Estimating the COVID-19 epidemic trajectory and hospital capacity requirements in South West England: a mathematical modelling framework
| Autor: | Catherine Hyams, Richard M Wood, Rajeka Lazarus, Ellen Brooks-Pollock, Katherine Mary Elizabeth Turner, Louis MacGregor, Philip D Bright, Daniel Lawson, Fergus Hamilton, Katharine J. Looker, Irasha Harding, Ross D. Booton, Lucy Vass, Leon Danon, Adrian C Pratt |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Male
Psychological intervention lcsh:Medicine 01 natural sciences State Medicine 0302 clinical medicine Epidemiology Health care Pandemic Credible interval Medicine 030212 general & internal medicine Child COVID-19/epidemiology education.field_of_study Covid19 General Medicine Middle Aged infection control Hospital Bed Capacity/statistics & numerical data Hospitalization Intensive Care Units Geography England Child Preschool epidemiology Female Public Health Adult medicine.medical_specialty Coronavirus disease 2019 (COVID-19) Occupancy Adolescent Critical Care Population Decision Making Regional Health Planning 03 medical and health sciences Young Adult Intensive care Humans Asset (economics) 0101 mathematics England/epidemiology education Hospitalization/statistics & numerical data Aged business.industry SARS-CoV-2 Public health 010102 general mathematics lcsh:R Critical Care/statistics & numerical data Infant Newborn Surge Capacity COVID-19 Infant Models Theoretical Hospital Bed Capacity business Demography |
| Zdroj: | BMJ Open 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, vol. 11, no. 1, e041536 . https://doi.org/10.1136/bmjopen-2020-041536 BMJ Open, Vol 11, Iss 1 (2021) |
| ISSN: | 2044-6055 |
| Popis: | 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/ applicationsOpen-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. |
| Databáze: | OpenAIRE |
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