Mutation Landscape of SARS-Cov-2 Reveals Five Mutually Exclusive Clusters of Leading and Trailing Single Nucleotide Substitutions
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Leveraging Artificial Intelligence to Analyze the COVID-19 Distribution Pattern Based on Socio-Economic Determinants
Leveraging Artificial Intelligence to Analyze the COVID-19 Distribution Pattern based on Socio-economic Determinants Mohammadhossein Ghahramania,<, Francesco Pillaa aSpatial Dynamics Lab, University College Dublin, Ireland ARTICLEINFO ABSTRACT Keywords: The spatialization of socioeconomic data can be used and integrated with other sources of information Geodemographic analysis to reveal valuable insights. Such data can be utilized to infer different variations, such as the dynamics Big Data of city dwellers and their spatial and temporal variability. This work focuses on such applications to Dimensionality Reduction explore the underlying association between socioeconomic characteristics of different geographical Neural Network regions in Dublin, Ireland, and the number of confirmed COVID cases in each area. Our aim is to implement a machine learning approach to identify demographic characteristics and spatial patterns. Spatial analysis was used to describe the pattern of interest in Electoral Divisions (ED), which are the legally defined administrative areas in the Republic of Ireland for which population statistics are published from the census data. We used the most informative variables of the census data to model the number of infected people in different regions at ED level. Seven clusters detected by implementing an unsupervised neural network method. The distribution of people who have contracted the virus was studied. 1. Introduction Different inequality issues can also make it difficult to main- tain social distancing [37]. Hence, it is essential to under- In March 11th, 2020, the Republic of Ireland’s govern- stand the existed relation between socio-economic inequal- ment launched a national action plan in response to COVID- ities and the pandemic. As discussed, such inequalities can 19, a widespread lock-down in order to minimize the risk threaten public health by making it difficult to enforce pro- of illness. -
Arxiv:2104.01215V1 [Cs.SI] 2 Apr 2021 Agreement on Ambiguous Statements
The Coronavirus is a Bioweapon: Analysing Coronavirus Fact-Checked Stories Lynnette Hui Xian Ng1 ID and Kathleen M. Carley1 ID CASOS, Institute for Software Research Carnegie Mellon University, Pittsburgh, PA 15213 fhuixiann, [email protected] Abstract. The 2020 coronavirus pandemic has heightened the need to flag coronavirus-related misinformation, and fact-checking groups have taken to verifying misinformation on the Internet. We explore stories reported by fact-checking groups PolitiFact, Poynter and Snopes from January to June 2020, characterising them into six story clusters before then analyse time-series and story validity trends and the level of agree- ment across sites. We further break down the story clusters into more granular story types by proposing a unique automated method with a BERT classifier, which can be used to classify diverse story sources, in both fact-checked stories and tweets. Keywords: Coronavirus · Fact Checking · Misinformation 1 Introduction The 2020 coronavirus pandemic has prompted major fact-checking groups like PolitiFact, Poynter and Snopes to verify misinformation on the Internet. Since fact-checking influences citizens' reactions [5], coronavirus-related fact checking is crucial in informing the public and reducing panic. Previous works compared election-related misinformation from fact-checking sites. While Amazeen con- clude a high level of agreement [1], Lim augmented the result [8], finding rare arXiv:2104.01215v1 [cs.SI] 2 Apr 2021 agreement on ambiguous statements. Hossain et. al trained word representations of manually classified COVID-19 tweets before performing automatic classifica- tion using ROBERTa and BERTScore [6]. This paper classifies coronavirus-related fact-checks into story clusters and empirically analyse their characteristics. -
Evolution of Disease Transmission During the COVID-19
www.nature.com/scientificreports OPEN Evolution of disease transmission during the COVID‑19 pandemic: patterns and determinants Jie Zhu & Blanca Gallego* Epidemic models are being used by governments to inform public health strategies to reduce the spread of SARS‑CoV‑2. They simulate potential scenarios by manipulating model parameters that control processes of disease transmission and recovery. However, the validity of these parameters is challenged by the uncertainty of the impact of public health interventions on disease transmission, and the forecasting accuracy of these models is rarely investigated during an outbreak. We ftted a stochastic transmission model on reported cases, recoveries and deaths associated with SARS‑CoV‑2 infection across 101 countries. The dynamics of disease transmission was represented in terms of the daily efective reproduction number ( Rt ). The relationship between public health interventions and Rt was explored, frstly using a hierarchical clustering algorithm on initial Rt patterns, and secondly computing the time‑lagged cross correlation among the daily number of policies implemented, Rt , and daily incidence counts in subsequent months. The impact of updating Rt every time a prediction is made on the forecasting accuracy of the model was investigated. We identifed 5 groups of countries with distinct transmission patterns during the frst 6 months of the pandemic. Early adoption of social distancing measures and a shorter gap between interventions were associated with a reduction on the duration of outbreaks. The lagged correlation analysis revealed that increased policy volume was associated with lower future Rt (75 days lag), while a lower Rt was associated with lower future policy volume (102 days lag). -
COVID-19 Clinical Update
COVID-19 Clinical Update Jonathan Vilasier Iralu, MD, FACP Indian Health Service Chief Clinical Consultant for Infectious Diseases Disclosures Virology: Denmark Cluster 5 Variant WHO Outbreak News November 6, 2020 214 human cases of COVID-19 associated with farmed minks 12 cases had unique “Cluster 5” variant with new mutations not seen before. Cluster 5 has decreased sensitivity to neutralizing antibodies. Human impact is being studied. 17 million minks were to be culled https://www.who.int/csr/don/06-november-2020-mink-associated-sars-cov2-denmark/en/ Immunology Humoral Response to SARS-CoV-2 in Iceland: (Gudbjartsson et al, NEJM. 10/129/2020) Studied sera from 30,576 people in Iceland with 6 assays: pan-Ig assays: nucleoprotein and receptor binding domain of spike protein. (IgM, IgG, IgA) IgG/IgM against N, IgG/IgA against S1 spike protein 1797 patients recovered from COVID-19, 4222 were under quarantine and 23,452 had no exposure 91% of recovered patients still had antibody two months after the diagnosis by PCR and remained positive up to 4 months Risk of death in Iceland was only 0.03% Serology results show and estimated 44% of people with SARS-CoV-2 infection were not diagnosed by PCR https://www.nejm.org/doi/full/10.1056/NEJMoa2026116 Epidemiology SARS CoV-2 seroprevalence and transmission risk factors among high-risk close contacts (Ng et al, Lancet 11/2/2020) Retrospective cohort study of all close contacts of COVID cases in Singapore January-April 2020 Identified 7770 Close Contacts. (1863 household, 2319 work, -
COVID-19 Situation and Strategic Response
COVID-19 Situation and Strategic Response REPORT TO THE EXECUTIVE BOARD GLOBAL EPIDEMIOLOGICAL TREND As of 17 Jan 2021 6 000 000 100 000 Americas South-East Asia 90 000 5 000 000 Europe 80 000 Eastern Mediterranean 70 000 4 000 000 Africa Western Pacific 60 000 3 000 000 Deaths 50 000 Cases Deaths 40 000 2 000 000 30 000 20 000 1 000 000 10 000 0 0 30-Dec 20-Jan 10-Feb 02-Mar 23-Mar 13-Apr 04-May 25-May 15-Jun 06-Jul 27-Jul 17-Aug 07-Sep 28-Sep 19-Oct 09-Nov 30-Nov 21-Dec 11-Jan Reported week commencing 28 Oct: 5th IHR EC; 5 Nov: TORs for Global ~43 million cases, Study of Origins of 14 Dec: UK 18 Dec: RSA 1.16 million deaths SARS-CoV-2 published; highlights concern 31 Dec: WHO issue first emergency 18 Jan: report detection Denmark report new over variant VOC use validation of COVID-19 vaccine 12 Jan: International 93 million cases, of new variant mink-associated variant 202012/01 (Pfizer/BioNTech Comirnaty) mission on virus >2 million deaths 501Y.V2 origins travels to China * Data are incomplete for the current week. Cases depicted by bars; deaths depicted by line. WEEKLY SITUATION BY WHO REGION As of 17 Jan 2021 COVID Data Source: World Health Organization | United Nations Population Division (Population Prospect 2020) AGE & GENDER DISTRIBUTION: CASES & DEATHS COVID Gender Female Male Age groups Cases Deaths Cases 49% 51% 0-4 1.4% 0.10% Deaths 43% 57% 5-14 3.8% 0.05% 15-24 11.7% 0.21% 25-64 63.2% 16,16% 65-84 20% 83.48% AGE DISTRIBUTION OF CASES & DEATHS OVER TIME COVID Cases Deaths Data Source: World Health Organization case report -
SARS-Cov-2 Genome Surveillance in Mainz, Germany, Reveals Convergent Origin of the N501Y Spike Mutation in a Hospital Setting
medRxiv preprint doi: https://doi.org/10.1101/2021.02.11.21251324; this version posted February 12, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . SARS-CoV-2 genome surveillance in Mainz, Germany, reveals convergent origin of the N501Y spike mutation in a hospital setting Lemmermann NA1,2#$, Lieb B3#, Laufs T3, Renzaho A2, Runkel S1,4, Kohnen W1,5, Linke M1,6, Gerber S1,6, Schweiger S1,6, Michel A1,7, Bikar S-E1,3*, Plachter B1,2*§, Hankeln T1,8* 1 SARS-CoV-2 Sequencing Consortium Mainz, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany 2 Institute for Virology and Research Center for Immunotherapy (FZI), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany 3 StarSEQ GmbH, Mainz, Germany 4 Transfusion Unit & Test Center, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany 5 Department of Hygiene and Infection Prevention, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany 6 Institute of Human Genetics, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany 7 Medical Management Department, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany 8 Faculty of Biology, Institute of Organismal and Molecular Evolution, Molecular Genetics & Genome Analysis, Johannes Gutenberg University Mainz, Mainz, Germany * joint senior authors # equal contribution $ correspondence to NL: [email protected] Niels A. -
SARS-Cov-2 Variants ACKNOWLEDGEMENTS
SARS-CoV-2 variants ACKNOWLEDGEMENTS This report was developed by PHG Foundation for FIND (the Foundation for Innovative New Diagnostics). The work was supported by Unitaid and UK aid from the British people. We would like to thank all those who contributed to the development and review of this report. Lead writers Chantal Babb de Villiers (PHG Foundation) Laura Blackburn (PHG Foundation) Sarah Cook (PHG Foundation) Joanna Janus (PHG Foundation) Reviewers Devy Emperador (FIND) Jilian Sacks (FIND) Marva Seifert (FIND/UCSD) Anita Suresh (FIND) Swapna Uplekar (FIND) Publication date: 11 March 2021 URLs correct as of 4 March 2021 SARS-CoV-2 variants CONTENTS 1 Introduction ............................................................................................................................ 3 2 SARS-CoV-2 variants and mutations .................................................................................... 3 2.1 Definitions of variants of concern ...................................................................................... 4 2.2 Initial variants of concern identified ................................................................................... 5 2.3 Variants of interest ............................................................................................................ 7 3 Impact of variants on diagnostics ........................................................................................ 7 3.1 Impact of variants of concern on diagnostics ................................................................... -
Evolution of Disease Transmission During the Course of SARS-COV-2: Patterns and Determinants
Evolution of disease transmission rate during the course of SARS-COV-2: Patterns and determinants Jie Zhu UNSW Sydney https://orcid.org/0000-0002-1863-9262 Blanca Gallego ( [email protected] ) UNSW Sydney Research Article Keywords: Public Health Intervention, Stochastic Model, Epidemic Modelling Posted Date: April 7th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-44647/v2 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Version of Record: A version of this preprint was published on May 26th, 2021. See the published version at https://doi.org/10.1038/s41598-021-90347-8. Evolution of disease transmission during the course of SARS-COV-2: patterns and determinants Jie Zhu, Blanca Gallego∗ Centre for Big Data Research in Health, UNSW Sydney, Sydney, 2052, NSW, Australia Abstract Background This has been the first time in recent history when extreme measures that have deep and wide impact on our economic and social systems, such as lock downs and border closings, have been adopted at a global scale. These measures have been taken in response to the severe acute respiratory syndrome coronavirus SARS-CoV-2 pandemic, declared a Public Health Emergency of International Concern on 30 January 2020. Epidemic models are being used by governments across the world to inform social distancing and other public health strategies to reduce the spread of the virus. These models, which vary widely in their complexity, simulate interventions by manipulating model parameters that control social mixing, healthcare provision and other behavioral and environmental processes of disease transmission and recovery. -
Natural History of SARS-Cov-2 Infection Detection, Burden
February 19, 2021 Edition 2021-02-19 (77) *** Available on-line at https://www.cdc.gov/library/covid19 *** Section headings in the COVID-19 Science Update align with the CDC Science Agenda for COVID-19. Natural History of SARS-CoV-2 Infection PEER -REVIEWED Rapid decline of neutralizing antibodies against SARS-CoV-2 among infected healthcare workers. Marot et al, Nature Communications, (February 8, 2021). Key findings: • Healthcare workers (HCWs) infected with mild symptomatic SARS-CoV-2 infection experienced changes in IgA profiles that were associated with declining neutralizing activity 2 months after symptom onset. • Anti-receptor binding domain (RBD) IgA decreased from 100% at baseline to 38.5% at 3 months, but anti- RBD immunoglobulin G (IgG) remained relatively stable (100% at baseline to 92.3% at 3 months). • Significant concomitant decreases in neutralizing antibody titers (NAbs) were observed between day 21 and 2 months and 3 months, respectively, suggesting that NAbs correlate with IgA. Methods: Observational study of serum samples of 26 HCW recovering from mild, RT-PCR confirmed SARS-CoV2 infection to assess the development of protective antibody response and duration. Changes in levels of IgG, IgA, and IgM antibodies and NAbs assessed at 3 time points post-symptom onset: day 21, 2 months, and 3 months. Limitations: Small sample size. Implications: Short-lasting immune protection suggests prevention and control measures should be maintained among seropositive HCWs. Detection, Burden, and Impact PEER-REVIEWED Emergence of a novel SARS-CoV-2 variant in Southern California. Zhang et al. JAMA (February 11, 2021). Key findings: • In California, diverse lineages with 2 main clusters, the small 20G cluster (22%) and a larger, novel SARS- CoV-2 variant (CAL.20C) cluster (36%), have been identified. -