PMC:7797241 / 3461-4854 JSON TXT

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Id	Subject	Object	Predicate	Lexical cue	tao:has_database_id
54	66-74	Disease	denotes	COVID-19	MESH:C000657245
55	373-381	Disease	denotes	COVID-19	MESH:C000657245
56	382-392	Disease	denotes	infections	MESH:D007239
57	471-479	Disease	denotes	COVID-19	MESH:C000657245
58	719-727	Disease	denotes	COVID-19	MESH:C000657245
59	893-901	Disease	denotes	COVID-19	MESH:C000657245
60	1217-1225	Disease	denotes	COVID-19	MESH:C000657245
61	1333-1341	Disease	denotes	COVID-19	MESH:C000657245

Id	Subject	Object	Predicate	Lexical cue
T29	0-915	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	916-1393	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).