpreting their results, and their claim based on the model results is reliable and most likely generalizable, and robust to the uncertainties. The authors adequately present and discuss the limitations of their work.

In my opinion, decision-makers should consider the claims in this study actionable with limitations based on the methods and data. The research work is steeped in reality with the potential to impact the implementation of policy. Therefore, I recommend that the authors of the manuscript be given the opportunity to publish with the RR:C19 Journal.

Minor comments and suggestions to the authors:

• In the section on Test, trace, and isolation strategies, it wasn't very evident how the authors estimated the controlling parameters and their uncertainty range.

• The authors used a logistic model to transition from base-line transmissibility to higher transmissibility due to the variant. Since the model is an individual-level model, it is unclear why the authors did not explicitly model the transmission of variants. I can better understand why a logistic model approach would be used to descriptively model this transition in compartmental models based on ordinary differential equations.

• The manuscript could have benefitted from a longer and more detailed description of how the simulation model was calibrated.

• I did not find Figure 1 as useful. I would have preferred a single figure showing the best fit of the calibrated model to past epidemiological data followed by figures showing forecasts for each of the five policies.

• It would have been of interest to know what the model projects in terms of school reopening in the Autumn of 2021. However, I realize that conclusions would be very speculative due to both the high uncertainty in the stochastic realizations of the model and the model parameter values.

• The simulation model uses a very ingenious dynamic rescaling algorithm that rescales their model population size over time.


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