The new strain of coronavirus, Covid-19, has led many countries to take drastic social distancing measures, and has driven consumers to supermarkets to stock up on foodstuffs, hygiene, and over-the-counter medical products such as vitamins and pain relievers. However, according to a poll conducted by Ipsos, an overwhelming majority of British consumers (** percent) think it is not acceptable to stockpile during the Coronavirus outbreak.

As Covid-19 continues to impact governments and communities worldwide, new data emerging on the virus are helping individuals to stay on top of the situation and protect themselves and those around them. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

This open data publication has moved to COVID-19 Statistical Data in Scotland (from 02/11/2022) Novel coronavirus (COVID-19) is a new strain of coronavirus first identified in Wuhan, China. Clinical presentation may range from mild-to-moderate illness to pneumonia or severe acute respiratory infection. This dataset provides information on demographic characteristics (age, sex, deprivation) of confirmed novel coronavirus (COVID-19) cases, as well as trend data regarding the wider impact of the virus on the healthcare system. Data includes information on primary care out of hours consultations, respiratory calls made to NHS24, contact with COVID-19 Hubs and Assessment Centres, incidents received by Scottish Ambulance Services (SAS), as well as COVID-19 related hospital admissions and admissions to ICU (Intensive Care Unit). Further data on the wider impact of the COVID-19 response, focusing on hospital admissions, unscheduled care and volume of calls to NHS24, is available on the COVID-19 Wider Impact Dashboard. There is a large amount of data being regularly published regarding COVID-19 (for example, Coronavirus in Scotland - Scottish Government and Deaths involving coronavirus in Scotland - National Records of Scotland. Additional data sources relating to this topic area are provided in the Links section of the Metadata below. Information on COVID-19, including stay at home advice for people who are self-isolating and their households, can be found on NHS Inform. All publications and supporting material to this topic area can be found in the weekly COVID-19 Statistical Report. The date of the next release can be found on our list of forthcoming publications. Data visualisation is available to view in the interactive dashboard accompanying the COVID-19 Statistical Report. Please note information on COVID-19 in children and young people of educational age, education staff and educational settings is presented in a new COVID-19 Education Surveillance dataset going forward.

This publication was archived on 12 October 2023. Please see the Viral Respiratory Diseases (Including Influenza and COVID-19) in Scotland publication for the latest data. This dataset provides information on number of new daily confirmed cases, negative cases, deaths, testing by NHS Labs (Pillar 1) and UK Government (Pillar 2), new hospital admissions, new ICU admissions, hospital and ICU bed occupancy from novel coronavirus (COVID-19) in Scotland, including cumulative totals and population rates at Scotland, NHS Board and Council Area levels (where possible). Seven day positive cases and population rates are also presented by Neighbourhood Area (Intermediate Zone 2011). Information on how PHS publish small are COVID figures is available on the PHS website. Information on demographic characteristics (age, sex, deprivation) of confirmed novel coronavirus (COVID-19) cases, as well as trend data regarding the wider impact of the virus on the healthcare system is provided in this publication. Data includes information on primary care out of hours consultations, respiratory calls made to NHS24, contact with COVID-19 Hubs and Assessment Centres, incidents received by Scottish Ambulance Services (SAS), as well as COVID-19 related hospital admissions and admissions to ICU (Intensive Care Unit). Further data on the wider impact of the COVID-19 response, focusing on hospital admissions, unscheduled care and volume of calls to NHS24, is available on the COVID-19 Wider Impact Dashboard. Novel coronavirus (COVID-19) is a new strain of coronavirus first identified in Wuhan, China. Clinical presentation may range from mild-to-moderate illness to pneumonia or severe acute respiratory infection. COVID-19 was declared a pandemic by the World Health Organisation on 12 March 2020. We now have spread of COVID-19 within communities in the UK. Public Health Scotland no longer reports the number of COVID-19 deaths within 28 days of a first positive test from 2nd June 2022. Please refer to NRS death certificate data as the single source for COVID-19 deaths data in Scotland. In the process of updating the hospital admissions reporting to include reinfections, we have had to review existing methodology. In order to provide the best possible linkage of COVID-19 cases to hospital admissions, each admission record is required to have a discharge date, to allow us to better match the most appropriate COVID positive episode details to an admission. This means that in cases where the discharge date is missing (either due to the patient still being treated, delays in discharge information being submitted or data quality issues), it has to be estimated. Estimating a discharge date for historic records means that the average stay for those with missing dates is reduced, and fewer stays overlap with records of positive tests. The result of these changes has meant that approximately 1,200 historic COVID admissions have been removed due to improvements in methodology to handle missing discharge dates, while approximately 820 have been added to the cumulative total with the inclusion of reinfections. COVID-19 hospital admissions are now identified as the following: A patient's first positive PCR or LFD test of the episode of infection (including reinfections at 90 days or more) for COVID-19 up to 14 days prior to admission to hospital, on the day of their admission or during their stay in hospital. If a patient's first positive PCR or LFD test of the episode of infection is after their date of discharge from hospital, they are not included in the analysis. Information on COVID-19, including stay at home advice for people who are self-isolating and their households, can be found on NHS Inform. Data visualisation of Scottish COVID-19 cases is available on the Public Health Scotland - Covid 19 Scotland dashboard. Further information on coronavirus in Scotland is available on the Scottish Government - Coronavirus in Scotland page, where further breakdown of past coronavirus data has also been published.

As the new coronavirus strain Sars-Cov-2 (Covid-19) is spreading across the world at an alarming pace, the consumer market is seeing disruptions as manufacturing and production sectors slow down, particularly in countries where the disease hit the hardest. According to a study conducted with UK retailers in the food, fashion, and health and beauty categories, retailers are positive that the Covid-19 will have a negative impact on their sales. While ** percent thought the impact would be significant, a great share of respondents thought the outbreak would have a slightly negative impact on their sales, if the virus persists.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Fact and Figures page.

These datasets comprise the main analyses for the paper “Household Transmission of Seasonal Coronavirus Infections: Results from the Flu Watch cohort study”, published in Wellcome Open Research. Details of the statistical methods are reported in the article. Datasets are given in CSV format and, where relevant, in .dta format. Descriptions for each dataset are as follows:

Household_CoV_acquired.csv/dta – data required to compute the proportion of cases presumably acquired outside of the household versus and the proportion acquired from household transmission. Each row represents an anonymised PCR-confirmed seasonal coronavirus case.

Household_CoV_TransmissionRisk.csv/dta – data required to compute the risk of symptomatic onward household transmission following a seasonal coronavirus index case, and perform stratified descriptive analyses.

Household_CoV_SAR.csv/.dta – data required to compute the seasonal coronavirus secondary attack risk overall and by strain. Each row represents an anonymised exposed-index pair from a given outbreak.

HH Transmission Serial Interval.csv – presents available, anonymised data required to compute the median clinical-onset serial interval overall and by strain for each household outbreak

Mutations in SARS-CoV-2 genomes and their detection in the GISAID database: Nucleotide variants and amino acid changes.

Demographic and epidemiological characteristics of laboratory-confirmed COVID-19 cases in Vietnam.

The main entity of this document is a structure with accession number 5jil

BackgroundThe emergence of a new coronavirus strain caused the COVID-19 pandemic. While vaccines effectively control the infection, it’s important to acknowledge the potential for side effects, including rare cases like psychosis, which may increase with the rising number of vaccinations.ObjectivesOur systematic review aimed to examine cases of new-onset psychosis following COVID-19 vaccination.MethodsWe conducted a systematic review of case reports and case series on new-onset psychosis following COVID-19 vaccination from December 1st, 2019, to November 21st, 2023, using PubMed, MEDLINE, ClinicalKey, and ScienceDirect. Data extraction covered study and participant characteristics, comorbidities, COVID-19 vaccine details, and clinical features. The Joanna Briggs Institute quality assessment tools were employed for included studies, revealing no significant publication bias.ResultsA total of 21 articles described 24 cases of new-onset psychotic symptoms following COVID-19 vaccination. Of these cases, 54.2% were female, with a mean age of 33.71 ± 12.02 years. Psychiatric events were potentially induced by the mRNA BNT162b2 vaccine in 33.3% of cases, and psychotic symptoms appeared in 25% following the viral vector ChAdOx1 nCoV-19 vaccine. The mean onset time was 5.75 ± 8.14 days, mostly reported after the first or second dose. The duration of psychotic symptoms ranged between 1 and 2 months with a mean of 52.48 ± 60.07 days. Blood test abnormalities were noted in 50% of cases, mainly mild to moderate leukocytosis and elevated C-reactive protein. Magnetic resonance imaging results were abnormal in 20.8%, often showing fluid-attenuated inversion recovery hyperintensity in the white matter. Treatment included atypical antipsychotics in 83.3% of cases, typical antipsychotics in 37.5%, benzodiazepines in 50%, 20.8% received steroids, and 25% were prescribed antiepileptic medications. Overall, 50% of patients achieved full recovery.ConclusionStudies on psychiatric side effects post-COVID-19 vaccination are limited, and making conclusions on vaccine advantages or disadvantages is challenging. Vaccination is generally safe, but data suggest a potential link between young age, mRNA, and viral vector vaccines with new-onset psychosis within 7 days post-vaccination. Collecting data on vaccine-related psychiatric effects is crucial for prevention, and an algorithm for monitoring and treating mental health reactions post-vaccination is necessary for comprehensive management.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO, identifier CRD42023446270.

Genome assembly of 44 SARS-CoV-2 sequences in Vietnam.

Since the start of COVID-19 pandemic the Republic of Djibouti, in the horn of Africa, has experienced two epidemic waves of the virus between April and August 2020 and between February and May 2021. By May 2021, COVID-19 had affected 1.18% of the Djiboutian population and caused 152 deaths. Djibouti hosts several foreign military bases which makes it a potential hot-spot for the introduction of different SARS-CoV-2 strains. We genotyped fifty three viruses that have spread during the two epidemic waves. Next, using spike sequencing of twenty-eight strains and whole genome sequencing of thirteen strains, we found that Nexstrain clades 20A and 20B with a typically European D614G substitution in the spike and a frequent P2633L substitution in nsp16 were the dominant viruses during the first epidemic wave, while the clade 20H South African variants spread during the second wave characterized by an increase in the number of severe forms of COVID-19.

Key demographics, ventilation parameters, treatment and disease severity scores, by UK pandemic wave.

In this study, we evaluated the impact of viral variant, in addition to other variables, on within-host viral burden, by analysing cycle threshold (Ct) values derived from nose and throat swabs, collected as part of the UK COVID-19 Infection Survey. Because viral burden distributions determined from community survey data can be biased due to the impact of variant epidemiology on the time-since-infection of samples, we developed a method to explicitly adjust observed Ct value distributions to account for the expected bias. By analysing the adjusted Ct values using partial least squares regression, we found that among unvaccinated individuals with no known prior exposure, viral burden was 44% lower among Alpha variant infections, compared to those with the predecessor strain, B.1.177. Vaccination reduced viral burden by 67%, and among vaccinated individuals, viral burden was 286% higher among Delta variant, compared to Alpha variant, infections. In addition, viral burden increased by 17% for every 10-year age increment of the infected individual. In summary, within-host viral burden increases with age, is reduced by vaccination, and is influenced by the interplay of vaccination status and viral variant.

Sequences can be accessed via the the European Nucleotide Archive (ENA) at https://www.ebi.ac.uk/ena/browser/home. (TXT)

Human coronaviruses (hCoV) have become a threat to global health and society, as evident from the SARS outbreak in 2002 caused by SARS-CoV-1 and the most recent COVID-19 pandemic caused by SARS-CoV-2. Despite high sequence similarity between SARS-CoV-1 and -2, each strain has distinctive virulence. A better understanding of the basic molecular mechanisms mediating changes in virulence is needed. Here, we profile the virus-host protein-protein interactions of two hCoV non-structural proteins (nsps) that are critical for virus replication. We use tandem mass tag-multiplexed quantitative proteomics to sensitively compare and contrast the interactomes of nsp2 and nsp4 from three betacoronavirus strains: SARS-CoV-1, SARS-CoV-2, and hCoV-OC43 – an endemic strain associated with the common cold. This approach enables the identification of both unique and shared host cell protein binding partners and the ability to further compare the enrichment of common interactions across homologs from related strains. We identify common nsp2 interactors involved in endoplasmic reticulum (ER) Ca2+ signaling and mitochondria biogenesis. We also identify nsp4 interactors unique to each strain, such as E3 ubiquitin ligase complexes for SARS-CoV-1 and ER homeostasis factors for SARS-CoV-2. Common nsp4 interactors include N-linked glycosylation machinery, unfolded protein response (UPR) associated proteins, and anti-viral innate immune signaling factors. Both nsp2 and nsp4 interactors are strongly enriched in proteins localized at mitochondrial-associated ER membranes suggesting a new functional role for modulating host processes, such as calcium homeostasis, at these organelle contact sites. Our results shed light on the role these hCoV proteins play in the infection cycle, as well as host factors that may mediate the divergent pathogenesis of OC43 from SARS strains. Our mass spectrometry workflow enables rapid and robust comparisons of multiple bait proteins, which can be applied to additional viral proteins. Furthermore, the identified common interactions may present new targets for exploration by host-directed anti-viral therapeutics.

Description of additional variables and parameters used in calculation of adjusted Ct values.

A breakdown of sample sizes, by category is also provided. *based upon a Ct value decrease of 3 being equivalent to a 10-fold increase in viral load [34].

Cox proportional risk analysis for mortality of all intubated and ventilated patients across both UK waves of the Sars-Cov-2 pandemic.

Association between hypertension and the odds of severe COVID-19 (N = 16,134).