Estimating the COVID-19 epidemic trajectory and hospital capacity requirements in South West England: a mathematical modelling framework
Ross. D. Booton,
Katharine J. Looker,
Philip D Bright,
Fergus W Hamilton,
Daniel J Lawson,
Ellen Brooks Pollock,
Katy M.E. Turner
Posted 12 Jun 2020
medRxiv DOI: 10.1101/2020.06.10.20084715
Posted 12 Jun 2020
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 of England (SW) as an example case. Design: Open-source age-structured variant of a susceptible-exposed-infectious-recovered (SEIR) deterministic 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 (NHS) authorities required evidence to guide localised planning and support decision-making. Participants: Publicly-available data on COVID-19 patients. 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 the 11th May 2020 (when 'lockdown' measures were eased), 5,793 (95% credible interval, CrI, 2,003 - 12,051) individuals were still infectious (0.10% of the total SW England population, 95%CrI 0.04 - 0.22%), and a total of 189,048 (95%CrI 141,580 - 277,955) had been infected with the virus (either asymptomatically or symptomatically), but recovered, which is 3.4% (95%CrI 2.5 - 5.0%) of the SW population. The total number of patients in acute and IC beds in the SW on the 11th May 2020 was predicted to be 701 (95%CrI 169 - 1,543) and 110 (95%CrI 8 - 464) respectively. The R value in SW England was predicted to be 2.6 (95%CrI 2.0 - 3.2) prior to any interventions, with social distancing reducing this to 2.3 (95%CrI 1.8 - 2.9) and lockdown/ school closures further reducing the R value to 0.6 (95CrI% 0.5 - 0.7). Conclusions: The developed model has proved a valuable asset for local and 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.
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