ABSTRACT
ObjectivesTo 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. DesignOpen-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. SettingSW at a time considered early in the pandemic, where National Health Service (NHS) authorities required evidence to guide localised planning and support decision-making. ParticipantsPublicly-available data on COVID-19 patients. Primary and secondary outcome measuresThe 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. ResultsSW 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). ConclusionsThe 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/ applicationsO_LIOpen-source modelling tool available for wider use and re-use. C_LIO_LICustomisable to a number of granularities such as at the local, regional and national level. C_LIO_LISupports a more holistic understanding of intervention efficacy through estimating unobservable quantities, e.g. asymptomatic population. C_LIO_LIWhile not presented here, future use of the model could evaluate the effect of various interventions on transmission of COVID-19. C_LIO_LIFurther developments could consider the impact of bedded capacity in terms of resulting excess deaths. C_LI
