Short-term forecasts to inform the response to the Covid-19 epidemic in the UK

Funk, Sebastian; Abbott, Sam; Atkins, Benjamin D.; Baguelin, Marc; Baillie, J Kenneth; Birrell, Paul; Blake, Joshua; Bosse, Nikos I.; Burton, Joshua; Carruthers, Jonathan; Davies, Nicholas; Angelis, Daniela De; Dyson, Louise; Edmunds, W. John; Eggo, Rosalind M; Ferguson, Neil; Gaythorpe, Katy A. M.; Gorsich, Erin E.; Guyver‐Fletcher, Glen; Hellewell, Joel; Hill, Edward M.; Holmes, Alexander; House, Thomas; Jewell, Chris; Jit, Mark; Jombart, Thibaut; Joshi, Indra; Keeling, Matt J.; Kendall, E. Dwight; Knock, Edward; Kucharski, Adam J.; Lythgoe, Katrina A.; Meakin, Sophie; Munday, James D.; Openshaw, Peter; Overton, Christopher E.; Pagani, Filippo; Pearson, John; Perez‐Guzman, Pablo N; Pellis, Lorenzo; Scarabel, Francesca; Semple, Malcolm G; Tang, Ming; Tildesley, Michael J.; Leeuwen, Edwin van; Whittles, Lilith K; Investigators, Isaric C

doi:10.1101/2020.11.11.20220962

Cited by 37 publications

(65 citation statements)

References 28 publications

(32 reference statements)

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“…17 Preliminary research suggested that COVID-19 ensemble forecasts were also more accurate and precise than individual models in the early phases of the pandemic. 18,19 As has been seen in research across disciplines, ensemble approaches are able to draw from and incorporate the information from multiple forecasts, each with their own strengths and limitations, to create highly accurate predictions with well-calibrated uncertainty. 20–25 Additionally, synthesizing multiple models removes the risk of over-reliance on any single approach for accuracy or stability.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

Cramer¹,

Ray²,

Lopez³

et al. 2021

Preprint

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Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. Significance Statement This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.

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Section: Introductionmentioning

confidence: 99%

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

Cramer¹,

Ray²,

Lopez³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Importantly, these are able to relate epidemiological disease dynamics to outcomes far downstream, such as hospitalisation and deaths. The fact that a large number and variety of models has been developed can be viewed as a strength, as demonstrated by efficacy of ensembles of multi-model forecasts to inform policy on future resource needs and population impacts [19]. One attractive feature of such model ensembles is that their forecasts may be relatively robust to changes in spatiotemporal and compartmental dynamics over the course of an epidemic.…”

Section: Sir Model -Discussion Assumptions and Caveatsmentioning

confidence: 99%

“…The spread of the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the ensuing outbreaks of coronavirus disease 2019 (COVID- 19) have placed a significant burden on public health in the United Kingdom (UK). As of 12 April 2021, the number of people who have died within 28 days of a positive SARS-CoV-2 test was 127,100 [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…The current pandemic has spurred the development of a number of models that also aim to incorporate multiple sources of data in order to estimate important epidemiological parameters, in particular R t [19]. Abbott et al [20] generate daily estimates of R t at a national and PHE region level by incorporating case counts and death notifications, building on a model to estimate R t from incidence time series [21].…”

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confidence: 99%

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Local prevalence of transmissible SARS-CoV-2 infection: an integrative causal model for debiasing fine-scale targeted testing data

Nicholson

Lehmann

Padellini

et al. 2021

Preprint

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Targeted surveillance testing schemes for SARS-CoV-2 focus on certain subsets of the population, such as individuals experiencing one or more of a prescribed list of symptoms. These schemes have routinely been used to monitor the spread of SARS-CoV-2 in countries across the world. The number of positive tests in a given region can provide local insights into important epidemiological parameters, such as prevalence and effective reproduction number. Moreover, targeted testing data has been used inform the deployment of localised non-pharmaceutical interventions. However, surveillance schemes typically suffer from ascertainment bias; the individuals who are tested are not necessarily representative of the wider population of interest. Here, we show that data from randomised testing schemes, such as the REACT study in the UK, can be used to debias fine-scale targeted testing data in order to provide accurate localised estimates of the number of infectious individuals. We develop a novel, integrative causal framework that explicitly models the process underlying the selection of individuals for targeted testing. The output from our model can readily be incorporated into longitudinal analyses to provide local estimates of the reproduction number. We apply our model to characterise the size of the infectious population in England between June 2020 and January 2021. Our local estimates of the effective reproduction number are predictive of future changes in positive case numbers. We also capture local increases in both prevalence and effective reproductive number in the South East from November 2020 to December 2020, reflecting the spread of the Kent variant. Preparations for future epidemics should ensure the rapid deployment of both types of schemes to accurately monitor the spread of emerging and ongoing infectious diseases.

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“…Different methods exist to estimate the time-varying reproduction number, and in the UK a number of mathematical and statistical methods have been used to produce estimates used to inform policy (7– 9). Empirical estimates of R t can be achieved by estimating time-varying patterns in transmission events from mapping to a directly observed time-series indicator of infection such as reported symptomatic cases.…”

Section: Introductionmentioning

confidence: 99%

Exploring surveillance data biases when estimating the reproduction number: with insights into subpopulation transmission of Covid-19 in England

Sherratt

Abbott

Meakin

et al. 2020

Preprint

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The time-varying reproduction number (Rt: the average number secondary infections caused by each infected person) may be used to assess changes in transmission potential during an epidemic. Since new infections usually are not observed directly, it can only be estimated from delayed and potentially biased data. We estimated Rt using a model that mapped unobserved infections to observed test-positive cases, hospital admissions, and deaths with confirmed Covid-19, in seven regions of England over March through August 2020. We explored the sensitivity of Rt estimates of Covid-19 in England to different data sources, and investigated the potential of using differences in the estimates to track epidemic dynamics in population sub-groups. Our estimates of transmission potential varied for each data source. The divergence between estimates from each source was not consistent within or across regions over time, although estimates based on hospital admissions and deaths were more spatio-temporally synchronous than compared to estimates from all test-positives. We compared differences in Rt with the demographic and social context of transmission, and found the differences between Rt may be linked to biased representations of sub-populations in each data source: from uneven testing rates, or increasing severity of disease with age, seen via outbreaks in care home populations and changing age distributions of cases. We highlight that policy makers should consider the source populations of Rt estimates. Further work should clarify the best way to combine and interpret Rt estimates from different data sources based on the desired use.

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Short-term forecasts to inform the response to the Covid-19 epidemic in the UK

Cited by 37 publications

References 28 publications

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

Local prevalence of transmissible SARS-CoV-2 infection: an integrative causal model for debiasing fine-scale targeted testing data

Exploring surveillance data biases when estimating the reproduction number: with insights into subpopulation transmission of Covid-19 in England

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