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LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
1 15-23 Disease denotes COVID-19 MESH:C000657245
5 189-197 Disease denotes COVID-19 MESH:C000657245
6 243-253 Disease denotes infections MESH:D007239
7 255-261 Disease denotes deaths MESH:D003643
9 604-610 Disease denotes deaths MESH:D003643
12 826-834 Species denotes patients Tax:9606
13 840-848 Disease denotes COVID-19 MESH:C000657245
19 964-971 Species denotes patient Tax:9606
20 914-922 Disease denotes infected MESH:D007239
21 930-936 Disease denotes deaths MESH:D003643
22 944-952 Disease denotes COVID-19 MESH:C000657245
23 953-962 Disease denotes infection MESH:D007239
29 1459-1464 Gene denotes CrI 2 Gene:163126
30 1802-1807 Gene denotes CrI 1 Gene:23741
31 1709-1714 Gene denotes CrI 2 Gene:163126
32 1519-1527 Species denotes patients Tax:9606
33 1355-1363 Disease denotes infected MESH:D007239
35 2613-2621 Disease denotes COVID-19 MESH:C000657245
37 2718-2724 Disease denotes deaths MESH:D003643
42 2833-2843 Species denotes SARS-CoV-2 Tax:2697049
43 3118-3123 Species denotes human Tax:9606
44 2801-2809 Disease denotes COVID-19 MESH:C000657245
45 3024-3030 Disease denotes deaths MESH:D003643
54 3527-3535 Disease denotes COVID-19 MESH:C000657245
55 3834-3842 Disease denotes COVID-19 MESH:C000657245
56 3843-3853 Disease denotes infections MESH:D007239
57 3932-3940 Disease denotes COVID-19 MESH:C000657245
58 4180-4188 Disease denotes COVID-19 MESH:C000657245
59 4354-4362 Disease denotes COVID-19 MESH:C000657245
60 4678-4686 Disease denotes COVID-19 MESH:C000657245
61 4794-4802 Disease denotes COVID-19 MESH:C000657245
65 5366-5374 Disease denotes COVID-19 MESH:C000657245
66 5589-5598 Disease denotes infection MESH:D007239
67 5622-5627 Disease denotes death MESH:D003643
72 5979-5987 Species denotes patients Tax:9606
73 5888-5896 Disease denotes COVID-19 MESH:C000657245
74 5963-5972 Disease denotes mortality MESH:D003643
75 6325-6334 Disease denotes mortality MESH:D003643
77 7269-7277 Disease denotes COVID-19 MESH:C000657245
80 7542-7548 Disease denotes deaths MESH:D003643
81 7579-7589 Disease denotes infections MESH:D007239
83 7804-7812 Disease denotes COVID-19 MESH:C000657245
88 8533-8541 Disease denotes COVID-19 MESH:C000657245
89 8676-8685 Disease denotes infection MESH:D007239
90 8731-8740 Disease denotes infection MESH:D007239
91 8913-8918 Disease denotes death MESH:D003643
97 9125-9131 Species denotes people Tax:9606
98 9090-9099 Disease denotes infection MESH:D007239
99 9173-9182 Disease denotes infection MESH:D007239
100 9552-9558 Disease denotes deaths MESH:D003643
101 9606-9614 Disease denotes COVID-19 MESH:C000657245
104 9764-9772 Disease denotes COVID-19 MESH:C000657245
105 10028-10036 Disease denotes COVID-19 MESH:C000657245
111 11338-11346 Species denotes patients Tax:9606
112 10256-10265 Disease denotes infection MESH:D007239
113 10378-10387 Disease denotes infection MESH:D007239
114 10422-10431 Disease denotes infection MESH:D007239
115 11234-11239 Disease denotes death MESH:D003643
120 13755-13763 Disease denotes COVID-19 MESH:C000657245
121 13803-13809 Disease denotes deaths MESH:D003643
123 13974-13983 Disease denotes infection MESH:D007239
125 14010-14019 Disease denotes infection MESH:D007239
127 14300-14308 Disease denotes COVID-19 MESH:C000657245
139 15081-15087 Disease denotes deaths MESH:D003643
140 15145-15153 Disease denotes COVID-19 MESH:C000657245
141 15202-15210 Disease denotes COVID-19 MESH:C000657245
142 15219-15225 Disease denotes deaths MESH:D003643
143 15375-15384 Disease denotes mortality MESH:D003643
144 15437-15445 Disease denotes COVID-19 MESH:C000657245
145 15454-15460 Disease denotes deaths MESH:D003643
146 15535-15543 Disease denotes COVID-19 MESH:C000657245
147 15544-15550 Disease denotes deaths MESH:D003643
148 15656-15665 Disease denotes mortality MESH:D003643
149 15709-15715 Disease denotes deaths MESH:D003643
151 15896-15900 Disease denotes fits MESH:D012640
154 16051-16061 Disease denotes infections MESH:D007239
155 17497-17505 Disease denotes COVID-19 MESH:C000657245
160 18113-18119 Disease denotes deaths MESH:D003643
161 18699-18705 Disease denotes deaths MESH:D003643
162 18824-18829 Disease denotes death MESH:D003643
163 18882-18887 Disease denotes death MESH:D003643
168 19063-19069 Disease denotes deaths MESH:D003643
169 19176-19184 Disease denotes infected MESH:D007239
170 19325-19334 Disease denotes mortality MESH:D003643
171 19340-19348 Disease denotes COVID-19 MESH:C000657245
176 19559-19563 Disease denotes fits MESH:D012640
177 19633-19641 Disease denotes COVID-19 MESH:C000657245
178 19659-19668 Disease denotes mortality MESH:D003643
179 19676-19684 Disease denotes COVID-19 MESH:C000657245
183 19938-19946 Disease denotes COVID-19 MESH:C000657245
184 20012-20018 Disease denotes deaths MESH:D003643
185 20050-20058 Disease denotes COVID-19 MESH:C000657245
187 20134-20142 Disease denotes COVID-19 MESH:C000657245
190 20649-20654 Gene denotes CrI 2 Gene:163126
191 20253-20263 Disease denotes infections MESH:D007239
194 21126-21134 Species denotes patients Tax:9606
195 21140-21148 Disease denotes COVID-19 MESH:C000657245
198 21190-21198 Species denotes patients Tax:9606
199 21311-21319 Species denotes patients Tax:9606
204 21596-21604 Species denotes patients Tax:9606
205 21717-21725 Species denotes patients Tax:9606
206 21741-21749 Disease denotes COVID-19 MESH:C000657245
207 21891-21895 Disease denotes fits MESH:D012640
211 22628-22633 Gene denotes CrI 1 Gene:23741
212 22537-22542 Gene denotes CrI 2 Gene:163126
213 22459-22467 Disease denotes COVID-19 MESH:C000657245
216 23267-23275 Disease denotes COVID-19 MESH:C000657245
217 23614-23622 Disease denotes COVID-19 MESH:C000657245
220 24100-24105 Gene denotes CrI 2 Gene:163126
221 23693-23699 Disease denotes deaths MESH:D003643
224 24350-24355 Gene denotes CrI 2 Gene:163126
225 24182-24190 Species denotes patients Tax:9606
232 24457-24461 Disease denotes fits MESH:D012640
233 24541-24547 Disease denotes deaths MESH:D003643
234 24981-24987 Disease denotes deaths MESH:D003643
235 25412-25418 Disease denotes deaths MESH:D003643
236 25569-25577 Disease denotes COVID-19 MESH:C000657245
237 25654-25660 Disease denotes deaths MESH:D003643
242 26984-26994 Disease denotes infections MESH:D007239
243 27149-27157 Disease denotes COVID-19 MESH:C000657245
244 27743-27753 Disease denotes infections MESH:D007239
245 28489-28497 Disease denotes COVID-19 MESH:C000657245
249 29472-29481 Disease denotes infection MESH:D007239
250 29657-29665 Disease denotes infected MESH:D007239
251 30481-30489 Disease denotes COVID-19 MESH:C000657245
265 31062-31073 Species denotes Nightingale Tax:383689
266 31394-31402 Species denotes patients Tax:9606
267 31621-31629 Species denotes patients Tax:9606
268 31688-31696 Species denotes patients Tax:9606
269 31772-31780 Species denotes patients Tax:9606
270 32038-32045 Species denotes patient Tax:9606
271 32821-32827 Species denotes people Tax:9606
272 30513-30521 Disease denotes COVID-19 MESH:C000657245
273 30843-30852 Disease denotes infection MESH:D007239
274 31158-31164 Disease denotes deaths MESH:D003643
275 31198-31203 Disease denotes death MESH:D003643
276 31353-31358 Disease denotes death MESH:D003643
277 32051-32059 Disease denotes COVID-19 MESH:C000657245
280 32925-32934 Disease denotes infection MESH:D007239
281 33285-33291 Disease denotes deaths MESH:D003643
284 34287-34295 Species denotes patients Tax:9606
285 34700-34708 Disease denotes infected MESH:D007239
291 35420-35425 Gene denotes CrI 2 Gene:163126
292 34908-34916 Disease denotes COVID-19 MESH:C000657245
293 35193-35201 Disease denotes COVID-19 MESH:C000657245
294 35334-35342 Disease denotes COVID-19 MESH:C000657245
295 35902-35910 Disease denotes COVID-19 MESH:C000657245
308 36882-36885 Gene denotes EBP Gene:10682
309 36913-36916 Gene denotes EBP Gene:10682
310 36965-36968 Gene denotes EBP Gene:10682
311 37101-37104 Gene denotes EBP Gene:10682
312 37208-37211 Gene denotes EBP Gene:10682
313 37313-37316 Gene denotes EBP Gene:10682
314 37059-37061 Disease denotes LV MESH:C535509
315 37085-37087 Disease denotes FH MESH:D006938
316 37166-37168 Disease denotes LV MESH:C535509
317 37192-37194 Disease denotes FH MESH:D006938
318 37271-37273 Disease denotes LV MESH:C535509
319 37297-37299 Disease denotes FH MESH:D006938
322 37746-37749 Gene denotes EBP Gene:10682
323 37615-37630 Disease denotes COVID Emergency MESH:C000657245
325 38470-38477 Species denotes Patient Tax:9606
327 38928-38936 Disease denotes COVID-19 MESH:C000657245

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T1 0-136 Sentence denotes Estimating the COVID-19 epidemic trajectory and hospital capacity requirements in South West England: a mathematical modelling framework
T2 138-146 Sentence denotes Abstract
T3 147-157 Sentence denotes Objectives
T4 158-363 Sentence denotes 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.
T5 365-371 Sentence denotes Design
T6 372-486 Sentence denotes Open-source age-structured variant of a susceptible-exposed-infectious-recovered compartmental mathematical model.
T7 487-611 Sentence denotes Latin hypercube sampling and maximum likelihood estimation were used to calibrate to cumulative cases and cumulative deaths.
T8 613-620 Sentence denotes Setting
T9 621-784 Sentence denotes SW at a time considered early in the pandemic, where National Health Service authorities required evidence to guide localised planning and support decision-making.
T10 786-798 Sentence denotes Participants
T11 799-849 Sentence denotes Publicly available data on patients with COVID-19.
T12 851-889 Sentence denotes Primary and secondary outcome measures
T13 890-1047 Sentence denotes 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.
T14 1049-1056 Sentence denotes Results
T15 1057-1498 Sentence denotes 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.
T16 1499-1661 Sentence denotes 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.
T17 1662-1904 Sentence denotes 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).
T18 1906-1917 Sentence denotes Conclusions
T19 1918-1999 Sentence denotes The developed model has proved a valuable asset for regional healthcare services.
T20 2000-2149 Sentence denotes 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.
T21 2151-2190 Sentence denotes Strengths and limitations of this study
T22 2191-2252 Sentence denotes Open-source modelling tool available for wider use and reuse.
T23 2253-2346 Sentence denotes Customisable to a number of granularities such as at the local, regional and national levels.
T24 2347-2492 Sentence denotes Supports a more holistic understanding of intervention efficacy through estimating unobservable quantities, for example, asymptomatic population.
T25 2493-2622 Sentence denotes While not presented here, future use of the model could evaluate the effect of various interventions on transmission of COVID-19.
T26 2623-2725 Sentence denotes Further developments could consider the impact of bedded capacity in terms of resulting excess deaths.
T27 2727-2739 Sentence denotes Introduction
T28 2740-3460 Sentence denotes Since the initial outbreak in 2019 in Hubei Province, China, COVID-19, the disease caused by SARS-CoV-2, has gone on to cause a pandemic.1 As of 11 May 2020, the Centre for Systems Science and Engineering at Johns Hopkins University reports over 4 000 000 confirmed cases and 250 000 deaths globally.2 National responses to the outbreak have varied: from severe restrictions on human mobility alongside widespread testing and contact tracing in China3 to the comparatively relaxed response in Sweden, where lockdown measures have not been enacted.4 In the UK, advice to socially distance if displaying symptoms was given on 15 March, while school closures and ‘lockdown’ measures were implemented from 23 March onwards.5
T29 3461-4376 Sentence denotes Mathematical modelling has been used to predict the course of the COVID-19 pandemic and to evaluate the effectiveness of proposed and enacted interventions.6–11 Prem et al 6 showed that the premature lifting of control strategies at the national level (within China) could lead to an earlier secondary peak; Flaxman et al7 used a semimechanistic model to predict the total COVID-19 infections in 11 countries; Ferguson et al8 used an individual-based simulation model of COVID-19 transmission to explore the effects of non-pharmaceutical interventions within the USA and Great Britain; Challen et al9 estimated the R number among regions of the UK; Danon et al10 used a spatial model to predict the potential course of COVID-19 in England and Wales in the absence of control measures; while Jarvis et al11 analysed the behavioural monitoring data to quantify the impact of control measures on COVID-19 transmission.
T30 4377-4854 Sentence denotes These models have been predominantly aimed at the national level and have largely been based on epidemiological and biological data sourced from the initial epidemic in Wuhan, China,12 and the first large outbreak in Lombardy, Italy.13 These models have also mainly focused on predicting the scale of COVID-19 transmission under various intervention measures, rather than producing estimates for potential numbers of COVID-19-related admissions to acute or intensive care (IC).
T31 4855-5049 Sentence denotes In the UK, the epidemic escalated most rapidly in London,14 and the majority of national modelling is seemingly driven by the trends in London due to its large case numbers and large population.
T32 5050-5255 Sentence denotes One of the key issues facing National Health Service (NHS) authorities is planning for more localised capacity needs and estimating the timings of surges in demand at a regional or healthcare system level.
T33 5256-5887 Sentence denotes This is especially challenging given the rapidly evolving epidemiological and biological data; the changes in COVID-19 testing availability (eg, previously limited and changing eligibility requirements); the uncertainty in the effectiveness of interventions in different contexts; significant and uncertain time lags between initial infection and hospitalisation or death; and different regions being at different points in the epidemic curve.9 South West England (SW) is the region with the lowest number of total cases in England (as of 11 May 2020), lagging behind the national data driven by the earlier epidemic in London.9 14
T34 5888-6210 Sentence denotes COVID-19 results in a significant requirement for hospitalisation and high mortality among patients requiring admission to critical care (particularly among those requiring ventilation).15 16 In the SW, the population is on average older than in London17 and is older than the UK as a whole (online supplemental table S1).
T35 6211-6469 Sentence denotes Older age puts individuals at elevated risk of requiring hospital care.18–20 Consequently, we might expect higher mortality and greater demand for beds in the SW than estimations output from national models that may lack such granularity or risk sensitivity.
T36 6470-6825 Sentence denotes Supplementary data However, the SW’s first case occurred around 2 weeks later than the first UK case14; perhaps implying that the local SW epidemic may be more effectively controlled due to a lower number of baseline cases (than the national average) at the time national interventions were implemented, as well as reduced transmission due to rurality.
T37 6826-7046 Sentence denotes This subnational analysis can support in mapping the local epidemic, planning local hospital capacity outside of the main urban centres and ensuring effective mobilisation of additional support and resources if required.
T38 7047-7306 Sentence denotes Should demand be lower than expected, reliable forecasts could facilitate more effective use of available resources through reintroducing elective treatments (that had initially been postponed) and responding to other non-COVID-19 sources of emergency demand.
T39 7307-7623 Sentence denotes In this study, taking into account the timeline of UK-wide non-pharmaceutical interventions (social distancing, school closures/lockdown), we illustrate use of our model in projecting estimates for the expected distributions of cases, deaths, asymptomatic and symptomatic infections and demand for acute and IC beds.
T40 7624-7695 Sentence denotes We present the model trajectories for SW using publicly available data.
T41 7697-7704 Sentence denotes Methods
T42 7705-7943 Sentence denotes We developed a deterministic, ordinary differential equation model of the transmission dynamics of COVID-19, including age-structured contact patterns, age-specific disease progression and demand for hospitalisation, both to acute and IC.
T43 7944-8048 Sentence denotes We then parameterised the model using available literature and calibrated the model to data from the SW.
T44 8049-8177 Sentence denotes The model is readily adapted to fit the data at subregional (eg, Clinical Commissioning Group, CCG), regional or national level.
T45 8178-8261 Sentence denotes Key assumptions of the model are summarised in the online supplemental information.
T46 8262-8382 Sentence denotes The model was developed in R and all code and links to source data are freely available (github.com/rdbooton/bricovmod).
T47 8383-8501 Sentence denotes The model is coded using package deSolve, with contact matrices from package socialmixr and sampling from package lhs.
T48 8503-8518 Sentence denotes Model structure
T49 8519-8919 Sentence denotes The stages of COVID-19 included within this model are S—susceptible, E—exposed (not currently infectious but have been exposed to the virus), A—asymptomatic infection (will never develop symptoms), I—symptomatic infection (consisting of presymptomatic or mild to moderate symptoms), H—severe symptoms requiring hospitalisation but not IC, C—very severe symptoms requiring IC, R—recovered and D—death.
T50 8920-8973 Sentence denotes The total population is N=S+E+A+I+H+C+R+D (figure 1).
T51 8974-9076 Sentence denotes Figure 1 Compartmental flow model diagram depicting stages of disease and transitions between states.
T52 9077-9300 Sentence denotes Asymptomatic infection represents the number of people never showing symptoms while symptomatic infection includes all those who show presymptomatic/mild symptoms to those who show more severe symptoms (prehospitalisation).
T53 9301-9423 Sentence denotes Those who are hospitalised first occupy a non-IC bed (acute bed) after which they can either move into IC, recover or die.
T54 9424-9517 Sentence denotes Those in IC can either recover or die at an increased rate compared with those in acute beds.
T55 9518-9615 Sentence denotes This model does not capture those deaths which occur outside of hospital as a result of COVID-19.
T56 9616-9635 Sentence denotes IC, intensive care.
T57 9636-9847 Sentence denotes Each compartment Xg is stratified by age group (0–4, 5–17, 18–29, 30–39, 40–49, 50–59, 60–69, ≥70) where X denotes the stage of COVID-19 (S, E, A, I, H, C, R, D) and g denotes the age group class of individuals.
T58 9848-10068 Sentence denotes Age groups were chosen to capture key social contact patterns (primary, secondary and tertiary education and employment) and variability in hospitalisation rates and outcomes from COVID-19 especially in older age groups.
T59 10069-10168 Sentence denotes The total in each age group is informed by recent Office for National Statistics (ONS) estimates.21
T60 10169-10329 Sentence denotes Susceptible individuals become exposed to the virus at a rate governed by the force of infection λg, and individuals are non-infectious in the exposed category.
T61 10330-10459 Sentence denotes A proportion δ move from exposed to symptomatic infection and the remaining to asymptomatic infection, both at the latent rate η.
T62 10460-10539 Sentence denotes Individuals leave both the asymptomatic and symptomatic compartments at rate μ.
T63 10540-10733 Sentence denotes All asymptomatic individuals eventually recover and there are no further stages of disease: the rate of leaving the asymptomatic compartment is therefore equivalent to the infectious period, μ.
T64 10734-10852 Sentence denotes A proportion of symptomatic individuals γg go on to develop severe symptoms which require hospitalisation, but not IC.
T65 10853-11148 Sentence denotes Once requiring hospitalisation, we assume individuals are no longer infectious to the general population due to self-isolation guidelines restricting further mixing with anyone aside from household members (if unable to be admitted to hospital) or front-line NHS staff (if admitted to hospital).
T66 11149-11318 Sentence denotes Individuals move out of the acute hospitalised compartment at rate ρ, either through death, being moved to IC at rate ϵ, or through recovery (all remaining individuals).
T67 11319-11408 Sentence denotes A proportion ωg of patients requiring IC will die at rate ψ, while the rest will recover.
T68 11409-11506 Sentence denotes The model (schematic in figure 1) is therefore described by the following differential equations:
T69 11507-11558 Sentence denotes Susceptible Sg (1a) d S g d t = − λ g S g
T70 11559-11614 Sentence denotes Exposed Eg (1b) d E g d t = λ g S g − η E g
T71 11615-11682 Sentence denotes Asymptomatic Ag (1c) d A g d t = η ( 1 − δ ) E g − μ A g
T72 11683-11738 Sentence denotes Infectious Ig (1d) d I g d t = η δ E g − μ I g
T73 11739-11813 Sentence denotes Hospitalised in acute bed Hg (1e) d H g d t = μ γ g I g − ρ H g
T74 11814-11877 Sentence denotes Hospitalised in IC Cg (1f) d C g d t = ρ ϵ H g − ψ C g
T75 11878-12009 Sentence denotes Recovered Rg (1g) d R g d t = μ A g + μ ( 1 − γ g ) I g + ( 1 − ϵ ) ( 1 − κ ) ρ H g + ( 1 − ω g ) ψ C g
T76 12010-12078 Sentence denotes Death Dg (1h) d D g d t = ( 1 − ϵ ) κ ρ H g + ω g ψ C g
T77 12080-12125 Sentence denotes Contact patterns under national interventions
T78 12126-12371 Sentence denotes We assume the population is stratified into predefined age groups with age-specific mixing pattern represented by a contact matrix M with an element of mij representing the contacts between someone of age group i∈G with someone of age group j∈G.
T79 12372-12672 Sentence denotes The baseline contact matrix (with no interventions in place) is taken from the POLYMOD survey conducted in the UK.22 The contact pattern may also be influenced by a range of interventions (social distancing was encouraged on 15 March 2020, schools were closed and lockdown occurred on 23 March 2020).
T80 12673-12920 Sentence denotes We implement these interventions by assuming that the percentage of 0–18 year-olds attending school after 23 March 2020 was 5% (reducing all contacts between school-age individuals by 95%) and that social distancing reduced all contacts by 0%–50%.
T81 12921-13067 Sentence denotes We take the element-wise minimum for each age group’s contact with another age group from all active interventions (distancing, schools/lockdown).
T82 13068-13284 Sentence denotes A study on postlockdown contact patterns (CoMix11) is used to inform contacts after lockdown (first survey 24 March 2020, with an average of 73% reduction in daily contacts observed per person compared with POLYMOD).
T83 13285-13397 Sentence denotes Moving between contact matrices of multiple interventions was implemented by assuming a phased, linear decrease.
T84 13398-13579 Sentence denotes After lockdown, we vary a parameter (endphase) to capture the time taken to fully adjust (across the population, on average) to the new measures (allowed to vary from 1 to 31 days).
T85 13580-13959 Sentence denotes This assumption represents the time taken for individuals to fully adapt to new measures (and household transmission), and is in line with data on the delay in the control of COVID-19 (reductions in hospital admissions and deaths after lockdown).23 The parameter endphase can be interpreted as accounting for the time taken to adjust to all interventions (and not just lockdown).
T86 13961-13983 Sentence denotes The force of infection
T87 13984-14162 Sentence denotes The age-specific force of infection λg depends on the proportion of the population who are infectious (asymptomatic Ag and symptomatic Ig only) and probability of transmission β:
T88 14163-14229 Sentence denotes (2) λ g = β ∑ i ∈ G m i g ( A i N g + I i N g )
T89 14231-14263 Sentence denotes The basic reproduction number R0
T90 14264-14619 Sentence denotes The basic reproduction number R0 of COVID-19 is estimated to be 2.79± 1.16.24 We include this estimate within our model by calculating the maximum eigenvalue of the contact matrix M, and allowing the transmission parameter to vary such that R0 is equal to the maximum eigenvalue of M multiplied by the infectious period μ and the transmission parameter β.
T91 14620-14784 Sentence denotes This gives the value for the initial basic reproduction number R0, which changes as the contact patterns change as lockdown and other interventions are implemented.
T92 14786-14822 Sentence denotes Parameter estimates and data sources
T93 14823-14864 Sentence denotes Model parameters are detailed in table 1.
T94 14865-14934 Sentence denotes We used available published literature to inform parameter estimates.
T95 14935-15370 Sentence denotes We used the following publicly available metrics for model fitting: regional cumulative cases in SW (tested and confirmed cases in hospital), and deaths (daily/cumulative counts) from the Public Health England COVID-19 dashboard,14 and ONS weekly provisional data on COVID-19-related deaths.25 The case data are finalised prior to the previous 5 days, so we include all data until 14 May 2020, based on data reported until 18 May 2020.
T96 15371-15585 Sentence denotes The mortality data from ONS do not explicitly state the number of COVID-19-related deaths occurring in hospital, but they do report this value nationally (83.9% of COVID-19 deaths in hospital, as of 17 April 2020).
T97 15586-15728 Sentence denotes We assume that this percentage applies to the SW data and rescale the mortality to 83.9% to represent an estimate of total deaths in hospital.
T98 15729-15794 Sentence denotes Table 1 Parameter estimates used in the model and their sources.
T99 15795-15900 Sentence denotes The distributions of unknown parameters are shown in online supplemental figure S1A for the best 100 fits
T100 15901-15967 Sentence denotes Symbol Description Uniform prior (min and max) or point estimate
T101 15968-16033 Sentence denotes 1 / η Duration of the non-infectious exposure period 5.1 days41
T102 16034-16127 Sentence denotes δ Percentage of infections which become symptomatic 82.1%42; vary between 73.15% and 91.05%
T103 16128-16231 Sentence denotes 1 / μ Duration of symptoms while not hospitalised (independent of outcome) Vary between 2 and 14 days
T104 16232-16321 Sentence denotes 1 / ρ Duration of stay in acute bed (independent of outcome) Vary between 2 and 14 days
T105 16322-16524 Sentence denotes γ g Percentage of symptomatic cases which will require hospitalisation 0–4=0.00%, 5–17=0.0408%, 18–29=1.04%,30–39=2.04%–7.00%, 40–49=2.53%–8.68%,50–59=4.86%–16.7%, 60–69=7.01%–24.0%,70+=9.87%–37.6%16
T106 16525-16596 Sentence denotes 1 / ψ Duration of stay in IC bed (independent of outcome) 3–11 days43
T107 16597-16690 Sentence denotes ϵ Percentage of those requiring hospitalisation who will require IC Vary between 0% and 30%
T108 16691-16840 Sentence denotes ω g Percentage of those requiring IC who will die 0–4=0.00%, 5–17=0.00%, 18–29=18.1%,30–39=18.1%, 40–49=24.7%,50–59=39.3%, 60–69=53.9%,70+=65.3%43
T109 16841-16935 Sentence denotes κ Percentage of those requiring acute beds (but not IC) who will die Vary between 5% and 35%
T110 16936-17020 Sentence denotes school Percentage of 0–18 year-olds attending school after 23 March 2020 Assume 5%
T111 17021-17140 Sentence denotes distancing Percentage reduction in contact rates due to social distancing after 15 March 2020 Vary between 0% and 50%
T112 17141-17244 Sentence denotes lockdown Percentage reduction in contact rates due to lockdown after 23 March 2020 Retail/recreation:
T113 17245-17348 Sentence denotes Bristol 86%, Bath 90%, Plymouth 85%, Gloucs 84%, Somerset 82%, Devon 85%, Dorset 84%44Transit stations:
T114 17349-17459 Sentence denotes Bristol 78%, Bath 71%, Plymouth 65%, Gloucs 69%, Somerset 67%, Devon 66%, Dorset 63%44Vary between 63% and 90%
T115 17460-17518 Sentence denotes R 0 Initial reproductive number of COVID-19 1.63–3.9524
T116 17519-17640 Sentence denotes endphase Time taken to fully adjust (across the population, on average) to new interventions Vary between 1 and 31 days
T117 17641-17660 Sentence denotes IC, intensive care.
T118 17662-17679 Sentence denotes Model calibration
T119 17680-18033 Sentence denotes Using the available data (table 1), we define ranges for all parameters in our model and sample all parameters simultaneously between these minimum and maximum values assuming uniform distributions using Latin hypercube sampling (statistical method for generating random parameters from multidimensional distribution) for a total of 100 000 simulations.
T120 18034-18220 Sentence denotes We used maximum likelihood estimation on total cumulative cases and cumulative deaths with a Poisson negative log likelihood calculated and summed over all observed and predicted points.
T121 18221-18447 Sentence denotes For i observed cases Xi (from data) and i predicted cases Yi (from simulations of the model), we select the best 100 parameter sets which maximise the log likelihood ∑Xilog⁡(Yi)−Yi from the total sample of 100 000 simulations.
T122 18448-18656 Sentence denotes The best 100 samples were taken as part of a bias–variance trade-off (online supplemental information, sensitivity analysis), and the qualitative inferences would not change with other choices of sample size.
T123 18657-18835 Sentence denotes For each data point (taken from cases and deaths), we calculate this log likelihood and weight each according to the square root of the mean of the respective case or death data.
T124 18836-18936 Sentence denotes This ensures that we are considering case and death data equally within our likelihood calculations.
T125 18938-18951 Sentence denotes Model outputs
T126 18952-19080 Sentence denotes For each of the 100 best parameter sets we run the model until 11 May 2020 and output the cumulative cases and deaths in the SW.
T127 19081-19195 Sentence denotes We output the predicted proportion of the population who are infectious and who have ever been infected over time.
T128 19196-19349 Sentence denotes Finally, we estimate the daily and cumulative patterns of admission to and discharge from hospital (IC and acute) and cumulative mortality from COVID-19.
T129 19350-19458 Sentence denotes We perform sensitivity analysis on the performance of the model when calibrated to subsets of the full data.
T130 19460-19479 Sentence denotes Results and outputs
T131 19480-19691 Sentence denotes From 100 000 simulated parameter sets, we selected the best 100 baseline model fits on the basis of agreement to the calibration data on daily confirmed COVID-19 cases and weekly mortality due to COVID-19 in SW.
T132 19692-19779 Sentence denotes The distribution of the best fitting values is shown in online supplemental figure S1A.
T133 19780-19847 Sentence denotes All results are shown with median and 95% credible intervals (CrI).
T134 19848-20080 Sentence denotes On 11 May 2020, the reported cumulative number of individuals with (laboratory confirmed) COVID-19 was 7116 in SW,14 and the most recent report on total cumulative deaths showed that 2306 had died from COVID-19 (as of 8 May 2020).25
T135 20082-20148 Sentence denotes Estimating the total proportion of individuals with COVID-19 in SW
T136 20149-20318 Sentence denotes Figure 2 shows the projected numbers of exposed, recovered and infectious (asymptomatic and symptomatic infections) until lockdown measures were lessened on 11 May 2020.
T137 20319-20473 Sentence denotes On this date, the model predicts that a total of 5793 (95% CrI 2003 to 12 051) were infectious (0.10% of the total SW population, 95% CrI 0.04% to 0.22%).
T138 20474-20740 Sentence denotes The model also predicts that a total of 189 048 (95% CrI 141 580 to 277 955) have had the virus but recovered (either asymptomatically or symptomatically), which is 3.4% (95% CrI 2.5% to 5.0%) of the SW population (not infectious and not susceptible to reinfection).
T139 20741-20955 Sentence denotes Figure 2 The predicted median size of the exposed (E), infectious (I) and recovered (R) classes, along with the size of asymptomatic and symptomatic individuals on each day in South West England until 11 May 2020.
T140 20956-21090 Sentence denotes Blue and red vertical lines represent the date the government introduced social distancing and school closures/lockdown, respectively.
T141 21092-21169 Sentence denotes Estimating the total hospitalised patients with COVID-19 in acute and IC beds
T142 21170-21409 Sentence denotes The total number of patients in acute (non-IC) hospital beds across SW was projected to be 701 (95% CrI 169 to 1543) and the total number of patients in IC hospital beds was projected to be 110 (95% CrI 8 to 464) on 11 May 2020 (figure 3).
T143 21410-21548 Sentence denotes Note that these ranges are quite large due to the uncertainty in the data and as more data become available these predictions will change.
T144 21549-21687 Sentence denotes Figure 3 The predicted number of hospitalised patients in acute and intensive care beds in the South West England (SW) until 11 May 2020.
T145 21688-21920 Sentence denotes The number of daily incoming patients diagnosed with COVID-19 is shown in orange (from SW daily case data14), 95% credible intervals are shown in light grey, 50% in dark grey and the median value of the fits is highlighted in black.
T146 21921-21991 Sentence denotes The shaded region indicates the prediction of the model from the data.
T147 21992-22126 Sentence denotes Blue and red vertical lines represent the date the government introduced social distancing and school closures/lockdown, respectively.
T148 22127-22146 Sentence denotes IC, intensive care.
T149 22148-22202 Sentence denotes Estimating the reproduction number under interventions
T150 22203-22338 Sentence denotes Figure 4 shows the model prediction for the reproduction (‘R’) number over time until 11 May 2020, when lockdown measures were relaxed.
T151 22339-22478 Sentence denotes All interventions (social distancing, school closures/lockdown) had a significant impact on the reproductive number for COVID-19 in the SW.
T152 22479-22644 Sentence denotes We predict that prior to any interventions R was 2.6 (95% CrI 2.0 to 3.2), and the introduction of social distancing reduced this number to 2.3 (95% CrI 1.8 to 2.9).
T153 22645-22800 Sentence denotes At the minimum, R was 0.6 (95% CrI 0.5 to 0.7) after all prior interventions were enacted and adhered to (social distancing, school closures and lockdown).
T154 22801-22894 Sentence denotes Figure 4 The effect of interventions on estimates of R (y-axis) over time until 11 May 2020.
T155 22895-23153 Sentence denotes Additional results for the fitting performance of the model (online supplemental figure S2A, B and table S2), the performance based on prior data (online supplemental figure S3A–D) and sensitivity analysis can be found in the online supplemental information.
T156 23155-23165 Sentence denotes Discussion
T157 23166-23398 Sentence denotes We have developed a deterministic ordinary differential equation model of the epidemic trajectory of COVID-19 focusing on acute and IC hospital bed capacity planning to support local NHS authorities, calibrating to SW-specific data.
T158 23399-23645 Sentence denotes The model is age structured and includes time-specific implementation of current interventions (following advice and enforcement of social distancing, school closures and lockdown) to predict the potential range of COVID-19 epidemic trajectories.
T159 23646-23953 Sentence denotes Using the publicly available data on cases and deaths, combined with the early estimates of parameters from early epidemics in other settings, we predict that on 11 May 2020 a total of 5793 (95% CrI 2003 to 12 051) were infectious, which equates to 0.10% (95% CrI 0.04% to 0.22%) of the total SW population.
T160 23954-24139 Sentence denotes In addition, we find that the model predicts a total of 189 048 (95% CrI 141 580 to 277 955) have had the virus but recovered, which is 3.4% (95% CrI 2.5% to 5.0%) of the SW population.
T161 24140-24452 Sentence denotes We also estimate that the total number of patients in acute hospital beds in SW on 11 May 2020 was 701 (95% CrI 169 to 1543) and in IC was 110 (95% CrI 8 to 464), while the R number has decreased from 2.6 (95% CrI 2.0 to 3.2) to 0.6 (95% CrI 0.5 to 0.7) after all interventions were enacted and fully adhered to.
T162 24453-24790 Sentence denotes The fits generally agree well with both the daily case data and the cumulative count of deaths in the SW, although the model overestimates the case data at early stages and underestimates later on (which can be seen in online supplemental figure S2A, and a scatter plot of expected vs observed outputs in online supplemental figure S2B).
T163 24791-24908 Sentence denotes This could be because we are using formal fitting methods or from the under-reporting of cases in the early epidemic.
T164 24909-25175 Sentence denotes When assessing model performance by projecting the numbers of cases and deaths forward from four dates in April, the model performs reasonably well, with more reliable predictions occurring when more data are used to fit the model (online supplemental figure S3A–D).
T165 25176-25495 Sentence denotes Even when using around half of the available data to generate forecasts (online supplemental figure S3D), the model performs reasonably well and captures the observed data later in May, but overestimates case numbers and underestimates deaths similar to those in the main analysis and in online supplemental figure S2A.
T166 25496-25661 Sentence denotes This suggests that our model could perform reasonably well at predicting COVID-19 outcomes but may still slightly overestimate case numbers and underestimate deaths.
T167 25662-26001 Sentence denotes The primary strength of this study is that we have developed generalisable and efficient modelling code incorporating disease transmission, interventions and hospital bed demand which can be adapted for use in other regional or national scenarios, with the model available on GitHub for open review and use (github.com/rdbooton/bricovmod).
T168 26002-26216 Sentence denotes We have worked closely with the NHS and at CCG level to ensure the model captures key clinical features of disease management in SW hospitals and provides output data in a format relevant to support local planning.
T169 26217-26398 Sentence denotes We combined local clinical expertise with detailed literature searches to ensure reasonable parameter ranges and assumptions in the presence of high levels of parameter uncertainty.
T170 26399-26645 Sentence denotes The main challenge of this work is in balancing the urgent need locally for prediction tools which are up to date (ie, not relying on the national trends to inform capacity planning) versus more exhaustive and robust methods for model comparison.
T171 26646-26957 Sentence denotes The latter of which uses existing models and more time-consuming (but more robust) data-fitting methods.26 27 However, we believe that release of this paper and sharing of model code will facilitate multidisciplinary collaboration and rapid review and support future model comparison and uncertainty analyses.27
T172 26958-27041 Sentence denotes As with all models of new infections there are significant parameter uncertainties.
T173 27042-27268 Sentence denotes Rapidly emerging literature is exploring a wide range of biological and epidemiological factors concerning COVID-19, but due to the worldwide nature of these studies, often parameter bands are wide and may be context specific.
T174 27269-27590 Sentence denotes For example, early estimates of the basic reproduction number ranged from 1.6 to 3.8 in different locations,28 29 with an early estimate of 2.4 used in UK model projections.8 In addition, the information which informs our parameter selection is rapidly evolving as new data are made available, sometimes on a daily basis.
T175 27591-28024 Sentence denotes From our initial analysis, we identified the following parameters as critical in determining the epidemic trajectory within our model—the percentage of infections which become symptomatic, the recovery time for cases which do not require hospital, the period between acute and IC occupancy, the length of stay in IC, the probability of transmission per contact and the gradual implementation of lockdown rather than immediate effect.
T176 28025-28238 Sentence denotes Other parameters (such as the percentage reduction in school-age contacts from school closures) did not seem to influence the dynamic trajectory as strongly—and thus we assume point estimates for these parameters.
T177 28239-28630 Sentence denotes However, for example, assuming that 95% of school-age contacts are reduced as a direct result of school closures is perhaps an overestimate, and future modelling work should address these uncertainties and their impacts on the epidemic trajectory of COVID-19 (but in this case, this value was somewhat arbitrary, and the assumption was used in the absence of school-age contact survey data).
T178 28631-28851 Sentence denotes In addition, we did not explicitly model the societal effect prior to governmental advice (social distancing, school closures, lockdown), instead assuming a fixed date, before which we assume there were no interventions.
T179 28852-29063 Sentence denotes This assumption may not be realistic and could have influenced the model output, but it is difficult to quantify the percentage compliance with interventions prior to the official release of governmental advice.
T180 29064-29189 Sentence denotes More research is urgently needed to refine these parameter ranges and to validate these biological parameters experimentally.
T181 29190-29269 Sentence denotes These estimates will improve the model as more empirical data become available.
T182 29270-29406 Sentence denotes We look forward to reducing the uncertainty in these parameters so that we can make better predictions and fit the data more accurately.
T183 29407-29594 Sentence denotes We have also assumed that there is no nosocomial transmission of infection between hospitalised cases and healthcare workers, as we do not have good data for within-hospital transmission.
T184 29595-29762 Sentence denotes However, front-line healthcare staff were likely to have been infected early on in the epidemic,30 which could have implications for our predicted epidemic trajectory.
T185 29763-29869 Sentence denotes Our model also assumes a closed system, which may not strictly be true due to continuing essential travel.
T186 29870-30056 Sentence denotes But given that up until 11 May, travel restrictions are very severe due to lockdown measures,5 any remaining inter-regional travel is likely to have minimal effects on our model outputs.
T187 30057-30490 Sentence denotes In addition, we assume that the transmission dynamics of asymptomatic individuals is equal to those of symptomatic individuals due to the viral load of asymptomatic and symptomatic carriers being comparable.31 However, this assumption should be further explored in future modelling studies due to the potential for asymptomatic carriers to engage in higher risk behaviour and potentially impact the transmission dynamics of COVID-19.
T188 30491-30971 Sentence denotes Similar to most other COVID-19 models, we use a variant on a susceptible-exposed-infectious-recovered structure.8–10 16 26 32 33 We do not spatially structure the population as in other UK modelling,9 10 but we do include age-specific mixing based on POLYMOD data22 and the postlockdown CoMix study.11 We also explicitly measure the total asymptomatic infection, and the total in each of the clinically relevant hospital classes (acute or IC), which is a strength of our approach.
T189 30972-31243 Sentence denotes Future models could also take into account local bed capacity within hospitals (including Nightingale centres) and accommodate the effect of demand outstripping supply leading to excess deaths, inclusive of non-hospital-based death such as is occurring within care homes.
T190 31244-31359 Sentence denotes Future models should also address the way in which we have compartmentalised the flow of hospitalisation and death.
T191 31360-31501 Sentence denotes From the symptomatic compartment, patients either recover or are admitted to hospital, from where they either die, recover or progress to IC.
T192 31502-31697 Sentence denotes Under our assumption, the symptomatic recovery rate is equal to the hospitalisation rate, and the time taken for acute patients to move to IC is equal to the time to discharge for acute patients.
T193 31698-31919 Sentence denotes These assumptions are a limitation of our model because in reality, those patients who progress to IC may have spent very little time in an acute bed (either due to rapid deterioration or presenting with severe symptoms).
T194 31920-32081 Sentence denotes Future studies should assess the effects of these assumptions and consider other such progressions and outcomes for a patient with COVID-19 through the hospital.
T195 32082-32188 Sentence denotes As with all modelling, we have not taken into account all possible sources of modelling mis-specification.
T196 32189-32303 Sentence denotes Some of these mis-specifications will tend to increase the predicted epidemic period, and others will decrease it.
T197 32304-32738 Sentence denotes One factor that could significantly change our predicted epidemic period is the underlying structure within the population leading to heterogeneity in the average number of contacts under lockdown, for example, key workers have high levels of contact but others are able to minimise contacts effectively, this might lead to an underestimate of ongoing transmission, but potentially an overestimate of the effect of releasing lockdown.
T198 32739-32891 Sentence denotes We also know that there are important socioeconomic considerations in determining people’s ability to stay at home and particularly to work from home.34
T199 32892-33615 Sentence denotes Early UK modelling predicted the infection peak to be reached roughly 3 weeks from the initiation of severe lockdown measures, as taken by the UK government in mid-March.8 A more recent study factoring spatial distribution of the population indicated the peak to follow in early April due to R0 reducing to below 1 in many settings in weeks following lockdown.9 Other modelling indicated that deaths in the UK would peak in mid-late April; furthermore, that the UK would not have enough acute and IC beds to meet demand.35 While modelling from the European Centre for Disease Prevention and Control estimated peak cases to occur in most European countries in mid-April,20 these estimations were largely at a national level.
T200 33616-33836 Sentence denotes Due to the expected lag of other regions behind London, these estimated peaks are likely to be shifted further into the future for the separate regions of the UK, and as shown by our model occurred in early to mid-April.
T201 33837-33926 Sentence denotes This is also likely to be true for future peaks which may result from relaxing lockdowns.
T202 33927-34106 Sentence denotes Outside of the UK, a similar modelling from France32 (which went into lockdown at a similar time the UK on 17 March) predicted the peak in daily IC admissions at the end of March.
T203 34107-34209 Sentence denotes Interestingly, however, when dissected by region, the peak in IC bed demand varied by roughly 2 weeks.
T204 34210-34602 Sentence denotes Swiss modelling similarly predicted a peak in hospitalisation and numbers of patients needing IC beds in early April, after lockdown implementation commenced on 17 March.33 US modelling36 disaggregated by State also highlights the peak of excess bed demand varies geographically, with this peak ranging from the second week of April through to May, dependent on the State under consideration.
T205 34603-34831 Sentence denotes The modelling based in France also cautioned that due to only 5.7% of the population having been infected by 11 May when the restrictions would be eased, the population would be vulnerable to a second epidemic peak thereafter.32
T206 34832-35091 Sentence denotes The ONS in England estimated that an average of 0.25% of the population had COVID-19 between 4 and 17 May 2020 (95% CI 0.16% to 0.38%),37 which is greater than the 0.10% (95% CrI 0.04% to 0.22%) we found with our model (on 11 May 2020), but with some overlap.
T207 35092-35673 Sentence denotes In addition, the ONS estimated that 6.78% (95% CrI 5.21% to 8.64%) tested positive for antibodies to COVID-19 up to 24 May 2020 in England,38 and Public Health England estimated that approximately 4% (2%–6%) tested positive for antibodies to COVID-19 between 20 and 26 April 2020 in the SW.39 Compared with our model, 3.4% (95% CrI 2.5% to 5.0%) had recovered on 11 May 2020 (2 weeks later), demonstrating that our model estimates may be within sensible bounds, and further highlighting the need for more regional estimates of crucial epidemiological parameters and seroprevalence.
T208 35674-35843 Sentence denotes We have assumed that individuals are not susceptible to reinfection within the model time frame; however, in future work it will be important to explore this assumption.
T209 35844-36043 Sentence denotes It is not known what the long-term pattern of immunity to COVID-19 will be,40 and this will be key to understanding the future epidemiology in the absence of a vaccine or effective treatment options.
T210 36044-36256 Sentence denotes With this in mind, our findings demonstrate that there are still significant data gaps—and in the absence of such data, mathematical models can provide a valuable asset for local and regional healthcare services.
T211 36257-36436 Sentence denotes This regional model will be used further in the SW as the pandemic evolves and could be used within other healthcare systems in other geographies to support localised predictions.
T212 36437-36704 Sentence denotes Controlling intervention measures at a more local level could be made possible through monitoring and assessment at the regional level through a combination of clinical expertise and local policy, guided by localised predictive forecasting as presented in this study.
T213 36706-36728 Sentence denotes Supplementary Material
T214 36729-36746 Sentence denotes Reviewer comments
T215 36747-36766 Sentence denotes Author's manuscript
T216 36768-36827 Sentence denotes Twitter: @rdbooton, @gushamilton, @leondanon, @katymeturner
T217 36828-36841 Sentence denotes Contributors:
T218 36842-36886 Sentence denotes Conception and design of the study: LD, EBP.
T219 36887-36927 Sentence denotes Acquisition of data: RDB, EBP, RW, KMET.
T220 36928-36980 Sentence denotes Mathematical modelling: RDB, LM, LD, EBP, RMW, KMET.
T221 36981-37009 Sentence denotes Coding and simulations: RDB.
T222 37010-37116 Sentence denotes Analysis and interpretation of results: RDB, LM, LV, KJL, CH, PDB, IH, RL, FH, DL, LD, AP, EBP, RMW, KMET.
T223 37117-37223 Sentence denotes Writing and drafting of the manuscript: RDB, LM, LV, KJL, CH, PDB, IH, RL, FH, DL, LD, AP, EBP, RMW, KMET.
T224 37224-37328 Sentence denotes Approval of the submitted manuscript: RDB, LM, LV, KJL, CH, PDB, IH, RL, FH, DL, LD, AP, EBP, RMW, KMET.
T225 37329-37337 Sentence denotes Funding:
T226 37338-37529 Sentence denotes 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:
T227 37530-37545 Sentence denotes 204813/Z/16/Z).
T228 37546-37660 Sentence denotes This work was also funded with support from Bristol UNCOVER (Bristol COVID Emergency Research, award number ISSF3:
T229 37661-37736 Sentence denotes 204813/Z/16/Z) and Medical Research Council UK (award number MR/S004769/1).
T230 37737-37922 Sentence denotes 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).
T231 37923-38433 Sentence denotes 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).
T232 38434-38454 Sentence denotes Competing interests:
T233 38455-38469 Sentence denotes None declared.
T234 38470-38502 Sentence denotes Patient consent for publication:
T235 38503-38516 Sentence denotes Not required.
T236 38517-38544 Sentence denotes Provenance and peer review:
T237 38545-38588 Sentence denotes Not commissioned; externally peer reviewed.
T238 38589-38617 Sentence denotes Data availability statement:
T239 38618-38673 Sentence denotes Data are available in a public, open access repository.
T240 38674-38774 Sentence denotes All data relevant to the study are included in the article or uploaded as supplementary information.
T241 38775-38882 Sentence denotes All model code are open source and available for download on GitHub: https://github.com/rdbooton/bricovmod.
T242 38883-38955 Sentence denotes All data are freely available via the GOV.UK COVID-19 dashboard and ONS.
T243 38956-38978 Sentence denotes Supplemental material:
T244 38979-39027 Sentence denotes This content has been supplied by the author(s).
T245 39028-39125 Sentence denotes It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed.
T246 39126-39230 Sentence denotes Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ.
T247 39231-39326 Sentence denotes BMJ disclaims all liability and responsibility arising from any reliance placed on the content.
T248 39327-39669 Sentence denotes Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.