Combining models to generate a consensus effective reproduction numberRfor the COVID-19 epidemic status in England

Abstract

The effective reproduction numberRwas widely accepted as a key indicator during the early stages of the COVID-19 pandemic. In the UK, theRvalue published on the UK Government Dashboard has been generated as a combined value from an ensemble of fourteen epidemiological models via a collaborative initiative between academia and government. In this paper we outline this collaborative modelling approach and illustrate how, by using an established combination method, a combinedRestimate can be generated from an ensemble of epidemiological models. We show that thisRis robust to different model weighting methods and ensemble size and that using heterogeneous data sources for validation increases its robustness and reduces the biases and limitations associated with a single source of data. We discuss howRcan be generated from different data sources and is therefore a good summary indicator of the current dynamics in an epidemic.

Inferring community transmission of SARS-CoV-2 in the United Kingdom using the ONS COVID-19 Infection Survey

“…43 Although deployed in multiple different fields, [44][45][46] this is particularly important in the context of modelling the SARS-CoV-2 epidemic in the UK, due to the diversity of surveillance data streams available. As discussed by Park et al 5 , the ensemble modelling approach has many strengths including increased prediction ability and greater robustness. We see our work completing this approach, by presenting a potential solution to the challenge faced by public health officials in the UK in early 2022 given the large scaling-down of the surveillance systems at the time.…”

“…Estimates of ܴሺ‫ݐ‬ሻ and ‫ݎ‬ሺ‫ݐ‬ሻ for SARS-CoV-2 in each of the four nations were produced for and published by the government from early in the pandemic for either weekly or biweekly time periods. 5,13 The estimates were derived from an ensemble of (up to 14) independently run models as part of a cross-government and academic modelling hub that comprised the UK Health Security Agency (UKHSA) Epidemiological Ensemble team and Scientific Pandemic Influenza Group on Modelling, Operational sub-group (SPI-M-O). 5,37 These models encompassed a range of different assumptions and data streams as set out in 5 .…”

“…1–4 Estimation of these quantities by public health officials in the United Kingdom (UK) has relied on an ensemble of models which encompass a range of data sources and assumptions. 5 These parameters are traditionally estimated using case data as a proxy for the infection incidence curve, 6,7 but methods have also been developed to estimate these parameters from other sources, such as hospitalisations 8 and genomic data. 9 The four nations of the UK (England, Scotland, Wales and Northern Ireland, herein ordered by population size) were recognised globally as having comprehensive SARS-CoV-2 testing surveillance systems, 10–12 comprising of widescale community testing, 13 nationwide surveys of infection, 14,15 genomic data, 16,17 and wastewater surveillance, 18 but these were largely scaled down from their peak capacity as part of the transition from an “emergency” to “endemic” state in the first half of 2022.…”

Combining models to generate consensus medium-term projections of hospital admissions, occupancy and deaths relating to COVID-19 in England

“…As we discussed in our previous paper [22], the value in getting a combined forecast from across models and datasets is not only just in the weighted averaging of those estimates but also in the formation of a community that is constantly discussing the outcomes, the modelling assumptions and the input data, identifying the drivers behind the differences across models’ outcomes when formulating the aggregated possible future projections.…”

Machine learning assisted calibration of stochastic agent-based models for pandemic outbreak analysis

“…The COVID-19 pandemic resulted in a rapid response by scientists worldwide to develop tools to aid policymakers in controlling infections. In the UK, a number of models were developed [1] and used timely and responsively to track epidemic trends, make short and medium-term projections of the epidemic trajectories and give informed advice.…”