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

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 11^th 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 11^th 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.

Future work/ applications

• Open-source modelling tool available for wider use and re-use.

• Customisable to a number of granularities such as at the local, regional and national level.

• Supports a more holistic understanding of intervention efficacy through estimating unobservable quantities, e.g. asymptomatic population.

• While not presented here, future use of the model could evaluate the effect of various interventions on transmission of COVID-19.

• Further developments could consider the impact of bedded capacity in terms of resulting excess deaths.