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      Understanding the leading indicators of hospital admissions from COVID-19 across successive waves in the UK

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          Abstract

          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.

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          Investigating Causal Relations by Econometric Models and Cross-spectral Methods

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            Immunological memory to SARS-CoV-2 assessed for up to 8 months after infection

            Understanding immune memory to SARS-CoV-2 is critical for improving diagnostics and vaccines, and for assessing the likely future course of the COVID-19 pandemic. We analyzed multiple compartments of circulating immune memory to SARS-CoV-2 in 254 samples from 188 COVID-19 cases, including 43 samples at ≥ 6 months post-infection. IgG to the Spike protein was relatively stable over 6+ months. Spike-specific memory B cells were more abundant at 6 months than at 1 month post symptom onset. SARS-CoV-2-specific CD4+ T cells and CD8+ T cells declined with a half-life of 3-5 months. By studying antibody, memory B cell, CD4+ T cell, and CD8+ T cell memory to SARS-CoV-2 in an integrated manner, we observed that each component of SARS-CoV-2 immune memory exhibited distinct kinetics.
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              Real-time tracking of self-reported symptoms to predict potential COVID-19

              A total of 2,618,862 participants reported their potential symptoms of COVID-19 on a smartphone-based app. Among the 18,401 who had undergone a SARS-CoV-2 test, the proportion of participants who reported loss of smell and taste was higher in those with a positive test result (4,668 of 7,178 individuals; 65.03%) than in those with a negative test result (2,436 of 11,223 participants; 21.71%) (odds ratio = 6.74; 95% confidence interval = 6.31–7.21). A model combining symptoms to predict probable infection was applied to the data from all app users who reported symptoms (805,753) and predicted that 140,312 (17.42%) participants are likely to have COVID-19.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: Formal analysisRole: Investigation
                Role: Formal analysisRole: Investigation
                Role: Formal analysisRole: Investigation
                Role: Formal analysisRole: InvestigationRole: VisualizationRole: Writing – original draft
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: SupervisionRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Journal
                Epidemiol Infect
                Epidemiol Infect
                HYG
                Epidemiology and Infection
                Cambridge University Press (Cambridge, UK )
                0950-2688
                1469-4409
                2023
                04 September 2023
                : 151
                : e172
                Affiliations
                [1 ]UK Health Security Agency, Data, Analytics and Surveillance, Nobel House , London, UK
                [2 ]Department of Mathematical Sciences, University of Liverpool , Liverpool, UK
                [3 ]Department of Mathematics, University of Manchester , Manchester, UK
                [4 ]Department of Pathology, University of Cambridge , Cambridge, UK
                Author notes
                Corresponding author: Thomas Ward; Email: Tom.Ward@ 123456ukhsa.gov.uk
                Author information
                https://orcid.org/0000-0001-8801-747X
                Article
                S0950268823001449
                10.1017/S0950268823001449
                10600913
                37664991
                dade0e87-b580-40f0-ac0b-5c356cc4c94f
                © The Author(s) 2023

                This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.

                History
                : 10 March 2023
                : 20 July 2023
                : 25 July 2023
                Page count
                Figures: 8, Tables: 1, References: 45, Pages: 14
                Categories
                Original Paper

                Public health
                covid-19,healthcare pressure,hospitalisation,leading indicators,syndromic surveillance

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