Modelling the effect of an improved trace and isolate system in the wake of a highly transmissible Covid-19 variant with potential vaccine escape
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Abstract
Objective
How helpful would a properly functioning find, test, trace, isolate and support (FTTIS) system be now in the UK with new Covid-19 infections at a low level and half the adult population immunised but with a highly transmissible variant becoming predominant?
Design
a dynamic causal model of Covid-19 supplied with the latest available empirical data is used to assess the impact of a new highly transmissible variant.
Setting
the United Kingdom.
Participants
a population based study.
Interventions
scenarios are used to explore a Covid-19 transmission rate 50% more and twice the current rate with or without a more effective FTTIS system.
Main outcome measures
incidence, death rate and reproductive ratio
Results
a small short third wave of infections occurs which does not occur if FTTIS effectiveness is improved from 25% to 30%.
Conclusion
a modest improvement in FTTIS would prevent a third wave caused by a highly transmissible virus.
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SciScore for 10.1101/2021.06.07.21258451: (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:The limitations of the approach are significant. While the model is able to factor in past behaviour it cannot predict the biological characteristics of a new Covid-19 variant. But based on the performance of the virus in recent weeks it seems that the level of vaccine induced immunity plus the immunity produced by past infection will …
SciScore for 10.1101/2021.06.07.21258451: (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:The limitations of the approach are significant. While the model is able to factor in past behaviour it cannot predict the biological characteristics of a new Covid-19 variant. But based on the performance of the virus in recent weeks it seems that the level of vaccine induced immunity plus the immunity produced by past infection will place limits on the next wave of infections. The role of FTTIS is interesting. A marginal improvement in efficacy at this stage in the epidemic can have a valuable impact on the spread of the virus. Vaccine efficacy as modelled here but also in real life changes all the time – with seasonality, waning immunity, level of vaccine coverage, past infections and new variant escape. The model predicts an efficacy of 86% at the moment. But we do not know the properties of the Indian variant as it becomes predominant in the UK. IHME offers an estimate for AstraZeneca of 35% efficacy at preventing disease and 32% efficacy at preventing infection by B.1.617 and for Pfizer/BioNTech of 86% efficacy at preventing disease and 82% efficacy at preventing infection by B.1.617 (10). This can be compared to the latest Public Health England (PHE) estimates for Pfizer/BioNTech of 88% and for AstraZeneca of 60% at preventing symptomatic infection by B.1.617.2 after two doses (11). Time will tell if vaccine efficacy remains as high as current estimates suggest.
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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