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van Leeuwen E, Panovska-Griffiths J, Elgohari S, Charlett A, Watson C. The interplay between susceptibility and vaccine effectiveness control the timing and size of an emerging seasonal influenza wave in England. Epidemics 2023; 44:100709. [PMID: 37579587 DOI: 10.1016/j.epidem.2023.100709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 06/12/2023] [Accepted: 07/17/2023] [Indexed: 08/16/2023] Open
Abstract
Relaxing social distancing measures and reduced level of influenza over the last two seasons may lead to a winter 2022 influenza wave in England. We used an established model for influenza transmission and vaccination to evaluate the rolled out influenza immunisation programme over October to December 2022. Specifically, we explored how the interplay between pre-season population susceptibility and influenza vaccine efficacy control the timing and the size of a possible winter influenza wave. Our findings suggest that susceptibility affects the timing and the height of a potential influenza wave, with higher susceptibility leading to an earlier and larger influenza wave while vaccine efficacy controls the size of the peak of the influenza wave. With pre-season susceptibility higher than pre-COVID-19 levels, under the planned vaccine programme an early influenza epidemic wave is possible, its size dependent on vaccine effectiveness against the circulating strain. If pre-season susceptibility is low and similar to pre-COVID levels, the planned influenza vaccine programme with an effective vaccine could largely suppress a winter 2022 influenza outbreak in England.
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Affiliation(s)
- E van Leeuwen
- UK Health Security Agency, Colindale, United Kingdom.
| | - J Panovska-Griffiths
- UK Health Security Agency, Colindale, United Kingdom; The Pandemic Sciences Institute, University of Oxford, Oxford, United Kingdom; The Queen's College, University of Oxford, Oxford, United Kingdom.
| | - S Elgohari
- UK Health Security Agency, Colindale, United Kingdom
| | - A Charlett
- UK Health Security Agency, Colindale, United Kingdom
| | - C Watson
- UK Health Security Agency, Colindale, United Kingdom
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2
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Jackson CH, Tom BD, Kirwan PD, Mandal S, Seaman SR, Kunzmann K, Presanis AM, De Angelis D. A comparison of two frameworks for multi-state modelling, applied to outcomes after hospital admissions with COVID-19. Stat Methods Med Res 2022; 31:1656-1674. [PMID: 35837731 PMCID: PMC9294033 DOI: 10.1177/09622802221106720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
We compare two multi-state modelling frameworks that can be used to represent dates of events following hospital admission for people infected during an epidemic. The methods are applied to data from people admitted to hospital with COVID-19, to estimate the probability of admission to intensive care unit, the probability of death in hospital for patients before and after intensive care unit admission, the lengths of stay in hospital, and how all these vary with age and gender. One modelling framework is based on defining transition-specific hazard functions for competing risks. A less commonly used framework defines partially-latent subpopulations who will experience each subsequent event, and uses a mixture model to estimate the probability that an individual will experience each event, and the distribution of the time to the event given that it occurs. We compare the advantages and disadvantages of these two frameworks, in the context of the COVID-19 example. The issues include the interpretation of the model parameters, the computational efficiency of estimating the quantities of interest, implementation in software and assessing goodness of fit. In the example, we find that some groups appear to be at very low risk of some events, in particular intensive care unit admission, and these are best represented by using 'cure-rate' models to define transition-specific hazards. We provide general-purpose software to implement all the models we describe in the flexsurv R package, which allows arbitrarily flexible distributions to be used to represent the cause-specific hazards or times to events.
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Affiliation(s)
| | - Brian Dm Tom
- 47959MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Peter D Kirwan
- 47959MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Public Health England, London, UK
| | | | - Shaun R Seaman
- 47959MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Kevin Kunzmann
- 47959MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Anne M Presanis
- 47959MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Daniela De Angelis
- 47959MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Public Health England, London, UK
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3
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Wood RM, Pratt AC, Murch BJ, Powell AL, Booton RD, Thomas DG, Twigger J, Diakou E, Coleborn S, Manning T, Davies C, Turner KM. Establishing an SEIR-based framework for local modelling of COVID-19 infections, hospitalisations and deaths. Health Syst (Basingstoke) 2021; 10:337-347. [PMID: 34745593 PMCID: PMC8567954 DOI: 10.1080/20476965.2021.1973348] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 08/19/2021] [Indexed: 12/13/2022] Open
Abstract
Without timely assessments of the number of COVID-19 cases requiring hospitalisation, healthcare providers will struggle to ensure an appropriate number of beds are made available. Too few could cause excess deaths while too many could result in additional waits for elective treatment. As well as supporting capacity considerations, reliably projecting future "waves" is important to inform the nature, timing and magnitude of any localised restrictions to reduce transmission. In making the case for locally owned and locally configurable models, this paper details the approach taken by one major healthcare system in founding a multi-disciplinary "Scenario Review Working Group", comprising commissioners, public health officials and academic epidemiologists. The role of this group, which met weekly during the pandemic, was to define and maintain an evolving library of plausible scenarios to underpin projections obtained through an SEIR-based compartmental model. Outputs have informed decision-making at the system's major incident Bronze, Silver and Gold Commands. This paper presents illustrated examples of use and offers practical considerations for other healthcare systems that may benefit from such a framework.
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Affiliation(s)
- R. M. Wood
- Bristol, North Somerset and South Gloucestershire CCG, National Health Service, Bristol, UK
- School of Management, University of Bath, Bath, UK
| | - A. C. Pratt
- Bristol, North Somerset and South Gloucestershire CCG, National Health Service, Bristol, UK
| | - B. J. Murch
- Bristol, North Somerset and South Gloucestershire CCG, National Health Service, Bristol, UK
| | - A. L. Powell
- Bristol, North Somerset and South Gloucestershire CCG, National Health Service, Bristol, UK
| | - R. D. Booton
- Bristol Medical School, University of Bristol, Bristol, UK
| | - D. G. Thomas
- Public Health, Bristol City Council, Bristol, UK
| | - J. Twigger
- Public Health, Bristol City Council, Bristol, UK
| | - E. Diakou
- Business Intelligence, North Somerset Council, Weston-Super-Mare, UK
| | - S. Coleborn
- Public Health, South Gloucestershire Council, Yate, UK
| | - T. Manning
- Bristol, North Somerset and South Gloucestershire CCG, National Health Service, Bristol, UK
| | - C. Davies
- Bristol, North Somerset and South Gloucestershire CCG, National Health Service, Bristol, UK
| | - K. M. Turner
- Bristol Medical School, University of Bristol, Bristol, UK
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4
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Barbieri D, Giuliani E, Del Prete A, Losi A, Villani M, Barbieri A. How Artificial Intelligence and New Technologies Can Help the Management of the COVID-19 Pandemic. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18147648. [PMID: 34300099 PMCID: PMC8303245 DOI: 10.3390/ijerph18147648] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 07/07/2021] [Accepted: 07/16/2021] [Indexed: 12/19/2022]
Abstract
The COVID-19 pandemic has worked as a catalyst, pushing governments, private companies, and healthcare facilities to design, develop, and adopt innovative solutions to control it, as is often the case when people are driven by necessity. After 18 months since the first case, it is time to think about the pros and cons of such technologies, including artificial intelligence—which is probably the most complex and misunderstood by non-specialists—in order to get the most out of them, and to suggest future improvements and proper adoption. The aim of this narrative review was to select the relevant papers that directly address the adoption of artificial intelligence and new technologies in the management of pandemics and communicable diseases such as SARS-CoV-2: environmental measures; acquisition and sharing of knowledge in the general population and among clinicians; development and management of drugs and vaccines; remote psychological support of patients; remote monitoring, diagnosis, and follow-up; and maximization and rationalization of human and material resources in the hospital environment.
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Affiliation(s)
- Davide Barbieri
- Department of Neuroscience and Rehabilitation, University of Ferrara, Via Savonarola 9, 44121 Ferrara, Italy;
| | - Enrico Giuliani
- Department of Biomedical, Metabolic and Neuroscience Sciences, University of Modena and Reggio Emilia, Via Del Pozzo 71, 41125 Modena, Italy;
| | - Anna Del Prete
- School of Anesthesiology and Intensive Care, University of Modena and Reggio Emilia, Via Del Pozzo 71, 41125 Modena, Italy; (A.D.P.); (A.B.)
| | - Amanda Losi
- Department of Biomedical, Metabolic and Neuroscience Sciences, University of Modena and Reggio Emilia, Via Del Pozzo 71, 41125 Modena, Italy;
- Correspondence: ; Tel.: +39-0598721234 (ext. 41125)
| | - Matteo Villani
- Department of Anesthesiology and Intensive Care, Azienda USL Piacenza, Via Antonio Anguissola 15, 29121 Piacenza, Italy;
| | - Alberto Barbieri
- School of Anesthesiology and Intensive Care, University of Modena and Reggio Emilia, Via Del Pozzo 71, 41125 Modena, Italy; (A.D.P.); (A.B.)
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5
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Zhang XS, Vynnycky E, Charlett A, De Angelis D, Chen Z, Liu W. Transmission dynamics and control measures of COVID-19 outbreak in China: a modelling study. Sci Rep 2021; 11:2652. [PMID: 33514781 PMCID: PMC7846591 DOI: 10.1038/s41598-021-81985-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 01/06/2021] [Indexed: 01/12/2023] Open
Abstract
COVID-19 is reported to have been brought under control in China. To understand the COVID-19 outbreak in China and provide potential lessons for other parts of the world, in this study we apply a mathematical model with multiple datasets to estimate the transmissibility of the SARS-CoV-2 virus and the severity of the illness associated with the infection, and how both were affected by unprecedented control measures. Our analyses show that before 19th January 2020, 3.5% (95% CI 1.7-8.3%) of infected people were detected; this percentage increased to 36.6% (95% CI 26.1-55.4%) thereafter. The basic reproduction number (R0) was 2.33 (95% CI 1.96-3.69) before 8th February 2020; then the effective reproduction number dropped to 0.04(95% CI 0.01-0.10). This estimation also indicates that control measures taken since 23rd January 2020 affected the transmissibility about 2 weeks after they were introduced. The confirmed case fatality rate is estimated at 9.6% (95% CI 8.1-11.4%) before 15 February 2020, and then it reduced to 0.7% (95% CI 0.4-1.0%). This shows that SARS-CoV-2 virus is highly transmissible but may be less severe than SARS-CoV-1 and MERS-CoV. We found that at the early stage, the majority of R0 comes from undetected infectious people. This implies that successful control in China was achieved through reducing the contact rates among people in the general population and increasing the rate of detection and quarantine of the infectious cases.
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Affiliation(s)
- Xu-Sheng Zhang
- Centre for Infectious Disease Surveillance and Control, National Infection Service, Public Health England, 61 Colindale Avenue, London, NW9 5EQ, UK.
- Medical Research Council Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College Faculty of Medicine, Norfolk Place, London, W2 1PG, UK.
| | - Emilia Vynnycky
- Centre for Infectious Disease Surveillance and Control, National Infection Service, Public Health England, 61 Colindale Avenue, London, NW9 5EQ, UK
- TB Modelling Group, TB Centre, Centre for Mathematical Modelling of Infectious Diseases and Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Andre Charlett
- Centre for Infectious Disease Surveillance and Control, National Infection Service, Public Health England, 61 Colindale Avenue, London, NW9 5EQ, UK
| | - Daniela De Angelis
- Centre for Infectious Disease Surveillance and Control, National Infection Service, Public Health England, 61 Colindale Avenue, London, NW9 5EQ, UK
- Medical Research Council Biostatistics Unit, University Forvie Site, Robinson Way, Cambridge, CB2 0SR, UK
| | - Zhengji Chen
- School of Public Health, Kunming Medical University, Kunming, Yunnan, People's Republic of China
| | - Wei Liu
- School of Public Health, Kunming Medical University, Kunming, Yunnan, People's Republic of China.
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Simpson CR, Thomas BD, Challen K, De Angelis D, Fragaszy E, Goodacre S, Hayward A, Lim WS, Rubin GJ, Semple MG, Knight M. The UK hibernated pandemic influenza research portfolio: triggered for COVID-19. THE LANCET. INFECTIOUS DISEASES 2020; 20:767-769. [PMID: 32422199 PMCID: PMC7228695 DOI: 10.1016/s1473-3099(20)30398-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 04/30/2020] [Indexed: 01/19/2023]
Affiliation(s)
- Colin R Simpson
- School of Health, Wellington Faculty of Health, Victoria University of Wellington, Wellington, New Zealand; Usher Institute, The University of Edinburgh, Edinburgh, EH89AG, UK.
| | - Benjamin D Thomas
- Clinical Trials Research Unit, School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Kirsty Challen
- Centre for Urgent and Emergency Care Research, School of Health and Related Research, University of Sheffield, Sheffield, UK
| | | | - Ellen Fragaszy
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Steve Goodacre
- Centre for Urgent and Emergency Care Research, School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Andrew Hayward
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK; Institute of Epidemiology and Health Care, University College London, London, UK
| | - Wei Shen Lim
- Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - G James Rubin
- Department of Psychological Medicine, Weston Education Centre, King's College London, London, UK
| | - Malcolm G Semple
- Health Protection Research Unit in Emerging and Zoonotic Infections, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK
| | - Marian Knight
- National Perinatal Epidemiology Unit, University of Oxford, Oxford, UK
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