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The COVID-19 pandemic led to 231,841 deaths and 940,243 hospitalisations in England, by the end of March 2023. This paper calculates the real-time infection hospitalisation risk (IHR) and infection fatality risk (IFR) using the Office for National Statistics Coronavirus Infection Survey (ONS CIS) and the Real-time Assessment of Community Transmission Survey between November 2020 to March 2023. The IHR and the IFR in England peaked in January 2021 at 3.39% (95% Credible Intervals (CrI): 2.79, 3.97) and 0.97% (95% CrI: 0.62, 1.36), respectively. After this time, there was a rapid decline in the severity from infection, with the lowest estimated IHR of 0.32% (95% CrI: 0.27, 0.39) in December 2022 and IFR of 0.06% (95% CrI: 0.04, 0.08) in April 2022. We found infection severity to vary more markedly between regions early in the pandemic however, the absolute heterogeneity has since reduced. The risk from infection of SARS-CoV-2 has changed substantially throughout the COVID-19 pandemic with a decline of 86.03% (80.86, 89.35) and 89.67% (80.18, 93.93) in the IHR and IFR, respectively, since early 2021. From April 2022 until March 2023, the end of the ONS CIS study, we found fluctuating patterns in the severity of infection with the resumption of more normative mixing, resurgent epidemic waves, patterns of waning immunity, and emerging variants that have shown signs of convergent evolution. The severity of SARS-CoV-2 infection varied over the course of the pandemic due to factors such as changes in variant characteristics and population immunity from previous infection or vaccination. Here, the authors estimate infection hospitalisation and infection fatality rates in England over time from the start of the pandemic until March 2023. [ABSTRACT FROM AUTHOR]

In the early phases of growth, resurgent epidemic waves of SARS-CoV-2 incidence have been characterised by localised outbreaks. Therefore, understanding the geographic dispersion of emerging variants at the start of an outbreak is key for situational public health awareness. Using telecoms data, we derived mobility networks describing the movement patterns between local authorities in England, which we have used to inform the spatial structure of a Bayesian BYM2 model. Surge testing interventions can result in spatio-temporal sampling bias, and we account for this by extending the BYM2 model to include a random effect for each timepoint in a given area. Simulated-scenario modelling and real-world analyses of each variant that became dominant in England were conducted using our BYM2 model at local authority level in England. Simulated datasets were created using a stochastic metapopulation model, with the transmission rates between different areas parameterised using telecoms mobility data. Different scenarios were constructed to reproduce real-world spatial dispersion patterns that could prove challenging to inference, and we used these scenarios to understand the performance characteristics of the BYM2 model. The model performed better than unadjusted test positivity in all the simulation-scenarios, and in particular when sample sizes were small, or data was missing for geographical areas. Through the analyses of emerging variant transmission across England, we found a reduction in the early growth phase geographic clustering of later dominant variants as England became more interconnected from early 2022 and public health interventions were reduced. We have also shown the recent increased geographic spread and dominance of variants with similar mutations in the receptor binding domain, which may be indicative of convergent evolution of SARS-CoV-2 variants. Author summary: Emerging variants of SARS-CoV-2 have been a significant catalyst of epidemic waves of incidence globally. These variants have caused concern due to transmission advantages, changes in the infection severity profile and immunological evasion. Understanding the spatial dispersion of a novel variant can be obfuscated by limited geographic test coverage, the ascertainment rate, and the proportion of tests that undergo sequencing or genotyping. Therefore, we have developed a spatial modelling approach based on the changing mobility patterns across England. We have used a BYM2 model structure that has been extended to include a random effect on each timepoint to account for variable daily testing patterns. This approach was assessed using simulated epidemic scenarios in England that would accurately reflect the spatial patterns of an emerging variant. The model estimated variant prevalence more accurately than unadjusted test positivity in every scenario tested particularly, when testing coverage was low and missing for some locations. We have used this modelling approach to estimate the spatial dispersion and growth for every dominant variant since the start of the SARS-CoV-2 pandemic. [ABSTRACT FROM AUTHOR]

In May 2022, a cluster of mpox cases were detected in the UK that could not be traced to recent travel history from an endemic region. Over the coming months, the outbreak grew, with over 3000 total cases reported in the UK, and similar outbreaks occurring worldwide. These outbreaks appeared linked to sexual contact networks between gay, bisexual and other men who have sex with men. Following the COVID-19 pandemic, local health systems were strained, and therefore effective surveillance for mpox was essential for managing public health policy. However, the mpox outbreak in the UK was characterised by substantial delays in the reporting of the symptom onset date and specimen collection date for confirmed positive cases. These delays led to substantial backfilling in the epidemic curve, making it challenging to interpret the epidemic trajectory in real-time. Many nowcasting models exist to tackle this challenge in epidemiological data, but these lacked sufficient flexibility. We have developed a nowcasting model using generalised additive models that makes novel use of individual-level patient data to correct the mpox epidemic curve in England. The aim of this model is to correct for backfilling in the epidemic curve and provide real-time characteristics of the state of the epidemic, including the real-time growth rate. This model benefited from close collaboration with individuals involved in collecting and processing the data, enabling temporal changes in the reporting structure to be built into the model, which improved the robustness of the nowcasts generated. The resulting model accurately captured the true shape of the epidemic curve in real time. Author summary: During 2022, outbreaks of mpox, the disease caused by the monkeypox virus, occurred simultaneously in multiple non-endemic countries, including England. These outbreaks were distinct from historic outbreaks with a majority of cases in gay, bisexual and other men who have sex with men and in individuals without recent travel histories to endemic countries. To inform public health policy, understanding the number of new cases and growth rate of the outbreak in real-time is essential. However, the outbreak was characterised by long delays from individuals developing symptoms (or getting a test) and being reported as a positive case. This creates a biased picture of the outbreak, where observed real-time cases underestimates the true extent of the outbreak. We developed a mathematical model that accounts for these reporting delays to estimate the true shape of the epidemic curve in real-time. The modelled outputs are able to accurately capture the true shape of the epidemic, and provide improved real-time insight over the raw data. This model was used continuously throughout the outbreak response in the UK to provide insight to the incident management team at the UK Health Security Agency. [ABSTRACT FROM AUTHOR]

The first year of the COVID-19 pandemic put considerable strain on healthcare systems worldwide. In order to predict the effect of the local epidemic on hospital capacity in England, we used a variety of data streams to inform the construction and parameterisation of a hospital progression model, EpiBeds, which was coupled to a model of the generalised epidemic. In this model, individuals progress through different pathways (e.g. may recover, die, or progress to intensive care and recover or die) and data from a partially complete patient-pathway line-list was used to provide initial estimates of the mean duration that individuals spend in the different hospital compartments. We then fitted EpiBeds using complete data on hospital occupancy and hospital deaths, enabling estimation of the proportion of individuals that follow the different clinical pathways, the reproduction number of the generalised epidemic, and to make short-term predictions of hospital bed demand. The construction of EpiBeds makes it straightforward to adapt to different patient pathways and settings beyond England. As part of the UK response to the pandemic, EpiBeds provided weekly forecasts to the NHS for hospital bed occupancy and admissions in England, Wales, Scotland, and Northern Ireland at national and regional scales. Author summary: COVID-19, the disease caused by SARS-CoV-2, leads to a high proportion of cases requiring admission to hospital. Coupled with the high burden of infections worldwide, this put substantial pressure on healthcare systems. To enable public health systems to cope with the high levels of demand, forecasting models are vital. These models enable public health managers to plan their workloads accordingly. Here, we developed EpiBeds, which combines an epidemic model with a model for patient flow through hospitals. By fitting this model to data from England, EpiBeds has been used to provide short-term forecasts of hospital admissions and bed demand weekly throughout the COVID-19 pandemic. In this paper, we describe the motivation behind the structure of EpiBeds, how the model is fitted to data, and report the estimates of the key parameters throughout the pandemic. We then evaluate the performance of EpiBeds by comparing generated forecasts to future data points, finding good agreement between the forecasts and data. [ABSTRACT FROM AUTHOR]

The emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) B.1.1.529 (Omicron) variant caused international concern due to its rapid spread in Southern Africa. It was unknown whether this variant would replace or coexist with (either transiently or long term) the then-dominant Delta variant on its introduction to England. We developed a set of hierarchical logistic growth models to describe changes in the frequency of S gene target failure (SGTF) PCR tests, a proxy for Omicron. The doubling time of SGTF cases peaked at 1.56 days (95% CI: 1.49 to 1.63) on 5 December, whereas triple-positive cases were halving every 5.82 days (95% CI: 5.11 to 6.67) going into Christmas 2021. We were unable to characterize the replacement of Delta by Omicron with a single rate. The replacement rate decreased by 53.56% (95% CrI: 45.38 to 61.01) between 14 and 15 December, meaning the competitive advantage of Omicron approximately halved. Preceding the changepoint, Omicron was replacing Delta 16.24% (95% CrI: 9.72 to 23.41) faster in those with two or more vaccine doses, indicative of vaccine escape being a substantial component of competitive advantage. Despite the slowdown, Delta was almost entirely replaced in England within a month of the first sequenced domestic case. The synchrony of changepoints across regions at various stages of Omicron epidemics suggests that the growth rate advantage was not attenuated because of biological mechanisms related to strain competition. The step change in replacement could have resulted from behavioral changes, potentially elicited by public health messaging or policies, that differentially affected Omicron. Omicron overtakes Delta: When the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant emerged near the end of 2021, it was unclear whether it would coexist with or outcompete the then dominant Delta variant. Using routine surveillance PCR tests as with S gene target failure as a proxy for Omicron detection, Paton et al. found evidence that this emerging variant had almost entirely replaced the Delta variant in England within a month of its introduction. Omicron's replacement of Delta was faster in individuals who had received vaccine boosters, suggesting that immune escape of the variant may have contributed to its success. [ABSTRACT FROM AUTHOR]

Objectives: Influenza-like-illness (ILI) is a commonly used symptom categorization in seasonal disease surveillance focusing on influenza in community and clinical settings. However, SARS-CoV-2 often causes presentation with a similar symptom profile. We explore how SARS-CoV-2-positive individuals can influence surveillance trends for the World Health Organization, the United States Centre for Disease Control, and the European Centre for Disease Control (ECDC) ILI criteria., Methods: Harnessing the Winter COVID-19 Infection Study in England, a cohort study, the prevalence of different ILI definitions is modeled using multilevel regression and poststratification using age and spatial stratifications with temporal smoothing. Trends over time across stratifications were compared for SARS-CoV-2 positive and negative individuals to understand differences in ILI trends. Symptom presentation across positive and negative SARS-CoV-2 cases were compared., Results: SARS-CoV-2 symptom profiles are shown to overlap with the ILI case definitions, particularly for "cough" and "fever", causing SARS-CoV-2 positive individuals to be frequently detected as ILI cases. The trend of SARS-CoV-2 positives is a substantial component of the ILI-modeled trend, driving an earlier perceived peak in prevalence. The ECDC symptom definition was most influenced by SARS-CoV-2 positive individuals., Conclusions: Using a large community cohort we show how SARS-CoV-2 can impact ILI surveillance trends. SARS-CoV-2 makes up a substantial part of the community ILI burden and public health messaging should reflect this when discussing ILI. We show ILI is no longer a strong proxy for influenza activity alone., Competing Interests: Declarations of competing interest The authors have no competing interests to declare., (Crown Copyright © 2024. Published by Elsevier Ltd. All rights reserved.)

Following the end of universal testing in the UK, hospital admissions are a key measure of COVID-19 pandemic pressure. Understanding leading indicators of admissions at the National Health Service (NHS) Trust, regional and national geographies help health services plan for ongoing pressures. We explored the spatio-temporal relationships of leading indicators of hospitalisations across SARS-CoV-2 waves in England. This analysis includes an evaluation of internet search volumes from Google Trends, NHS triage calls and online queries, the NHS COVID-19 app, lateral flow devices (LFDs), and the ZOE app. Data sources were analysed for their feasibility as leading indicators using Granger causality, cross-correlation, and dynamic time warping at fine spatial scales. Google Trends and NHS triages consistently temporally led admissions in most locations, with lead times ranging from 5 to 20 days, whereas an inconsistent relationship was found for the ZOE app, NHS COVID-19 app, and LFD testing, which diminished with spatial resolution, showing cross-correlation of leads between -7 and 7 days. The results indicate that novel surveillance sources can be used effectively to understand the expected healthcare burden within hospital administrative areas though the temporal and spatial heterogeneity of these relationships is a key determinant of their operational public health utility.

The COVID-19 pandemic has had high mortality rates in the elderly and frail worldwide, particularly in care homes. This is driven by the difficulty of isolating care homes from the wider community, the large population sizes within care facilities (relative to typical households), and the age/frailty of the residents. To quantify the mortality risk posed by disease, the case fatality risk (CFR) is an important tool. This quantifies the proportion of cases that result in death. Throughout the pandemic, CFR amongst care home residents in England has been monitored closely. To estimate CFR, we apply both novel and existing methods to data on deaths in care homes, collected by Public Health England and the Care Quality Commission. We compare these different methods, evaluating their relative strengths and weaknesses. Using these methods, we estimate temporal trends in the instantaneous CFR (at both daily and weekly resolutions) and the overall CFR across the whole of England, and dis-aggregated at regional level. We also investigate how the CFR varies based on age and on the type of care required, dis-aggregating by whether care homes include nursing staff and by age of residents. This work has contributed to the summary of measures used for monitoring the UK epidemic., Competing Interests: The authors have declared that no competing interests exist.

Background: Predicting hospital length of stay (LoS) for patients with COVID-19 infection is essential to ensure that adequate bed capacity can be provided without unnecessarily restricting care for patients with other conditions. Here, we demonstrate the utility of three complementary methods for predicting LoS using UK national- and hospital-level data., Method: On a national scale, relevant patients were identified from the COVID-19 Hospitalisation in England Surveillance System (CHESS) reports. An Accelerated Failure Time (AFT) survival model and a truncation corrected method (TC), both with underlying Weibull distributions, were fitted to the data to estimate LoS from hospital admission date to an outcome (death or discharge) and from hospital admission date to Intensive Care Unit (ICU) admission date. In a second approach we fit a multi-state (MS) survival model to data directly from the Manchester University NHS Foundation Trust (MFT). We develop a planning tool that uses LoS estimates from these models to predict bed occupancy., Results: All methods produced similar overall estimates of LoS for overall hospital stay, given a patient is not admitted to ICU (8.4, 9.1 and 8.0 days for AFT, TC and MS, respectively). Estimates differ more significantly between the local and national level when considering ICU. National estimates for ICU LoS from AFT and TC were 12.4 and 13.4 days, whereas in local data the MS method produced estimates of 18.9 days., Conclusions: Given the complexity and partiality of different data sources and the rapidly evolving nature of the COVID-19 pandemic, it is most appropriate to use multiple analysis methods on multiple datasets. The AFT method accounts for censored cases, but does not allow for simultaneous consideration of different outcomes. The TC method does not include censored cases, instead correcting for truncation in the data, but does consider these different outcomes. The MS method can model complex pathways to different outcomes whilst accounting for censoring, but cannot handle non-random case missingness. Overall, we conclude that data-driven modelling approaches of LoS using these methods is useful in epidemic planning and management, and should be considered for widespread adoption throughout healthcare systems internationally where similar data resources exist., (© 2021. The Author(s).)