Real-time nowcasting and forecasting of COVID-19 dynamics in England: the first wave

Birrell, Paul; Blake, Joshua; Leeuwen, Edwin van; Gent, Nick; Angelis, Daniela De

doi:10.1098/rstb.2020.0279

Cited by 76 publications

(97 citation statements)

References 19 publications

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“…Otherwise, we fix parameters relating to disease progression to values informed by the literature, in line with established literature (see Ref. [ 8 ]) and another modeling study for the UK [ 9 ]. Such fixed parameters include the rates for leaving stage E, γ E , stage A, γ A , and stage Ia, γ a .…”

Section: Model Structure and Datamentioning

confidence: 99%

Bayesian inference across multiple models suggests a strong increase in lethality of COVID-19 in late 2020 in the UK

Pietzonka

Brorson²,

Bankes³

et al. 2021

PLoS ONE

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We apply Bayesian inference methods to a suite of distinct compartmental models of generalised SEIR type, in which diagnosis and quarantine are included via extra compartments. We investigate the evidence for a change in lethality of COVID-19 in late autumn 2020 in the UK, using age-structured, weekly national aggregate data for cases and mortalities. Models that allow a (step-like or graded) change in infection fatality rate (IFR) have consistently higher model evidence than those without. Moreover, they all infer a close to two-fold increase in IFR. This value lies well above most previously available estimates. However, the same models consistently infer that, most probably, the increase in IFR preceded the time window during which variant B.1.1.7 (alpha) became the dominant strain in the UK. Therefore, according to our models, the caseload and mortality data do not offer unequivocal evidence for higher lethality of a new variant. We compare these results for the UK with similar models for Germany and France, which also show increases in inferred IFR during the same period, despite the even later arrival of new variants in those countries. We argue that while the new variant(s) may be one contributing cause of a large increase in IFR in the UK in autumn 2020, other factors, such as seasonality, or pressure on health services, are likely to also have contributed.

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Section: Model Structure and Datamentioning

confidence: 99%

Bayesian inference across multiple models suggests a strong increase in lethality of COVID-19 in late 2020 in the UK

Pietzonka

Brorson²,

Bankes³

et al. 2021

PLoS ONE

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“…Our model uses a similar Monte Carlo based structure as presented by Bays et al for use in studying border screening policies[4], [5]. The model starts by simulating 500,000 individuals, all being assumed to have become infected with COVID-19 at some point prior, each of which is then randomly assigned an infectious period (given in days), t inf , sampled from a gamma distribution (shape = 2, scale = 2.1) which has been fitted to real-time data using methods described by Birrell et al[6]. At time t = 0, individuals are assumed to have been identified as COVID-19 positive, either through testing or the displaying of symptoms, at which point they enter self-isolation.…”

Section: Methodsmentioning

confidence: 99%

Mitigating isolation: further comparing the effect of LFD testing for early release from self-isolation for COVID-19 cases

Bays¹,

Whiteley²,

Williams³

et al. 2022

Preprint

Self Cite

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In a recent paper, we described how lateral flow device (LFD) testing might be used to reduce the amount of excess time individuals spend in isolation following confirmation of a COVID-19 infection. Through the work presented here, we look to expand upon this and explore in more detail the benefit that such an approach might provide. We use our previously described model to study scenarios through the metrics "proportion released still infectious", "excess time spent in isolation" (time isolated while no longer infectious), and "time spent infectious after early release". We also look to consider the effect on these metrics by comparing values obtained when a single negative LFD test is required for early release, versus requiring two and three sequential negative LFD tests. Results show that jointly employing self-isolation and LFD testing may deliver sizeable reductions to the proportion of individuals being release while still infection, the average amount of excess time spent in isolation by those no longer a public health threat, and the average amount of time spent infectious by those released early. These effects considered in conjunction could provide a considerable decrease in the public health risk posed by still infectious individuals being released back into the population by actively monitoring their infection status throughout their isolation period. Such an approach could also help lighten the impact incurred on the individual by reducing the amount of time spent in isolation while posing no further public health risk, in addition to alleviating pressures on the economy and in healthcare settings caused by mass isolation in times of high prevalence.

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“…The issue covers early models that were developed for the UK (figure 4), often with limited data and initially relying on SARS-1-like parameters [15] and theoretical insights [31]. It includes models that are used in the ongoing overview of the epidemic with weekly consensus estimates of the Reproduction number [19,20], short term and medium-term projections [36] and real-time data stream monitoring [37] all playing a part. There are time-sensitive, changing policy questions, such as the impact of mass gatherings [38], reopening schools in May 2020 [39][40][41], the introduction of support bubbles [42] or the impact of contact tracing and lockdown [43].…”

Section: Putting This Special Issue Togethermentioning

confidence: 99%

Modelling that shaped the early COVID-19 pandemic response in the UK

Brooks-Pollock

Danon

Jombart

et al. 2021

Phil. Trans. R. Soc. B

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Infectious disease modelling has played an integral part of the scientific evidence used to guide the response to the COVID-19 pandemic. In the UK, modelling evidence used for policy is reported to the Scientific Advisory Group for Emergencies (SAGE) modelling subgroup, SPI-M-O (Scientific Pandemic Influenza Group on Modelling-Operational). This Special Issue contains 20 articles detailing evidence that underpinned advice to the UK government during the SARS-CoV-2 pandemic in the UK between January 2020 and July 2020. Here, we introduce the UK scientific advisory system and how it operates in practice, and discuss how infectious disease modelling can be useful in policy making. We examine the drawbacks of current publishing practices and academic credit and highlight the importance of transparency and reproducibility during an epidemic emergency. This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’.

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Real-time nowcasting and forecasting of COVID-19 dynamics in England: the first wave

Cited by 76 publications

References 19 publications

Bayesian inference across multiple models suggests a strong increase in lethality of COVID-19 in late 2020 in the UK

Bayesian inference across multiple models suggests a strong increase in lethality of COVID-19 in late 2020 in the UK

Mitigating isolation: further comparing the effect of LFD testing for early release from self-isolation for COVID-19 cases

Modelling that shaped the early COVID-19 pandemic response in the UK

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