Fitting to the UK COVID-19 outbreak, short-term forecasts and estimating the reproductive number

ISARIC4C Investigators; Matt J. Keeling; Louise Dyson; Glen Guyver-Fletcher; Alexander Holmes; Malcolm G Semple; Michael J. Tildesley; Edward M. Hill

doi:10.1177/09622802211070257

Fitting to the UK COVID-19 outbreak, short-term forecasts and estimating the reproductive number

ISARIC4C Investigators, Matt J. Keeling^*, Louise Dyson, Glen Guyver-Fletcher, Alexander Holmes, Malcolm G Semple, Michael J. Tildesley, Edward M. Hill

^*Corresponding author for this work

Chemical Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

The COVID-19 pandemic has brought to the fore the need for policy makers to receive timely and ongoing scientific guidance in response to this recently emerged human infectious disease. Fitting mathematical models of infectious disease transmission to the available epidemiological data provide a key statistical tool for understanding the many quantities of interest that are not explicit in the underlying epidemiological data streams. Of these, the effective reproduction number, R, has taken on special significance in terms of the general understanding of whether the epidemic is under control (R<1). Unfortunately, none of the epidemiological data streams are designed for modelling, hence assimilating information from multiple (often changing) sources of data is a major challenge that is particularly stark in novel disease outbreaks. Here, focusing on the dynamics of the first wave (March–June 2020), we present in some detail the inference scheme employed for calibrating the Warwick COVID-19 model to the available public health data streams, which span hospitalisations, critical care occupancy, mortality and serological testing. We then perform computational simulations, making use of the acquired parameter posterior distributions, to assess how the accuracy of short-term predictions varied over the time course of the outbreak. To conclude, we compare how refinements to data streams and model structure impact estimates of epidemiological measures, including the estimated growth rate and daily incidence.

Original language	English
Pages (from-to)	1716–1737
Number of pages	22
Journal	Statistical Methods in Medical Research
Volume	31
Issue number	9
Early online date	17 Jan 2022
DOIs	https://doi.org/10.1177/09622802211070257
Publication status	Published - Sept 2022

Bibliographical note

Funding:
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by grants from the National Institute for Health Research (award CO-CIN-01), the Medical Research Council (grant MC_PC_19059) and by the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Emerging and Zoonotic Infections at the University of Liverpool in partnership with PHE, in collaboration with Liverpool School of Tropical Medicine and the University of Oxford (NIHR award 200907), Wellcome Trust and Department for International Development (215091/Z/18/Z), and the Bill and Melinda Gates Foundation (OPP1209135). The views expressed are those of the authors and not necessarily those of the DHSC, DID, NIHR, MRC, Wellcome Trust or PHE. Study registration ISRCTN66726260. MJK, LD, AH and MJT were supported by the Engineering and Physical Sciences Research Council through the MathSys CDT (grant number EP/S022244/1); MJK, LD, MJK and EMH were supported by the Medical Research Council through the COVID-19 Rapid Response Rolling Call (grant number MR/V009761/1); GG-F was supported by the Biotechnology and Biological Sciences Research Council through the MIBTP (BB/M01116X/1); MJK, LD and MJT were supported by UKRI through the JUNIPER modelling consortium (grant number MR/V038613/1). MJK is affiliated with the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Gastrointestinal Infections at the University of Liverpool in partnership with the UK Health Security Agency (UKHSA), in collaboration with the University of Warwick. MJK is also affiliated to the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Genomics and Enabling Data at the University of Warwick in partnership with UKHSA. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, the Department of Health and Social Care or the UK Health Security Agency. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Keywords

COVID-19
severe acute respiratory syndrome coronavirus 2
mathematical modelling
Markov chain Monte Carlo
Bayesian inference
epidemiology
growth rate
reproduction number
short-term forecasts

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Cite this

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title = "Fitting to the UK COVID-19 outbreak, short-term forecasts and estimating the reproductive number",

abstract = "The COVID-19 pandemic has brought to the fore the need for policy makers to receive timely and ongoing scientific guidance in response to this recently emerged human infectious disease. Fitting mathematical models of infectious disease transmission to the available epidemiological data provide a key statistical tool for understanding the many quantities of interest that are not explicit in the underlying epidemiological data streams. Of these, the effective reproduction number, R, has taken on special significance in terms of the general understanding of whether the epidemic is under control (R<1). Unfortunately, none of the epidemiological data streams are designed for modelling, hence assimilating information from multiple (often changing) sources of data is a major challenge that is particularly stark in novel disease outbreaks. Here, focusing on the dynamics of the first wave (March–June 2020), we present in some detail the inference scheme employed for calibrating the Warwick COVID-19 model to the available public health data streams, which span hospitalisations, critical care occupancy, mortality and serological testing. We then perform computational simulations, making use of the acquired parameter posterior distributions, to assess how the accuracy of short-term predictions varied over the time course of the outbreak. To conclude, we compare how refinements to data streams and model structure impact estimates of epidemiological measures, including the estimated growth rate and daily incidence.",

keywords = "COVID-19, severe acute respiratory syndrome coronavirus 2, mathematical modelling, Markov chain Monte Carlo, Bayesian inference, epidemiology, growth rate, reproduction number, short-term forecasts",

author = "{ISARIC4C Investigators} and Keeling, {Matt J.} and Louise Dyson and Glen Guyver-Fletcher and Alexander Holmes and Semple, {Malcolm G} and Tildesley, {Michael J.} and Hill, {Edward M.} and Christopher Green",

note = "Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by grants from the National Institute for Health Research (award CO-CIN-01), the Medical Research Council (grant MC_PC_19059) and by the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Emerging and Zoonotic Infections at the University of Liverpool in partnership with PHE, in collaboration with Liverpool School of Tropical Medicine and the University of Oxford (NIHR award 200907), Wellcome Trust and Department for International Development (215091/Z/18/Z), and the Bill and Melinda Gates Foundation (OPP1209135). The views expressed are those of the authors and not necessarily those of the DHSC, DID, NIHR, MRC, Wellcome Trust or PHE. Study registration ISRCTN66726260. MJK, LD, AH and MJT were supported by the Engineering and Physical Sciences Research Council through the MathSys CDT (grant number EP/S022244/1); MJK, LD, MJK and EMH were supported by the Medical Research Council through the COVID-19 Rapid Response Rolling Call (grant number MR/V009761/1); GG-F was supported by the Biotechnology and Biological Sciences Research Council through the MIBTP (BB/M01116X/1); MJK, LD and MJT were supported by UKRI through the JUNIPER modelling consortium (grant number MR/V038613/1). MJK is affiliated with the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Gastrointestinal Infections at the University of Liverpool in partnership with the UK Health Security Agency (UKHSA), in collaboration with the University of Warwick. MJK is also affiliated to the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Genomics and Enabling Data at the University of Warwick in partnership with UKHSA. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, the Department of Health and Social Care or the UK Health Security Agency. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.",

year = "2022",

month = sep,

doi = "10.1177/09622802211070257",

language = "English",

volume = "31",

pages = "1716–1737",

journal = "Statistical Methods in Medical Research",

issn = "0962-2802",

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number = "9",

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TY - JOUR

T1 - Fitting to the UK COVID-19 outbreak, short-term forecasts and estimating the reproductive number

AU - ISARIC4C Investigators

AU - Keeling, Matt J.

AU - Dyson, Louise

AU - Guyver-Fletcher, Glen

AU - Holmes, Alexander

AU - Semple, Malcolm G

AU - Tildesley, Michael J.

AU - Hill, Edward M.

AU - Green, Christopher

N1 - Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by grants from the National Institute for Health Research (award CO-CIN-01), the Medical Research Council (grant MC_PC_19059) and by the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Emerging and Zoonotic Infections at the University of Liverpool in partnership with PHE, in collaboration with Liverpool School of Tropical Medicine and the University of Oxford (NIHR award 200907), Wellcome Trust and Department for International Development (215091/Z/18/Z), and the Bill and Melinda Gates Foundation (OPP1209135). The views expressed are those of the authors and not necessarily those of the DHSC, DID, NIHR, MRC, Wellcome Trust or PHE. Study registration ISRCTN66726260. MJK, LD, AH and MJT were supported by the Engineering and Physical Sciences Research Council through the MathSys CDT (grant number EP/S022244/1); MJK, LD, MJK and EMH were supported by the Medical Research Council through the COVID-19 Rapid Response Rolling Call (grant number MR/V009761/1); GG-F was supported by the Biotechnology and Biological Sciences Research Council through the MIBTP (BB/M01116X/1); MJK, LD and MJT were supported by UKRI through the JUNIPER modelling consortium (grant number MR/V038613/1). MJK is affiliated with the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Gastrointestinal Infections at the University of Liverpool in partnership with the UK Health Security Agency (UKHSA), in collaboration with the University of Warwick. MJK is also affiliated to the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Genomics and Enabling Data at the University of Warwick in partnership with UKHSA. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, the Department of Health and Social Care or the UK Health Security Agency. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

PY - 2022/9

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N2 - The COVID-19 pandemic has brought to the fore the need for policy makers to receive timely and ongoing scientific guidance in response to this recently emerged human infectious disease. Fitting mathematical models of infectious disease transmission to the available epidemiological data provide a key statistical tool for understanding the many quantities of interest that are not explicit in the underlying epidemiological data streams. Of these, the effective reproduction number, R, has taken on special significance in terms of the general understanding of whether the epidemic is under control (R<1). Unfortunately, none of the epidemiological data streams are designed for modelling, hence assimilating information from multiple (often changing) sources of data is a major challenge that is particularly stark in novel disease outbreaks. Here, focusing on the dynamics of the first wave (March–June 2020), we present in some detail the inference scheme employed for calibrating the Warwick COVID-19 model to the available public health data streams, which span hospitalisations, critical care occupancy, mortality and serological testing. We then perform computational simulations, making use of the acquired parameter posterior distributions, to assess how the accuracy of short-term predictions varied over the time course of the outbreak. To conclude, we compare how refinements to data streams and model structure impact estimates of epidemiological measures, including the estimated growth rate and daily incidence.

AB - The COVID-19 pandemic has brought to the fore the need for policy makers to receive timely and ongoing scientific guidance in response to this recently emerged human infectious disease. Fitting mathematical models of infectious disease transmission to the available epidemiological data provide a key statistical tool for understanding the many quantities of interest that are not explicit in the underlying epidemiological data streams. Of these, the effective reproduction number, R, has taken on special significance in terms of the general understanding of whether the epidemic is under control (R<1). Unfortunately, none of the epidemiological data streams are designed for modelling, hence assimilating information from multiple (often changing) sources of data is a major challenge that is particularly stark in novel disease outbreaks. Here, focusing on the dynamics of the first wave (March–June 2020), we present in some detail the inference scheme employed for calibrating the Warwick COVID-19 model to the available public health data streams, which span hospitalisations, critical care occupancy, mortality and serological testing. We then perform computational simulations, making use of the acquired parameter posterior distributions, to assess how the accuracy of short-term predictions varied over the time course of the outbreak. To conclude, we compare how refinements to data streams and model structure impact estimates of epidemiological measures, including the estimated growth rate and daily incidence.

KW - COVID-19

KW - severe acute respiratory syndrome coronavirus 2

KW - mathematical modelling

KW - Markov chain Monte Carlo

KW - Bayesian inference

KW - epidemiology

KW - growth rate

KW - reproduction number

KW - short-term forecasts

U2 - 10.1177/09622802211070257

DO - 10.1177/09622802211070257

M3 - Article

SN - 0962-2802

VL - 31

SP - 1716

EP - 1737

JO - Statistical Methods in Medical Research

JF - Statistical Methods in Medical Research

IS - 9

ER -

Fitting to the UK COVID-19 outbreak, short-term forecasts and estimating the reproductive number

Abstract

Bibliographical note

Keywords

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Cite this