Optimal health and economic impact of non-pharmaceutical intervention measures prior and post vaccination in England: a mathematical modelling study
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Abstract
Background. Even with good progress on vaccination, SARS-CoV-2 infections in the UK may continue to impose a high burden of disease and therefore pose substantial challenges for health policy decision makers. Stringent government-mandated physical distancing measures (lockdown) have been demonstrated to be epidemiologically effective, but can have both positive and negative economic consequences. The duration and frequency of any intervention policy could, in theory, be optimized to maximize economic benefits while achieving substantial reductions in disease. Methods. Here, we use a pre-existing SARS-CoV-2 transmission model to assess the health and economic implications of different strengths of control through time in order to identify optimal approaches to non-pharmaceutical intervention stringency in the UK, considering the role of vaccination in reducing the need for future physical distancing measures. The model is calibrated to the COVID-19 epidemic in England and we carry out retrospective analysis of the optimal timing of precautionary breaks in 2020 and the optimal relaxation policy from the January 2021 lockdown, considering the willingness to pay (WTP) for health improvement. Results. We find that the precise timing and intensity of interventions is highly dependent upon the objective of control. As intervention measures are relaxed, we predict a resurgence in cases, but the optimal intervention policy can be established dependent upon the WTP per quality adjusted life year loss avoided. Our results show that establishing an optimal level of control can result in a reduction in net monetary loss of billions of pounds, dependent upon the precise WTP value. Conclusion. It is vital, as the UK emerges from lockdown, but continues to face an on-going pandemic, to accurately establish the overall health and economic costs when making policy decisions. We demonstrate how some of these can be quantified, employing mechanistic infectious disease transmission models to establish optimal levels of control for the ongoing COVID-19 pandemic.
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SciScore for 10.1101/2021.04.22.21255949: (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: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There are important caveats to the findings in this work that should be discussed, and can inform those who wish to develop this approach further. First, we have examined the relationship between levels of control and economic and health impact. The level of control is driven both by voluntary and mandated distancing, and the former is …
SciScore for 10.1101/2021.04.22.21255949: (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: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There are important caveats to the findings in this work that should be discussed, and can inform those who wish to develop this approach further. First, we have examined the relationship between levels of control and economic and health impact. The level of control is driven both by voluntary and mandated distancing, and the former is likely to be driven at least in part by economic and health risk. These feed backs may result in underestimating the optimal level of control, since strong controls can reduce disease incidence and hence minimise economic damage from voluntary distancing. We also note that our economic approach, utilising current GDP loss as a proxy for overall economic loss, is unlikely to capture the dynamics of economic impact and wider societal welfare and long-term economic impacts of control, particularly given government borrowing to mitigate costs in the short term. There will be a range of lagged impacts, and longer-term impacts on GDP of any set of controls. We also note that GDP loss is not the only important economic indicator. Fiscal impact or increases in unemployment are other important factors, and wider impacts such as educational losses could be considered. It is therefore possible that the findings represented here may overestimate the short-term economic impact of lockdown, whilst not capturing the social and long-term economic impact. These aspects should also be considered when determining the optimal strength of control implemented within...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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