| Id |
Subject |
Object |
Predicate |
Lexical cue |
| T27 |
0-12 |
Sentence |
denotes |
Introduction |
| T28 |
13-733 |
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 |
734-1649 |
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 |
1650-2127 |
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 |
2128-2322 |
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 |
2323-2528 |
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 |
2529-3160 |
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 |
3161-3483 |
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 |
3484-3742 |
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 |
3743-4098 |
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 |
4099-4319 |
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 |
4320-4579 |
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 |
4580-4896 |
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 |
4897-4968 |
Sentence |
denotes |
We present the model trajectories for SW using publicly available data. |
