Estimating the COVID-19 epidemic trajectory and hospital capacity requirements in South West England: a mathematical modelling framework

Ross D Booton; Louis MacGregor; Lucy Vass; Katharine J Looker; Catherine Hyams; Philip D Bright; Irasha Harding; Rajeka Lazarus; Fergus Hamilton; Daniel Lawson; Leon Danon; Adrian Pratt; Richard Wood; Ellen Brooks-Pollock; Katherine M E Turner

doi:10.1136/bmjopen-2020-041536

Estimating the COVID-19 epidemic trajectory and hospital capacity requirements in South West England: a mathematical modelling framework

Ross D Booton, Louis MacGregor, Lucy Vass, Katharine J Looker, Catherine Hyams, Philip D Bright, Irasha Harding, Rajeka Lazarus, Fergus Hamilton, Daniel Lawson, Leon Danon, Adrian Pratt, Richard Wood, Ellen Brooks-Pollock, Katherine M E Turner^*

^*Corresponding author for this work

Research output: Contribution to journal › Article (Academic Journal) › peer-review

22 Citations (Scopus)

191 Downloads (Pure)

Abstract

OBJECTIVES: To develop a regional model of COVID-19 dynamics for use in estimating the number of infections, deaths and required acute and intensive care (IC) beds using the South West England (SW) as an example case.

DESIGN: Open-source age-structured variant of a susceptible-exposed-infectious-recovered compartmental mathematical model. Latin hypercube sampling and maximum likelihood estimation were used to calibrate to cumulative cases and cumulative deaths.

SETTING: SW at a time considered early in the pandemic, where National Health Service authorities required evidence to guide localised planning and support decision-making.

PARTICIPANTS: Publicly available data on patients with COVID-19.

PRIMARY AND SECONDARY OUTCOME MEASURES: The expected numbers of infected cases, deaths due to COVID-19 infection, patient occupancy of acute and IC beds and the reproduction ('R') number over time.

RESULTS: SW model projections indicate that, as of 11 May 2020 (when 'lockdown' measures were eased), 5793 (95% credible interval (CrI) 2003 to 12 051) individuals were still infectious (0.10% of the total SW population, 95% CrI 0.04% to 0.22%), and a total of 189 048 (95% CrI 141 580 to 277 955) had been infected with the virus (either asymptomatically or symptomatically), but recovered, which is 3.4% (95% CrI 2.5% to 5.0%) of the SW population. The total number of patients in acute and IC beds in the SW on 11 May 2020 was predicted to be 701 (95% CrI 169 to 1543) and 110 (95% CrI 8 to 464), respectively. The R value in SW was predicted to be 2.6 (95% CrI 2.0 to 3.2) prior to any interventions, with social distancing reducing this to 2.3 (95% CrI 1.8 to 2.9) and lockdown/school closures further reducing the R value to 0.6 (95% CrI 0.5 to 0.7).

CONCLUSIONS: The developed model has proved a valuable asset for regional healthcare services. The model will be used further in the SW as the pandemic evolves, and-as open-source software-is portable to healthcare systems in other geographies.

Original language	English
Article number	e041536
Number of pages	10
Journal	BMJ Open
Volume	11
Issue number	1
DOIs	https://doi.org/10.1136/bmjopen-2020-041536
Publication status	Published - 7 Jan 2021

Bibliographical note

Funding Information:
This work was supported by Global Public Health strand of the Elizabeth Blackwell Institute for Health Research, funded under the University of Bristol's QR GCRF strategy (award number ISSF3: 204813/Z/16/Z). This work was also funded with support from Bristol UNCOVER (Bristol COVID Emergency Research, award number ISSF3: 204813/Z/16/Z) and Medical Research Council UK (award number MR/S004769/1). LM, KJL, EBP and KMET acknowledge the support from the NIHR Health Protection Research Unit in Behavioural Science and Evaluation at the University of Bristol (award number NIHR200877). This work was also supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, National Institute for Health Research, Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (South Western Ireland), British Heart Foundation and Wellcome (award number CFC0129).

Funding Information:
Funding This work was supported by Global Public Health strand of the Elizabeth Blackwell Institute for Health Research, funded under the University of Bristol’s QR GCRF strategy (award number ISSF3: 204813/Z/16/Z). This work was also funded with support from Bristol UNCOVER (Bristol COVID Emergency Research, award number ISSF3: 204813/Z/16/Z) and Medical Research Council UK (award number MR/S004769/1). LM, KJL, EBP and KMET acknowledge the support from the NIHR Health Protection Research Unit in Behavioural Science and Evaluation at the University of Bristol (award number NIHR200877). This work was also supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, National Institute for Health Research, Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (South Western Ireland), British Heart Foundation and Wellcome (award number CFC0129).

Publisher Copyright:
© Author(s) (or their employer(s)) 2020.

Research Groups and Themes

Covid19

Keywords

Adolescent
Adult
Aged
COVID-19/epidemiology
Child
Child, Preschool
Critical Care/statistics & numerical data
Decision Making
England/epidemiology
Female
Hospital Bed Capacity/statistics & numerical data
Hospitalization/statistics & numerical data
Humans
Infant
Infant, Newborn
Intensive Care Units
Male
Middle Aged
Models, Theoretical
Regional Health Planning
SARS-CoV-2
State Medicine
Surge Capacity
Young Adult

Access to Document

10.1136/bmjopen-2020-041536

Full-text PDF (final published version)
This is the final published version of the article (version of record). It first appeared online via BMJ at http://dx.doi.org/10.1136/bmjopen-2020-041536 . Please refer to any applicable terms of use of the publisher.
Final published version, 2.01 MBLicence: CC BY

Handle.net

Persistent link

Cite this

Booton, R. D., MacGregor, L., Vass, L., Looker, K. J., Hyams, C., Bright, P. D., Harding, I., Lazarus, R., Hamilton, F., Lawson, D., Danon, L., Pratt, A., Wood, R., Brooks-Pollock, E., & Turner, K. M. E. (2021). Estimating the COVID-19 epidemic trajectory and hospital capacity requirements in South West England: a mathematical modelling framework. BMJ Open, 11(1), Article e041536. https://doi.org/10.1136/bmjopen-2020-041536

@article{c2ce8f2fdba840658a66ec5dfece7529,

title = "Estimating the COVID-19 epidemic trajectory and hospital capacity requirements in South West England: a mathematical modelling framework",

abstract = "OBJECTIVES: To develop a regional model of COVID-19 dynamics for use in estimating the number of infections, deaths and required acute and intensive care (IC) beds using the South West England (SW) as an example case.DESIGN: Open-source age-structured variant of a susceptible-exposed-infectious-recovered compartmental mathematical model. Latin hypercube sampling and maximum likelihood estimation were used to calibrate to cumulative cases and cumulative deaths.SETTING: SW at a time considered early in the pandemic, where National Health Service authorities required evidence to guide localised planning and support decision-making.PARTICIPANTS: Publicly available data on patients with COVID-19.PRIMARY AND SECONDARY OUTCOME MEASURES: The expected numbers of infected cases, deaths due to COVID-19 infection, patient occupancy of acute and IC beds and the reproduction ('R') number over time.RESULTS: SW model projections indicate that, as of 11 May 2020 (when 'lockdown' measures were eased), 5793 (95% credible interval (CrI) 2003 to 12 051) individuals were still infectious (0.10% of the total SW population, 95% CrI 0.04% to 0.22%), and a total of 189 048 (95% CrI 141 580 to 277 955) had been infected with the virus (either asymptomatically or symptomatically), but recovered, which is 3.4% (95% CrI 2.5% to 5.0%) of the SW population. The total number of patients in acute and IC beds in the SW on 11 May 2020 was predicted to be 701 (95% CrI 169 to 1543) and 110 (95% CrI 8 to 464), respectively. The R value in SW was predicted to be 2.6 (95% CrI 2.0 to 3.2) prior to any interventions, with social distancing reducing this to 2.3 (95% CrI 1.8 to 2.9) and lockdown/school closures further reducing the R value to 0.6 (95% CrI 0.5 to 0.7).CONCLUSIONS: The developed model has proved a valuable asset for regional healthcare services. The model will be used further in the SW as the pandemic evolves, and-as open-source software-is portable to healthcare systems in other geographies.",

keywords = "Adolescent, Adult, Aged, COVID-19/epidemiology, Child, Child, Preschool, Critical Care/statistics & numerical data, Decision Making, England/epidemiology, Female, Hospital Bed Capacity/statistics & numerical data, Hospitalization/statistics & numerical data, Humans, Infant, Infant, Newborn, Intensive Care Units, Male, Middle Aged, Models, Theoretical, Regional Health Planning, SARS-CoV-2, State Medicine, Surge Capacity, Young Adult",

author = "Booton, {Ross D} and Louis MacGregor and Lucy Vass and Looker, {Katharine J} and Catherine Hyams and Bright, {Philip D} and Irasha Harding and Rajeka Lazarus and Fergus Hamilton and Daniel Lawson and Leon Danon and Adrian Pratt and Richard Wood and Ellen Brooks-Pollock and Turner, {Katherine M E}",

note = "Funding Information: This work was supported by Global Public Health strand of the Elizabeth Blackwell Institute for Health Research, funded under the University of Bristol's QR GCRF strategy (award number ISSF3: 204813/Z/16/Z). This work was also funded with support from Bristol UNCOVER (Bristol COVID Emergency Research, award number ISSF3: 204813/Z/16/Z) and Medical Research Council UK (award number MR/S004769/1). LM, KJL, EBP and KMET acknowledge the support from the NIHR Health Protection Research Unit in Behavioural Science and Evaluation at the University of Bristol (award number NIHR200877). This work was also supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, National Institute for Health Research, Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (South Western Ireland), British Heart Foundation and Wellcome (award number CFC0129). Funding Information: Funding This work was supported by Global Public Health strand of the Elizabeth Blackwell Institute for Health Research, funded under the University of Bristol{\textquoteright}s QR GCRF strategy (award number ISSF3: 204813/Z/16/Z). This work was also funded with support from Bristol UNCOVER (Bristol COVID Emergency Research, award number ISSF3: 204813/Z/16/Z) and Medical Research Council UK (award number MR/S004769/1). LM, KJL, EBP and KMET acknowledge the support from the NIHR Health Protection Research Unit in Behavioural Science and Evaluation at the University of Bristol (award number NIHR200877). This work was also supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, National Institute for Health Research, Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (South Western Ireland), British Heart Foundation and Wellcome (award number CFC0129). Publisher Copyright: {\textcopyright} Author(s) (or their employer(s)) 2020.",

year = "2021",

month = jan,

day = "7",

doi = "10.1136/bmjopen-2020-041536",

language = "English",

volume = "11",

journal = "BMJ Open",

issn = "2044-6055",

publisher = "BMJ Publishing Group",

number = "1",

}

Booton, RD, MacGregor, L, Vass, L , Looker, KJ , Hyams, C, Bright, PD, Harding, I, Lazarus, R , Hamilton, F , Lawson, D , Danon, L, Pratt, A, Wood, R, Brooks-Pollock, E & Turner, KME 2021, 'Estimating the COVID-19 epidemic trajectory and hospital capacity requirements in South West England: a mathematical modelling framework', BMJ Open, vol. 11, no. 1, e041536. https://doi.org/10.1136/bmjopen-2020-041536

TY - JOUR

T1 - Estimating the COVID-19 epidemic trajectory and hospital capacity requirements in South West England

T2 - a mathematical modelling framework

AU - Booton, Ross D

AU - MacGregor, Louis

AU - Vass, Lucy

AU - Looker, Katharine J

AU - Hyams, Catherine

AU - Bright, Philip D

AU - Harding, Irasha

AU - Lazarus, Rajeka

AU - Hamilton, Fergus

AU - Lawson, Daniel

AU - Danon, Leon

AU - Pratt, Adrian

AU - Wood, Richard

AU - Brooks-Pollock, Ellen

AU - Turner, Katherine M E

N1 - Funding Information: This work was supported by Global Public Health strand of the Elizabeth Blackwell Institute for Health Research, funded under the University of Bristol's QR GCRF strategy (award number ISSF3: 204813/Z/16/Z). This work was also funded with support from Bristol UNCOVER (Bristol COVID Emergency Research, award number ISSF3: 204813/Z/16/Z) and Medical Research Council UK (award number MR/S004769/1). LM, KJL, EBP and KMET acknowledge the support from the NIHR Health Protection Research Unit in Behavioural Science and Evaluation at the University of Bristol (award number NIHR200877). This work was also supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, National Institute for Health Research, Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (South Western Ireland), British Heart Foundation and Wellcome (award number CFC0129). Funding Information: Funding This work was supported by Global Public Health strand of the Elizabeth Blackwell Institute for Health Research, funded under the University of Bristol’s QR GCRF strategy (award number ISSF3: 204813/Z/16/Z). This work was also funded with support from Bristol UNCOVER (Bristol COVID Emergency Research, award number ISSF3: 204813/Z/16/Z) and Medical Research Council UK (award number MR/S004769/1). LM, KJL, EBP and KMET acknowledge the support from the NIHR Health Protection Research Unit in Behavioural Science and Evaluation at the University of Bristol (award number NIHR200877). This work was also supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, National Institute for Health Research, Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (South Western Ireland), British Heart Foundation and Wellcome (award number CFC0129). Publisher Copyright: © Author(s) (or their employer(s)) 2020.

PY - 2021/1/7

Y1 - 2021/1/7

N2 - OBJECTIVES: To develop a regional model of COVID-19 dynamics for use in estimating the number of infections, deaths and required acute and intensive care (IC) beds using the South West England (SW) as an example case.DESIGN: Open-source age-structured variant of a susceptible-exposed-infectious-recovered compartmental mathematical model. Latin hypercube sampling and maximum likelihood estimation were used to calibrate to cumulative cases and cumulative deaths.SETTING: SW at a time considered early in the pandemic, where National Health Service authorities required evidence to guide localised planning and support decision-making.PARTICIPANTS: Publicly available data on patients with COVID-19.PRIMARY AND SECONDARY OUTCOME MEASURES: The expected numbers of infected cases, deaths due to COVID-19 infection, patient occupancy of acute and IC beds and the reproduction ('R') number over time.RESULTS: SW model projections indicate that, as of 11 May 2020 (when 'lockdown' measures were eased), 5793 (95% credible interval (CrI) 2003 to 12 051) individuals were still infectious (0.10% of the total SW population, 95% CrI 0.04% to 0.22%), and a total of 189 048 (95% CrI 141 580 to 277 955) had been infected with the virus (either asymptomatically or symptomatically), but recovered, which is 3.4% (95% CrI 2.5% to 5.0%) of the SW population. The total number of patients in acute and IC beds in the SW on 11 May 2020 was predicted to be 701 (95% CrI 169 to 1543) and 110 (95% CrI 8 to 464), respectively. The R value in SW was predicted to be 2.6 (95% CrI 2.0 to 3.2) prior to any interventions, with social distancing reducing this to 2.3 (95% CrI 1.8 to 2.9) and lockdown/school closures further reducing the R value to 0.6 (95% CrI 0.5 to 0.7).CONCLUSIONS: The developed model has proved a valuable asset for regional healthcare services. The model will be used further in the SW as the pandemic evolves, and-as open-source software-is portable to healthcare systems in other geographies.

AB - OBJECTIVES: To develop a regional model of COVID-19 dynamics for use in estimating the number of infections, deaths and required acute and intensive care (IC) beds using the South West England (SW) as an example case.DESIGN: Open-source age-structured variant of a susceptible-exposed-infectious-recovered compartmental mathematical model. Latin hypercube sampling and maximum likelihood estimation were used to calibrate to cumulative cases and cumulative deaths.SETTING: SW at a time considered early in the pandemic, where National Health Service authorities required evidence to guide localised planning and support decision-making.PARTICIPANTS: Publicly available data on patients with COVID-19.PRIMARY AND SECONDARY OUTCOME MEASURES: The expected numbers of infected cases, deaths due to COVID-19 infection, patient occupancy of acute and IC beds and the reproduction ('R') number over time.RESULTS: SW model projections indicate that, as of 11 May 2020 (when 'lockdown' measures were eased), 5793 (95% credible interval (CrI) 2003 to 12 051) individuals were still infectious (0.10% of the total SW population, 95% CrI 0.04% to 0.22%), and a total of 189 048 (95% CrI 141 580 to 277 955) had been infected with the virus (either asymptomatically or symptomatically), but recovered, which is 3.4% (95% CrI 2.5% to 5.0%) of the SW population. The total number of patients in acute and IC beds in the SW on 11 May 2020 was predicted to be 701 (95% CrI 169 to 1543) and 110 (95% CrI 8 to 464), respectively. The R value in SW was predicted to be 2.6 (95% CrI 2.0 to 3.2) prior to any interventions, with social distancing reducing this to 2.3 (95% CrI 1.8 to 2.9) and lockdown/school closures further reducing the R value to 0.6 (95% CrI 0.5 to 0.7).CONCLUSIONS: The developed model has proved a valuable asset for regional healthcare services. The model will be used further in the SW as the pandemic evolves, and-as open-source software-is portable to healthcare systems in other geographies.

KW - Adolescent

KW - Adult

KW - Aged

KW - COVID-19/epidemiology

KW - Child

KW - Child, Preschool

KW - Critical Care/statistics & numerical data

KW - Decision Making

KW - England/epidemiology

KW - Female

KW - Hospital Bed Capacity/statistics & numerical data

KW - Hospitalization/statistics & numerical data

KW - Humans

KW - Infant

KW - Infant, Newborn

KW - Intensive Care Units

KW - Male

KW - Middle Aged

KW - Models, Theoretical

KW - Regional Health Planning

KW - SARS-CoV-2

KW - State Medicine

KW - Surge Capacity

KW - Young Adult

U2 - 10.1136/bmjopen-2020-041536

DO - 10.1136/bmjopen-2020-041536

M3 - Article (Academic Journal)

C2 - 33414147

SN - 2044-6055

VL - 11

JO - BMJ Open

JF - BMJ Open

IS - 1

M1 - e041536

ER -

Estimating the COVID-19 epidemic trajectory and hospital capacity requirements in South West England: a mathematical modelling framework

Abstract

Bibliographical note

Research Groups and Themes

Keywords

Access to Document

Handle.net

Fingerprint

Cite this