Modelling the impact of lockdown-easing measures on cumulative COVID-19 cases and deaths in England
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Abstract
To assess the potential impacts of successive lockdown-easing measures in England, at a point in the COVID-19 pandemic when community transmission levels were relatively high.
Design
We developed a Bayesian model to infer incident cases and reproduction number ( R ) in England, from incident death data. We then used this to forecast excess cases and deaths in multiple plausible scenarios in which R increases at one or more time points.
Setting
England.
Participants
Publicly available national incident death data for COVID-19 were examined.
Primary outcome
Excess cumulative cases and deaths forecast at 90 days, in simulated scenarios of plausible increases in R after successive easing of lockdown in England, compared with a baseline scenario where R remained constant.
Results
Our model inferred an R of 0.75 on 13 May when England first started easing lockdown. In the most conservative scenario modelled where R increased to 0.80 as lockdown was eased further on 1 June and then remained constant, the model predicted an excess 257 (95% CI 108 to 492) deaths and 26 447 (95% CI 11 105 to 50 549) cumulative cases over 90 days. In the scenario with maximal increases in R (but staying ≤1), the model predicts 3174 (95% CI 1334 to 6060) excess cumulative deaths and 421 310 (95% CI 177 012 to 804 811) cases. Observed data from the forecasting period aligned most closely to the scenario in which R increased to 0.85 on 1 June, and 0.9 on 4 July.
Conclusions
When levels of transmission are high, even small changes in R with easing of lockdown can have significant impacts on expected cases and deaths, even if R remains ≤1. This will have a major impact on population health, tracing systems and healthcare services in England. Following an elimination strategy rather than one of maintenance of R ≤1 would substantially mitigate the impact of the COVID-19 epidemic within England.
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SciScore for 10.1101/2020.06.21.20136853: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:We acknowledge some important limitations of our model. The first is that it is based on a back calculation of cases based on incident deaths, which are likely to underestimated due to reporting delays and underreporting. Second, our model is reliant on inferring cases, and reproduction numbers, which depend on the assumed distributions of the serial interval, and the time of onset to death distributions. While we have based our assumptions on …
SciScore for 10.1101/2020.06.21.20136853: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:We acknowledge some important limitations of our model. The first is that it is based on a back calculation of cases based on incident deaths, which are likely to underestimated due to reporting delays and underreporting. Second, our model is reliant on inferring cases, and reproduction numbers, which depend on the assumed distributions of the serial interval, and the time of onset to death distributions. While we have based our assumptions on the literature, misspecification of these would influence our estimates. While we have evaluated this, greater deviations from true estimates would make our forecasting less reliable. Third, similar to Flaxman et al, our model uses the IFR as a multiplier for the distribution of time from infection to death, in the absence of reliable population level case fatality rates (CFR). While this would not affect the estimation of deaths, if the CFR were higher (due to large proportions of cases being asymptomatic), then the predicted case numbers would be overestimated by our model. We note, however that the estimate of IFR we used (1.1%) is consistent with the CFR estimated in previously from Beijing.15 We have also, for simplicity, assumed that IFR remains constant throughout the pandemic and the forecasting period, and this may not reflect complex heterogeneity in IFR over time. Finally, we do not consider the impact of mitigatory measures in our current modelling. However mitigatory measures are likely to be implemented with significant de...
Results from TrialIdentifier: No clinical trial numbers were referenced.
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Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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