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      Global perspective of COVID‐19 epidemiology for a full‐cycle pandemic

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          As of October 2020, there are >1 million documented deaths with COVID‐19. Excess deaths can be caused by both COVID‐19 and the measures taken. COVID‐19 shows extremely strong risk stratification across age, socioeconomic factors, and clinical factors. Calculation of years‐of‐life‐lost from COVID‐19 is methodologically challenging and can yield misleading over‐estimates. Many early deaths may have been due to suboptimal management, malfunctional health systems, hydroxychloroquine, sending COVID‐19 patients to nursing homes, and nosocomial infections; such deaths are partially avoidable moving forward. About 10% of the global population may be infected by October 2020. Global infection fatality rate is 0.15‐0.20% (0.03‐0.04% in those <70 years), with large variability across locations with different age‐structure, institutionalization rates, socioeconomic inequalities, population‐level clinical risk profile, public health measures, and health care. There is debate on whether at least 60% of the global population must be infected for herd immunity, or, conversely, mixing heterogeneity and pre‐existing cross‐immunity may allow substantially lower thresholds. Simulations are presented with a total of 1.58‐8.76 million COVID‐19 deaths over 5‐years (1/2020‐12/2024) globally (0.5‐2.9% of total global deaths). The most favorable figures in that range would be feasible if high risk groups can be preferentially protected with lower infection rates than the remaining population. Death toll may also be further affected by potential availability of effective vaccines and treatments, optimal management and measures taken, COVID‐19 interplay with influenza and other health problems, reinfection potential, and any chronic COVID‐19 consequences. Targeted, precise management of the pandemic and avoiding past mistakes would help minimize mortality.

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          Most cited references 55

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          The psychological impact of quarantine and how to reduce it: rapid review of the evidence

          Summary The December, 2019 coronavirus disease outbreak has seen many countries ask people who have potentially come into contact with the infection to isolate themselves at home or in a dedicated quarantine facility. Decisions on how to apply quarantine should be based on the best available evidence. We did a Review of the psychological impact of quarantine using three electronic databases. Of 3166 papers found, 24 are included in this Review. Most reviewed studies reported negative psychological effects including post-traumatic stress symptoms, confusion, and anger. Stressors included longer quarantine duration, infection fears, frustration, boredom, inadequate supplies, inadequate information, financial loss, and stigma. Some researchers have suggested long-lasting effects. In situations where quarantine is deemed necessary, officials should quarantine individuals for no longer than required, provide clear rationale for quarantine and information about protocols, and ensure sufficient supplies are provided. Appeals to altruism by reminding the public about the benefits of quarantine to wider society can be favourable.
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            OpenSAFELY: factors associated with COVID-19 death in 17 million patients

            Coronavirus disease 2019 (COVID-19) has rapidly affected mortality worldwide1. There is unprecedented urgency to understand who is most at risk of severe outcomes, and this requires new approaches for the timely analysis of large datasets. Working on behalf of NHS England, we created OpenSAFELY-a secure health analytics platform that covers 40% of all patients in England and holds patient data within the existing data centre of a major vendor of primary care electronic health records. Here we used OpenSAFELY to examine factors associated with COVID-19-related death. Primary care records of 17,278,392 adults were pseudonymously linked to 10,926 COVID-19-related deaths. COVID-19-related death was associated with: being male (hazard ratio (HR) 1.59 (95% confidence interval 1.53-1.65)); greater age and deprivation (both with a strong gradient); diabetes; severe asthma; and various other medical conditions. Compared with people of white ethnicity, Black and South Asian people were at higher risk, even after adjustment for other factors (HR 1.48 (1.29-1.69) and 1.45 (1.32-1.58), respectively). We have quantified a range of clinical factors associated with COVID-19-related death in one of the largest cohort studies on this topic so far. More patient records are rapidly being added to OpenSAFELY, we will update and extend our results regularly.
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              Targets of T cell responses to SARS-CoV-2 coronavirus in humans with COVID-19 disease and unexposed individuals

              Summary Understanding adaptive immunity to SARS-CoV-2 is important for vaccine development, interpreting coronavirus disease 2019 (COVID-19) pathogenesis, and calibration of pandemic control measures. Using HLA class I and II predicted peptide ‘megapools’, circulating SARS-CoV-2−specific CD8+ and CD4+ T cells were identified in ∼70% and 100% of COVID-19 convalescent patients, respectively. CD4+ T cell responses to spike, the main target of most vaccine efforts, were robust and correlated with the magnitude of the anti-SARS-CoV-2 IgG and IgA titers. The M, spike and N proteins each accounted for 11-27% of the total CD4+ response, with additional responses commonly targeting nsp3, nsp4, ORF3a and ORF8, among others. For CD8+ T cells, spike and M were recognized, with at least eight SARS-CoV-2 ORFs targeted. Importantly, we detected SARS-CoV-2−reactive CD4+ T cells in ∼40-60% of unexposed individuals, suggesting cross-reactive T cell recognition between circulating ‘common cold’ coronaviruses and SARS-CoV-2.

                Author and article information

                Eur J Clin Invest
                Eur J Clin Invest
                European Journal of Clinical Investigation
                John Wiley and Sons Inc. (Hoboken )
                25 October 2020
                [ 1 ] Departments of Medicine, of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, and Meta‐Research Innovation Center at Stanford (METRICS) Stanford University Stanford CA USA
                Author notes
                [* ] Correspondence

                John P.A. Ioannidis, Departments of Medicine, of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, and Meta‐Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA, USA.

                Email: jioannid@ 123456stanford.edu

                © 2020 Stichting European Society for Clinical Investigation Journal Foundation. Published by John Wiley & Sons Ltd

                This article is being made freely available through PubMed Central as part of the COVID-19 public health emergency response. It can be used for unrestricted research re-use and analysis in any form or by any means with acknowledgement of the original source, for the duration of the public health emergency.

                Page count
                Figures: 0, Tables: 2, Pages: 9, Words: 16509
                Funded by: Laura and John Arnold Foundation , open-funder-registry 10.13039/100009827;
                Custom metadata
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.9.3 mode:remove_FC converted:06.11.2020


                risk factors, mortality, infection fatality rate, epidemiology, covid‐19


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