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 journalArticle (Academic Journal)peer-review

6 Citations (Scopus)
88 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 languageEnglish
Article numbere041536
Number of pages10
JournalBMJ Open
Volume11
Issue number1
DOIs
Publication statusPublished - 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.

Structured keywords

  • 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

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