Literature DB >> 33782427

Analysis of temporal trends in potential COVID-19 cases reported through NHS Pathways England.

Quentin J Leclerc1,2, Emily S Nightingale3,4, Sam Abbott1,2, Thibaut Jombart1,2,5,6.   

Abstract

The National Health Service (NHS) Pathways triage system collates data on enquiries to 111 and 999 services in England. Since the 18th of March 2020, these data have been made publically available for potential COVID-19 symptoms self-reported by members of the public. Trends in such reports over time are likely to reflect behaviour of the ongoing epidemic within the wider community, potentially capturing valuable information across a broader severity profile of cases than hospital admission data. We present a fully reproducible analysis of temporal trends in NHS Pathways reports until 14th May 2020, nationally and regionally, and demonstrate that rates of growth/decline and effective reproduction number estimated from these data may be useful in monitoring transmission. This is a particularly pressing issue as lockdown restrictions begin to be lifted and evidence of disease resurgence must be constantly reassessed. We further assess the correlation between NHS Pathways reports and a publicly available NHS dataset of COVID-19-associated deaths in England, finding that enquiries to 111/999 were strongly associated with daily deaths reported 16 days later. Our results highlight the potential of NHS Pathways as the basis of an early warning system. However, this dataset relies on self-reported symptoms, which are at risk of being severely biased. Further detailed work is therefore necessary to investigate potential behavioural issues which might otherwise explain our conclusions.

Entities:  

Mesh:

Year:  2021        PMID: 33782427      PMCID: PMC8007605          DOI: 10.1038/s41598-021-86266-3

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  4 in total

1.  How generation intervals shape the relationship between growth rates and reproductive numbers.

Authors:  J Wallinga; M Lipsitch
Journal:  Proc Biol Sci       Date:  2007-02-22       Impact factor: 5.349

2.  Serial interval of novel coronavirus (COVID-19) infections.

Authors:  Hiroshi Nishiura; Natalie M Linton; Andrei R Akhmetzhanov
Journal:  Int J Infect Dis       Date:  2020-03-04       Impact factor: 3.623

3.  COVID-19 length of hospital stay: a systematic review and data synthesis.

Authors:  Eleanor M Rees; Emily S Nightingale; Yalda Jafari; Naomi R Waterlow; Samuel Clifford; Carl A B Pearson; Cmmid Working Group; Thibaut Jombart; Simon R Procter; Gwenan M Knight
Journal:  BMC Med       Date:  2020-09-03       Impact factor: 8.775

4.  Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020.

Authors:  Kenji Mizumoto; Katsushi Kagaya; Alexander Zarebski; Gerardo Chowell
Journal:  Euro Surveill       Date:  2020-03
  4 in total
  3 in total

1.  Refining epidemiological forecasts with simple scoring rules.

Authors:  Robert E Moore; Conor Rosato; Simon Maskell
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2022-08-15       Impact factor: 4.019

2.  Real-time monitoring of COVID-19 dynamics using automated trend fitting and anomaly detection.

Authors:  Thibaut Jombart; Stéphane Ghozzi; Dirk Schumacher; Timothy J Taylor; Quentin J Leclerc; Mark Jit; Stefan Flasche; Felix Greaves; Tom Ward; Rosalind M Eggo; Emily Nightingale; Sophie Meakin; Oliver J Brady; Graham F Medley; Michael Höhle; W John Edmunds
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2021-05-31       Impact factor: 6.237

3.  Estimates of regional infectivity of COVID-19 in the United Kingdom following imposition of social distancing measures.

Authors:  Robert Challen; Krasimira Tsaneva-Atanasova; Martin Pitt; Tom Edwards; Luke Gompels; Lucas Lacasa; Ellen Brooks-Pollock; Leon Danon
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2021-05-31       Impact factor: 6.237

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.