MRC Biostatistics UnitNowcasting and Forecasting of the COVID-19 Pandemic
Real-time tracking of a pandemic, as data accumulate over time, is an essential component of a public health response to a new outbreak. A team of statistical modellers at the Medical Research Council Biostatistics Unit regularly nowcast and forecast COVID-19 infections and deaths. This information feeds directly to SAGE sub-group, Scientific Pandemic Influenza sub-group on Modelling (SPI-M) and to regional PHE teams.
The work uses a statistical programming model called a transmission model (Birrell et al. 2020), data on daily COVID-19 confirmed deaths from PHE (by NHS region and age group), and published information on the risk of dying and the time from infection to death, to reconstruct the number of new COVID-19 infections over time; estimate a measure of ongoing transmission (R); and predict the number of new COVID-19 deaths in different regions and age groups.
For more information and to view the latest published reports, go to the Nowcasting and Forecasting page
Nowcasting COVID-19 Deaths
Every day the UK Government publishes figures for the numbers of deaths within 28 days of a positive coronavirus test: https://coronavirus.data.gov.uk/. These numbers come from Public Health England (PHE). Both the number of deaths reported on each day and the number of deaths that occurred on each day are presented. The notification of a COVID-19 death to PHE is subject to a reporting delay, which is typically several days, but can be as long as several weeks. Consequently, the number of deaths that occurred on each day is incomplete, particularly for the most recent days. The process of estimating the number of deaths that occurred on each day from the numbers of deaths that have so far been reported is referred to as `nowcasting’. For details of our approach to such a nowcast see Seaman S et al. 2020.
Severity of COVID-19
For the COVID-19 pandemic, estimates of the infection- and case severity risks, i.e. the probabilities of experiencing severe events such as hospitalisations, admission to intensive care and death, are crucial to understand and predict the burden and impact on healthcare services.
No single dataset can provide enough information on its own to estimate severity, but estimation is feasible by synthesising multiple datasets. Work at the BSU is investigating use of both individual- and aggregate-level data, from clinical cohorts and population registries, and a combination of survival analysis techniques, to estimate severity as data accumulates over the course of the epidemic.
Publications:
- Grosso FM, Presanis A et al (BMC Public Health): Decreasing hospital burden of COVID-19 during the first wave in Regione Lombardia: an emergency measures context
- Nyberg et al (BMJ): Risk of hospital admission for patients with SARS-CoV-2 variant B.1.1.7: cohort analysis
- Presanis A, Kunzmann K et al (BMC Infect Dis): Risk factors associated with severe hospital burden of COVID-19 disease in Regione Lombardia: a cohort study
- Twohig KA et al (Lancet Infect Dis): 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 – The Lancet Infectious Diseases
- Kirwan P et al (pre-print, currently under revision): https://arxiv.org/abs/2103.04867
Full Publications List
Álvarez-Esteban PC et al. Predicting COVID-19 progression from diagnosis to recovery or death linking primary care and hospital records in Castilla y León (Spain). PLoS ONE 16(9): e0257613. https://doi.org/10.1371/journal.pone.0257613
Barnard S et al. Methods for modelling excess mortality across England during the COVID-19 pandemic. Stat Methods Med Res. 2021 Oct 23;9622802211046384. PMID: 34693801
Birrell P et al. Real-time Nowcasting and Forecasting of COVID-19 Dynamics in England: the first wave? Phil. Trans. R. Soc. B. 2021. https://doi.org/10.1098/rstb.2020.0279.
Challen R et al. Early epidemiological signatures of novel SARS-CoV-2 variants: establishment of B.1.617.2 in England. medRxiv preprint: https://doi.org/10.1101/2021.06.05.21258365
Cooper DJ et al. A prospective study of risk factors associated with seroprevalence of SARS-CoV-2 antibodies in healthcare workers at a large UK teaching hospital. medRxiv preprint: https://doi.org/10.1101/2020.11.03.20220699
Docherty AB et al. Changes in in-hospital mortality in the first wave of COVID-19: a multicentre prospective observational cohort study using the WHO Clinical Characterisation Protocol UK. The Lancet Respiratory Medicine. In press. doi: https://doi.org/10.1016/S2213-2600(21)00175-2
Funk S et al. Short-term forecasts to inform the response to the Covid-19 epidemic in the UK. medRxiv preprint: https://doi.org/10.1101/2020.11.11.20220962
Grosso FM et al. Decreasing hospital burden of COVID-19 during the first wave in Regione Lombardia: an emergency measures context. BMC Public Health. 2021 March. https://doi.org/10.21203/rs.3.rs-288193/v1
Illingworth C et al. Superspreaders drive the largest outbreaks of hospital onset COVID-19 infections. eLife 2021;10:e67308 DOI: 10.7554/eLife.67308
Illingworth C et al. A2B-COVID: A method for evaluating potential SARS-CoV-2 transmission events. Mol Biol Evol. 2022 Feb 2;msac025. PMID: 35106603
Hall VJ et al. SARS-CoV-2 infection rates of antibody-positive compared with antibody-negative health-care workers in England: a large, multicentre, prospective cohort study (SIREN). The Lancet. 2021 ; 397: 1459–69. PMID: 33844963
Hamilton WL et al. Applying prospective genomic surveillance to support investigation of hospital-onset COVID-19. Lancet Infect Dis. 2021 Jul;21(7):916-917. PMID: 33984262
Kemp SA et al. SARS-CoV-2 evolution during treatment of chronic infection. Nature. 2021 Feb 5. doi: 10.1038/s41586-021-03291-y. PMID: 33545711
Kemp SA et al. Neutralising antibodies in Spike mediated SARS-CoV-2 adaptation. medRxiv preprint: https://www.medrxiv.org/content/10.1101/2020.12.05.20241927v3
Kirwan P et al. Trends in risks of severe events and lengths of stay for COVID-19 hospitalisations in England over the pre-vaccination era: results from the Public Health England SARI-Watch surveillance scheme. arXiv preprint: arXiv:2103.04867
Laydon DJ et al. Modelling the impact of the tier system on SARS-CoV-2 transmission in the UK between the first and second national lockdowns. BMJ. 2021. Open 11:e050346. PubMed PMID:33888533
Mulchandani R et al. Self assessment overestimates historical COVID-19 disease relative to sensitive serological assays: cross sectional study in UK key workers. medRxiv preprint: https://doi.org/10.1101/2020.08.19.20178186
Nicholson G et al. Local prevalence of transmissible SARS-CoV-2 infection: an integrative causal model for debiasing fine-scale targeted testing data. medRxiv preprint: https://doi.org/10.1101/2021.05.17.21256818
Nyberg T et al. Risk of hospital admission for patients with SARS-CoV-2 variant B.1.1.7: cohort analysis. BMJ. 2021; 373 doi: https://doi.org/10.1136/bmj.n1412
Pellis L et al. Estimation of reproduction numbers in real time: conceptual and statistical challenges. Royal Statistical Society website. 2021. PellisBirrel.pdf (rss.org.uk)
Presanis A et al. Risk factors associated with severe hospital burden of COVID-19 disease in Regione Lombardia: a cohort study. BMC Infect Dis. 2021 Oct 7;21(1):1041. PMID: 34620121
Pouwels KB et al. Community prevalence of SARS-CoV-2 in England from April to November, 2020: results from the ONS Coronavirus Infection Survey. Lancet Public Health. 2020 Dec 10:S2468-2667(20)30282-6. doi: 10.1016/S2468-2667(20)30282-6. Online ahead of print. PMID: 33308423
Samartsidis P et al. Evaluating the impact of local tracing partnerships on the performance of contact tracing for COVID-19 in England. arXiv preprint: https://arxiv.org/abs/2110.02005
Seaman S et al. Estimating a Time-to-Event Distribution from Right-Truncated Data in an Epidemic: a Review of Methods. Sage SMMR. 2021 Dec. https://doi.org/10.1177/09622802211023955
Nowcasting CoVID-19 Deaths in England by Age and Region. medRxiv preprint: https://doi.org/10.1101/2020.09.15.20194209
Seaman et al. Adjusting for time of infection or positive test when estimating the risk of a post-infection outcome in an epidemic. medRxiv preprint: https://doi.org/10.1101/2021.08.13.21262014
Lancet Infect Dis. 2020 Jul;20(7):767-769. doi: 10.1016/S1473-3099(20)30398-4. PMID: 32422199
Stage HB et al. Shut and re-open: the role of schools in the spread of COVID-19 in Europe. The Royal Society Publishing. 2021. https://doi.org/10.1098/rstb.2020.0277
Twohig KA et al. 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. Lancet Infect Dis. 2021 Aug. https://doi.org/10.1016/S1473-3099(21)00475-8
Walker AS et al. Viral load in community SARS-CoV-2 cases varies widely and temporally. medRxiv preprint doi: https://doi.org/10.1101/2020.10.25.20219048
Walker AS et al. Ct threshold values, a proxy for viral load in community SARS-CoV-2 cases, demonstrate wide variation across populations and over time. medRxiv preprint doi: https://doi.org/10.1101/2020.10.25.20219048
Wallace S et al. SIREN protocol: Impact of detectable anti-SARS-CoV-2 on the subsequent incidence of COVID-19 in 100,000 healthcare workers: do antibody positive healthcare workers have less reinfection than antibody negative healthcare workers? medRxiv preprint doi: https://doi.org/10.1101/2020.12.15.20247981
Zhang X-S et al. Transmission dynamics and control measures of COVID-19 outbreak in China: a modelling study. Sci Rep. 2021 Jan 29;11(1):2652. PMID: 33514781