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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.)

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., (© 2024. Crown.)

Background: Cross-reactivity to SARS-CoV-2 from exposure to endemic human coronaviruses (eHCoV) is gaining increasing attention as a possible driver of both protection against infection and COVID-19 severity. Here we explore the potential role of cross-reactivity induced by eHCoVs on age-specific COVID-19 severity in a mathematical model of eHCoV and SARS-CoV-2 transmission., Methods: We use an individual-based model, calibrated to prior knowledge of eHCoV dynamics, to fully track individual histories of exposure to eHCoVs. We also model the emergent dynamics of SARS-CoV-2 and the risk of hospitalisation upon infection., Results: We hypothesise that primary exposure with any eHCoV confers temporary cross-protection against severe SARS-CoV-2 infection, while life-long re-exposure to the same eHCoV diminishes cross-protection, and increases the potential for disease severity. We show numerically that our proposed mechanism can explain age patterns of COVID-19 hospitalisation in EU/EEA countries and the UK. We further show that some of the observed variation in health care capacity and testing efforts is compatible with country-specific differences in hospitalisation rates under this model., Conclusions: This study provides a "proof of possibility" for certain biological and epidemiological mechanisms that could potentially drive COVID-19-related variation across age groups. Our findings call for further research on the role of cross-reactivity to eHCoVs and highlight data interpretation challenges arising from health care capacity and SARS-CoV-2 testing.

BackgroundReverse-transcription PCR (RT-PCR) assays are used to test for infection with the SARS-CoV-2 virus. RT-PCR tests are highly specific and the probability of false positives is low, but false negatives are possible depending on swab type and time since symptom onset.AimTo determine how the probability of obtaining a false-negative test in infected patients is affected by time since symptom onset and swab type.MethodsWe used generalised additive mixed models to analyse publicly available data from patients who received multiple RT-PCR tests and were identified as SARS-CoV-2 positive at least once.ResultsThe probability of a positive test decreased with time since symptom onset, with oropharyngeal (OP) samples less likely to yield a positive result than nasopharyngeal (NP) samples. The probability of incorrectly identifying an uninfected individual due to a false-negative test was considerably reduced if negative tests were repeated 24 hours later. For a small false-positive test probability (<0.5%), the true number of infected individuals was larger than the number of positive tests. For a higher false-positive test probability, the true number of infected individuals was smaller than the number of positive tests.ConclusionNP samples are more sensitive than OP samples. The later an infected individual is tested after symptom onset, the less likely they are to test positive. This has implications for identifying infected patients, contact tracing and discharging convalescing patients who are potentially still infectious.