Estimating severity from multiple data sources using Bayesian evidence synthesis
Lead Research Organisation:
University of Cambridge
Department Name: UNLISTED
Abstract
As we prepare for a possible COVID-19 pandemic, understanding the severity of the
epidemic, i.e. the proportion of infections that result in a severe event such as
hospitalisation or death, is crucial to monitoring and predicting the burden of the epidemic
on healthcare services. This burden is measured by the number of people infected who
require primary care from general practitioners, hospital admission, respiratory support
and/or admission to intensive care. Severity is quantified by infection-severity and caseseverity
risks, namely the probability that an infection (whether with or without symptoms)
or a symptomatic infection (clinical case) leads to a severe event. These quantities are
challenging to observe directly from a single dataset, as it is not possible to detect and
follow-up every case in a population, particularly early in the epidemic when case counts
may miss many asymptomatic or mild cases. The problem is compounded by the fact that
for patients still ill in hospital, we have not yet had time to observe whether they will
recover or not. We propose combining information from multiple datasets, both on
individuals and aggregated counts, to estimate severity while accounting for the
challenges of missing cases and not yet observing outcomes.
epidemic, i.e. the proportion of infections that result in a severe event such as
hospitalisation or death, is crucial to monitoring and predicting the burden of the epidemic
on healthcare services. This burden is measured by the number of people infected who
require primary care from general practitioners, hospital admission, respiratory support
and/or admission to intensive care. Severity is quantified by infection-severity and caseseverity
risks, namely the probability that an infection (whether with or without symptoms)
or a symptomatic infection (clinical case) leads to a severe event. These quantities are
challenging to observe directly from a single dataset, as it is not possible to detect and
follow-up every case in a population, particularly early in the epidemic when case counts
may miss many asymptomatic or mild cases. The problem is compounded by the fact that
for patients still ill in hospital, we have not yet had time to observe whether they will
recover or not. We propose combining information from multiple datasets, both on
individuals and aggregated counts, to estimate severity while accounting for the
challenges of missing cases and not yet observing outcomes.
Technical Summary
This COVID-19 Rapid Response award is jointly funded (50:50) between the Medical Research Council and the National Institute for Health Research. The figure displayed is the total award amount of the two funders combined, with each partner contributing equally towards the project.
In preparing for a possible COVID-19 pandemic, estimates of severity, in particular of both
the numbers of infections occurring at different levels of severity and the infection- and
case-severity risks, are crucial to understand and predict the burden and impact of the
epidemic on healthcare services. Such estimates are most importantly needed by age and
risk group (e.g. defined by co-morbidities) strata, although in the early stages of an
epidemic, strata-specific information is rarely available. No single dataset can provide
enough information on its own to estimate severity, but estimation is feasible by
synthesising multiple datasets, such as: line-listing data from first few hundred type
studies; surveillance data including case counts, numbers accessing healthcare, and
numbers of deaths; cohort studies; and household studies. Such a synthesis needs to
account for biases inherent in each data source, including differential ascertainment by
severity level; and to account for the incomplete nature of the data, which, collected in real
time, are typically affected by censoring of final outcomes (recovery/hospital discharge or
death). We propose to make the best use of both individual- and aggregate-level data that
will become available, by using a combination of survival analysis techniques (e.g. curerate
mixture or competing risks models) and Bayesian evidence synthesis in a single
analysis to estimate severity in real time, as data accumulate over the course of the
epidemic, and once the epidemic is over.
In preparing for a possible COVID-19 pandemic, estimates of severity, in particular of both
the numbers of infections occurring at different levels of severity and the infection- and
case-severity risks, are crucial to understand and predict the burden and impact of the
epidemic on healthcare services. Such estimates are most importantly needed by age and
risk group (e.g. defined by co-morbidities) strata, although in the early stages of an
epidemic, strata-specific information is rarely available. No single dataset can provide
enough information on its own to estimate severity, but estimation is feasible by
synthesising multiple datasets, such as: line-listing data from first few hundred type
studies; surveillance data including case counts, numbers accessing healthcare, and
numbers of deaths; cohort studies; and household studies. Such a synthesis needs to
account for biases inherent in each data source, including differential ascertainment by
severity level; and to account for the incomplete nature of the data, which, collected in real
time, are typically affected by censoring of final outcomes (recovery/hospital discharge or
death). We propose to make the best use of both individual- and aggregate-level data that
will become available, by using a combination of survival analysis techniques (e.g. curerate
mixture or competing risks models) and Bayesian evidence synthesis in a single
analysis to estimate severity in real time, as data accumulate over the course of the
epidemic, and once the epidemic is over.
Organisations
- University of Cambridge (Lead Research Organisation)
- HARVARD UNIVERSITY (Collaboration)
- United Kingdom Research and Innovation (Collaboration)
- PUBLIC HEALTH ENGLAND (Collaboration)
- University of Hong Kong (Collaboration)
- National Institute for Health Research (Collaboration)
- IMPERIAL COLLEGE LONDON (Collaboration)
People |
ORCID iD |
Anne Presanis (Principal Investigator) |
Publications
Stimson J
(2022)
Estimation of the impact of hospital-onset SARS-CoV-2 infections on length of stay in English hospitals using causal inference.
in BMC infectious diseases
Twohig KA
(2022)
Hospital admission and emergency care attendance risk for SARS-CoV-2 delta (B.1.617.2) compared with alpha (B.1.1.7) variants of concern: a cohort study.
in The Lancet. Infectious diseases
Webster H
(2022)
Hospitalisation and mortality risk of SARS-COV-2 variant omicron sub-lineage BA.2 compared to BA.1 in England
in Nature Communications
Nyberg, T.
(2021)
Hospitalisation risk for COVID-19 patients infected with SARS-CoV-2 variant B.1.1.7: Cohort analysis
in arXiv
Nyberg T
(2022)
Hospitalization and Mortality Risk for COVID-19 Cases With SARS-CoV-2 AY.4.2 (VUI-21OCT-01) Compared to Non-AY.4.2 Delta Variant Sublineages
in The Journal of Infectious Diseases
Nyberg T
(2022)
Misclassification bias in estimating clinical severity of SARS-CoV-2 variants - Authors' reply.
in Lancet (London, England)
Description | National and International government advisory groups |
Geographic Reach | Europe |
Policy Influence Type | Contribution to a national consultation/review |
Impact | Severity estimates for COVID-19, including probability of ICU admission given hospital admission, probabilities of death given hospital admission (with and without ICU admission), probabilities of discharge given hospital admission, lengths of stay in hospital and ICU, and relative severity (risks of hospitalisation and death) by variant, have regularly been provided to Public Health England's (PHE)/UKHSA Joint Modelling Team, Variant Technical Group and to the SPI-MO and SAGE advisory groups, providing evidence to inform public health decisions. |
URL | https://www.gov.uk/government/publications/investigation-of-sars-cov-2-variants-technical-briefings |
Description | Regione Lombardia Welfare Directorate |
Geographic Reach | Local/Municipal/Regional |
Policy Influence Type | Implementation circular/rapid advice/letter to e.g. Ministry of Health |
Impact | A technical report describing estimates of COVID-19 severe burden in the Italian region of Lombardy (risks of competing outcomes in hospital and the community, including hospital admission, discharge/recovery, ICU admission and death, lengths of stay in hospital and ICU, times to recovery in the community) was provided to public health officials at the Welfare Directorate of Regione Lombardia, to inform their resource planning for current and future waves of COVID-19. |
Description | Agreement for Performance of Work - Emergency - COVID-19 severity assessment in WHO Europe member states, evidence synthesis |
Amount | $17,595 (USD) |
Funding ID | 2022/1201685-0 |
Organisation | World Health Organization Regional Office for Europe |
Sector | Public |
Country | Denmark |
Start | 01/2022 |
End | 04/2022 |
Title | Estimation of relative risks of hospital admission and death by COVID-19 variant |
Description | Survival modelling (stratified Cox proportional hazards) to estimate the relative risks of severe events among COVID-19 cases by variant, e.g. Alpha vs pandemic original strain; Delta vs Alpha; Delta AY4.2 vs other Delta; Omicron BA.1 vs Delta. Model and code for model shared with UKHSA collaborators as knowledge transfer. Protocol and reuseable code for the model being drafted for sharing with WHO Europe member states to carry out similar analyses. Update March 2023: Protocol and preprint for standardised analyses across WHO Europe member states available here: https://arxiv.org/abs/2303.05541 and the accompanying code is publicy available here: https://github.com/TommyNyberg/variant_severity |
Type Of Material | Data analysis technique |
Year Produced | 2021 |
Provided To Others? | Yes |
Impact | Presentation of resulting estimates to national and international government advisory groups. Four papers published or in press. Update March 2023: Standardised protocol, models and code used by several WHO Europe member states to analyse relative severity in their countries; and estimates pooled across Europe, as reported in the preprint linked above. |
URL | https://arxiv.org/abs/2303.05541 |
Description | Cambridge / Imperial / UKHSA collaboration |
Organisation | Imperial College London |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Statistical expertise; coding; paper writing |
Collaborator Contribution | Data provision; epidemiological expertise; coding |
Impact | Paper on relative severity of Omicron variant compared to Delta, in press in The Lancet (publication date 17th March 2022). |
Start Year | 2021 |
Description | Cambridge / Imperial / UKHSA collaboration |
Organisation | National Institute for Health Research |
Department | Health Protection Research Unit for Modelling Methodology |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Statistical expertise; coding; paper writing |
Collaborator Contribution | Data provision; epidemiological expertise; coding |
Impact | Paper on relative severity of Omicron variant compared to Delta, in press in The Lancet (publication date 17th March 2022). |
Start Year | 2021 |
Description | Cambridge / Imperial / UKHSA collaboration |
Organisation | Public Health England |
Department | Centre of Infectious Disease Surveillance and Control |
Country | United Kingdom |
Sector | Public |
PI Contribution | Statistical expertise; coding; paper writing |
Collaborator Contribution | Data provision; epidemiological expertise; coding |
Impact | Paper on relative severity of Omicron variant compared to Delta, in press in The Lancet (publication date 17th March 2022). |
Start Year | 2021 |
Description | Harvard/Hong Kong collaboration |
Organisation | Harvard University |
Department | Harvard T.H. Chan School of Public Health |
Country | United States |
Sector | Academic/University |
PI Contribution | Statistical expertise and analysis, particularly in Bayesian evidence synthesis for severity estimation |
Collaborator Contribution | Mathematical modelling and epidemiological expertise, particularly in infectious disease severity |
Impact | Prior to this grant, this multi-disciplinary collaboration (statistics, mathematical modelling, infectious disease epidemiology) resulted in several publications on influenza severity. Both partners are co-investigators on this grant. |
Start Year | 2009 |
Description | Harvard/Hong Kong collaboration |
Organisation | University of Hong Kong |
Country | Hong Kong |
Sector | Academic/University |
PI Contribution | Statistical expertise and analysis, particularly in Bayesian evidence synthesis for severity estimation |
Collaborator Contribution | Mathematical modelling and epidemiological expertise, particularly in infectious disease severity |
Impact | Prior to this grant, this multi-disciplinary collaboration (statistics, mathematical modelling, infectious disease epidemiology) resulted in several publications on influenza severity. Both partners are co-investigators on this grant. |
Start Year | 2009 |
Description | JUNIPER (Joint UNIversities Pandemic and Epidemiological Research) Consortium |
Organisation | United Kingdom Research and Innovation |
Country | United Kingdom |
Sector | Public |
PI Contribution | Contributed statistical input; estimates of severity. |
Collaborator Contribution | The JUNIPER Consortium, funded by UKRI-MRC (MR/V038613/1) consists of teams and research groups (Bristol, Cambridge, Exeter, Lancaster, Manchester, Oxford and Warwick) involved in the current effort to generate forecasts of the pandemic to inform the SPI-M and SAGE advisory groups, and the funding will support these teams in their continued forecasting and prediction roles. The consortium aims to build national capacity, train the next generation of epidemic modellers, and develop their modelling capacity. Work has until now focussed on hotspot detection and schools/universities. |
Impact | A paper on epidemic phase bias has been re-submitted following revision for Statistical Methods in Medical Research. |
Start Year | 2021 |
Description | PHE/UKHSA COVID-19 collaboration |
Organisation | Public Health England |
Country | United Kingdom |
Sector | Public |
PI Contribution | Statistical expertise & analysis, as a member of the PHE/UKHSA Joint Modelling Team, one of their cells in the national response to the COVID-19 pandemic. |
Collaborator Contribution | Provision of data, statistical and mathematical modelling expertise, one senior member of the PHE/UKHSA Joint Modelling Team is a co-Investigator on this grant, epidemiological and public health surveillance expertise via the PHE/UKHSA Epidemiology and Surveillance Cells of the COVID-19 response. |
Impact | Three papers have resulted from this multi-disciplinary collaboration, two are under revision for re-submission to Statistical Methods in Medical Research, one has been submitted to the BMJ. Crucially, results from this collaboration on the severity of COVID-19 have been regularly discussed at the PHE Joint Modelling Team meetings, and sent to the governernment advisory groups SPI-M (Scientific Pandemic Influenza Modelling) and SAGE (Scientific Advisory Group for Emergencies) as evidence informing the government response. Several co-Investigators on this grant are members of SPI-M. 2022 update: several further papers have resulted assessing the relative severity of different COVID-19 variants, presented regularly to the UKHSA Variant Technical Group, reported in UKHSA Variant Technical Briefings, and sent to SPI-M. |
Start Year | 2020 |
Title | R package for flexible survival and multi-state modelling: flexsurv |
Description | Software developed for flexible survival and multi-state modelling by my colleague at MRC Biostatistics Unit, University of Cambridge, Christopher Jackson. It was developed prior to this award, but was signifcantly expanded during this award, motivated by the problem of estimating COVID-19 severity, among hospitalised cases. |
Type Of Technology | Software |
Year Produced | 2021 |
Open Source License? | Yes |
Impact | Two papers published (Grosso et al 2021, Presanis et al 2021) and two in press (Jackson et al 2022a, 2022b) using the software package to estimate risks of competing outcomes among hospitalised COVID-19 cases (ICU admission, death, discharge) and lengths of stay in hospital. |
URL | https://cran.r-project.org/package=flexsurv |
Title | pkirwan/COVID-hospital-outcomes: R code for manuscript |
Description | Trends in COVID-19 hospital outcomes in England before and after vaccine introduction, a cohort study |
Type Of Technology | Software |
Year Produced | 2022 |
Open Source License? | Yes |
Impact | This code accompanies a paper on estimating hospital-fatality risks and lengths of stay among COVID-19 patients. Initial results were presented to the Scientific Advisory Group for Emergencies in December 2021: https://www.gov.uk/government/publications/mrc-biostatistics-unit-and-phe-estimates-of-covid-19-hospitalised-mortality-and-length-of-stay-data-from-march-2020-to-september-2021-7-december-20 |
URL | https://zenodo.org/record/6856530 |
Description | Invited speaker - WHO Europe and ECDC joint surveillance meeting (respiratory infections) |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Presentation of our research on estimating COVID-19 severity by variant to the member states of WHO Europe and ECDC. Let to emergency funding for us to work with these member states to support them in producing similar analyses, both by writing a protocol for the analysis and sharing code/methods/support, and to synthesise the resulting estimates across the involved countries to produce more precise estimates than from one country alone. |
Year(s) Of Engagement Activity | 2021 |
Description | Presentation on COVID-19 severity estimation to CMStatistics Conference |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Presented our work of the last 18 months of the pandemic on estimating its severity to other statistical and epidemiological colleagues, prompting questions and discussion. |
Year(s) Of Engagement Activity | 2021 |
URL | http://www.cmstatistics.org/CMStatistics2021/programme.php |
Description | Presentation on trends in COVID-19 hospital outcomes at UKHSA conference |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Presentation at UKHSA conference entitled: Trends in COVID-19 hospital outcomes in England before and after vaccine introduction, 2020-2021: a cohort study. Presentation was made to other statistical and epidemiological colleagues, prompting questions and discussion. |
Year(s) Of Engagement Activity | 2022 |
Description | Press coverage on paper showing Delta variant of COVID-19 caused more hospitalisations than Alpha variant |
Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Media (as a channel to the public) |
Results and Impact | Press coverage on paper showing Delta variant of COVID-19 caused more hospitalisations than Alpha variant. Relates to paper published in The Lancet Infectious Diseases. Research was covered in 644 national, international and regional media outlets. |
Year(s) Of Engagement Activity | 2021 |