The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

Status of COVID-19 cases in Ontario

This dataset compiles daily snapshots of publicly reported data on 2019 Novel Coronavirus (COVID-19) testing in Ontario.

Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak.

Effective April 13, 2023, this dataset will be discontinued. The public can continue to access the data within this dataset in the following locations updated weekly on the Ontario Data Catalogue:

• Ontario COVID-19 testing percent positive by age group
• Confirmed positive cases of COVID-19 in Ontario
• Ontario COVID-19 testing metrics by Public Health Unit (PHU)
• Ontario COVID-19 testing percent positive by age group
• COVID-19 cases in hospital and ICU, by Ontario Health (OH) region
• Cumulative deaths (new methodology)
• Deaths Involving COVID-19 by Fatality Type

For information on Long-Term Care Home COVID-19 Data, please visit: Long-Term Care Home COVID-19 Data.

Data includes:

• reporting date
• daily tests completed
• total tests completed
• test outcomes
• total case outcomes (resolutions and deaths)
• current tests under investigation
• current hospitalizations
  • current patients in Intensive Care Units (ICUs) due to COVID-related critical Illness
  • current patients in Intensive Care Units (ICUs) testing positive for COVID-19
  • current patients in Intensive Care Units (ICUs) no longer testing positive for COVID-19
  • current patients in Intensive Care Units (ICUs) on ventilators due to COVID-related critical illness
  • current patients in Intensive Care Units (ICUs) on ventilators testing positive for COVID-19
  • current patients in Intensive Care Units (ICUs) on ventilators no longer testing positive for COVID-19
• Long-Term Care (LTC) resident and worker COVID-19 case and death totals
• Variants of Concern case totals
• number of new deaths reported (occurred in the last month)
• number of historical deaths reported (occurred more than one month ago)
• change in number of cases from previous day by Public Health Unit (PHU).

This dataset is subject to change. Please review the daily epidemiologic summaries for information on variables, methodology, and technical considerations.

Cumulative Deaths

**Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool **

The methodology used to count COVID-19 deaths has changed to exclude deaths not caused by COVID. This impacts data captured in the columns “Deaths”, “Deaths_Data_Cleaning” and “newly_reported_deaths” starting with data for March 11, 2022. A new column has been added to the file “Deaths_New_Methodology” which represents the methodological change.

The method used to count COVID-19 deaths has changed, effective December 1, 2022. Prior to December 1, 2022, deaths were counted based on the date the death was updated in the public health unit’s system. Going forward, deaths are counted on the date they occurred.

On November 30, 2023 the count of COVID-19 deaths was updated to include missing historical deaths from January 15, 2020 to March 31, 2023. A small number of COVID deaths (less than 20) do not have recorded death date and will be excluded from this file.

CCM is a dynamic disease reporting system which allows ongoing update to data previously entered. As a result, data extracted from CCM represents a snapshot at the time of extraction and may differ from previous or subsequent results. Public Health Units continually clean up COVID-19 data, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes and current totals being different from previously reported cases and deaths. Observed trends over time should be interpreted with caution for the most recent period due to reporting and/or data entry lags.

Related dataset(s)

• Confirmed positive cases of COVID-19 in Ontario

DISCLAIMER

Dataset consists of historical data of pre-pandemic period and doesn’t represent the current reality which may have changed due to the spikes in demand. This dataset has been generated in collaboration of efforts within CoronaWhy community.

Context

Last updated: April 26th 2020 Updates: April 14th 2020 - Added missing population data April 15th 2020 - Added Brazil statewise ICU hospital beds dataset April 21th 2020 - Added Italy, Spain statewise ICU hospital beds dataset, India statewise TOTAL hospital beds dataset April 26th 2020 - Added Sweden ICU(2019) and TOTAL(2018) beds datasets

Purpose of the dataset

I am trying to produce a dataset that will provide a foundation for policymakers to understand the realistic capacity of healthcare providers being able to deal with the spikes in demand for intensive care. As a way to help, I’ve prepared a dataset of beds across countries and states. Work in progress dataset that should and will be updated as more data becomes available and public on weekly basis.

Importance

This dataset is intended to be used as a baseline for understanding the typical bed capacity and coverage globally. This information is critical for understanding the impact of a high utilization event, like COVID-19.

Current challenges

Datasets are scattered across the web and are very hard to normalize, I did my best but help would be much appreciated.

Data sources / Acknowledgments

arcgis (USA) - https://services1.arcgis.com/Hp6G80Pky0om7QvQ/arcgis/rest/services/Hospitals_1/FeatureServer/0 KHN (USA) - https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds/ datahub.io (World) - https://datahub.io/world-bank/sh.med.beds.zs eurostat - https://data.europa.eu/euodp/en/data/dataset/vswUL3c6yKoyahrvIRyew OECD - https://data.oecd.org/healtheqt/hospital-beds.htm WDI (World) - https://data.worldbank.org/indicator/SH.MED.BEDS.ZS NHP(India) - http://www.cbhidghs.nic.in/showfile.php?lid=1147 data.gov.sg (Singapore) - https://data.gov.sg/dataset/health-facilities?view_id=91b4feed-dcb9-4720-8cb0-ac2f04b7efd0&resource_id=dee5ccce-4dfb-467f-bcb4-dc025b56b977 dati.salute.gov.it (Italy)- http://www.dati.salute.gov.it/dati/dettaglioDataset.jsp?menu=dati&idPag=96 portal.icuregswe.org (Sweden) - https://portal.icuregswe.org/seiva/en/Rapport publications: Intensive Care Medicine Journal (Europe) - https://link.springer.com/article/10.1007/s00134-012-2627-8 Critical Care Medicine Journal (Asia) - https://www.researchgate.net/figure/Number-of-critical-care-beds-per-100-000-population_fig1_338520008 Medicina Intensiva (Spain) - https://www.medintensiva.org/en-pdf-S2173572713000878 news: https://lanuovaferrara.gelocal.it/italia-mondo/cronaca/2020/03/19/news/dietro-la-corsa-a-nuovi-posti-in-terapia-intensiva-gli-errori-del-passato-1.38611596 kaggle: germany - https://www.kaggle.com/manuelblechschmidt/icu-beds-in-germany brazil (IBGE) - https://www.kaggle.com/thiagobodruk/brazilianstates Manual population data search from wiki

Data columns

country,state,county,lat,lng,type,measure,beds,population,year,source,source_url - country - country of origin, if present - state - more granular location, if present - lat - latitude - lng - longtitude - type - [TOTAL, ICU, ACUTE(some data could include ICU beds too), PSYCHIATRIC, OTHER(merged ‘SPECIAL’, ‘CHRONIC DISEASE’, ‘CHILDREN’, ‘LONG TERM CARE’, ‘REHABILITATION’, ‘WOMEN’, ‘MILITARY’] - measure - type of measure (per 1000 inhabitants) - beds - number of beds per 1000 - population - population of location based on multiple sources and wikipedia - year - source year for beds and population data - source - source of data - source_url - URL of the original source

Files

for each of datasource: hospital_beds_per_source.csv

US only: US arcgis + khn (state/county granularity): hospital_beds_USA.csv

Global (state(region)/county granularity): hospital_beds_global_regional.csv

Global (country granularity): hospital_beds_global_v1.csv

Contributors

Igor Kiulian - extracting/normalizing/formatting/merging data Artur Kiulian - helped with Kaggle setup Augaly S. Kiedi - helped with country population data Kristoffer Jan Zieba - found Swedish data sources

Possible Improvements

Find and megre more detailed (state/county wise) or newer datasource

The COVID-19 dashboard includes data on city/town COVID-19 activity, confirmed and probable cases of COVID-19, confirmed and probable deaths related to COVID-19, and the demographic characteristics of cases and deaths.

The ongoing COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in more than 28,000,000 infections and 900,000 deaths worldwide to date. Antibody development efforts mainly revolve around the extensively glycosylated SARS-CoV-2 spike (S) protein, which mediates host cell entry by binding to the angiotensin-converting enzyme 2 (ACE2). Similar to many other viral fusion proteins, the SARS-CoV-2 spike utilizes a glycan shield to thwart the host immune response. Here, we built a full-length model of the glycosylated SARS-CoV-2 S protein, both in the open and closed states, augmenting the available structural and biological data. Multiple microsecond-long, all-atom molecular dynamics simulations were used to provide an atomistic perspective on the roles of glycans and on the protein structure and dynamics. We reveal an essential structural role of N-glycans at sites N165 and N234 in modulating the conformational dynamics of the spike’s receptor binding domain (RBD), which is responsible for ACE2 recognition. This finding is corroborated by biolayer interferometry experiments, which show that deletion of these glycans through N165A and N234A mutations significantly reduces binding to ACE2 as a result of the RBD conformational shift toward the “down” state. Additionally, end-to-end accessibility analyses outline a complete overview of the vulnerabilities of the glycan shield of the SARS-CoV-2 S protein, which may be exploited in the therapeutic efforts targeting this molecular machine. Overall, this work presents hitherto unseen functional and structural insights into the SARS-CoV-2 S protein and its glycan coat, providing a strategy to control the conformational plasticity of the RBD that could be harnessed for vaccine development.

PDBs were generated using molecular dynamics. See DESRES_README.txt for more details on molecular dynamics simulation. PDBs were converted to volumetric data using EMAN2. The image stack contains 100 000 projection images each of the 10 states (see PDBs), at an SNR of 1/10 in the following order: state00 (closed) state01 (closed) state02 (closed) state10 (intermediate) state11 (intermediate) state12 (intermediate) state13 (intermediate) state20 (open) state21 (open) state22 (open) Projections were made using relion_project. White gaussian noise with standard deviation 1.0 CTF multiplied signal High signal-to-noise ratio Image size 96x96x96 MRC-files used for the projections not included, but can be generated using the PDB files. Final RELION reconstruction resolution is 5.33334 Angstrom (Nyqvist is at 5.33334). Command line for RELION reconstruction: relion_refine_mpi --o refine3d/run --auto_refine --split_random_halves --i rot_trans_ctf_noise/stack.star --ref pdb2mrc/state21.mrc --ini_high 20 --dont_combine_weights_via_disc --preread_images --pool 30 --pad 2 --ctf --particle_diameter 130 --flatten_solvent --zero_mask --oversampling 1 --healpix_order 2 --auto_local_healpix_order 4 --offset_range 5 --offset_step 2 --low_resol_join_halves 40 --norm --scale --j 2 --gpu --fristiter_cc --grad This dataset is generated as a testbed for cryo-EM heterogeneity analysis.

This dataset reports the daily reported number of the 7-day moving average rates of Deaths involving COVID-19 by vaccination status and by age group. Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak. Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool Data includes: * Date on which the death occurred * Age group * 7-day moving average of the last seven days of the death rate per 100,000 for those not fully vaccinated * 7-day moving average of the last seven days of the death rate per 100,000 for those fully vaccinated * 7-day moving average of the last seven days of the death rate per 100,000 for those vaccinated with at least one booster ##Additional notes As of June 16, all COVID-19 datasets will be updated weekly on Thursdays by 2pm. As of January 12, 2024, data from the date of January 1, 2024 onwards reflect updated population estimates. This update specifically impacts data for the 'not fully vaccinated' category. On November 30, 2023 the count of COVID-19 deaths was updated to include missing historical deaths from January 15, 2020 to March 31, 2023. CCM is a dynamic disease reporting system which allows ongoing update to data previously entered. As a result, data extracted from CCM represents a snapshot at the time of extraction and may differ from previous or subsequent results. Public Health Units continually clean up COVID-19 data, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes and current totals being different from previously reported cases and deaths. Observed trends over time should be interpreted with caution for the most recent period due to reporting and/or data entry lags. The data does not include vaccination data for people who did not provide consent for vaccination records to be entered into the provincial COVaxON system. This includes individual records as well as records from some Indigenous communities where those communities have not consented to including vaccination information in COVaxON. “Not fully vaccinated” category includes people with no vaccine and one dose of double-dose vaccine. “People with one dose of double-dose vaccine” category has a small and constantly changing number. The combination will stabilize the results. Spikes, negative numbers and other data anomalies: Due to ongoing data entry and data quality assurance activities in Case and Contact Management system (CCM) file, Public Health Units continually clean up COVID-19, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes, negative numbers and current totals being different from previously reported case and death counts. Public Health Units report cause of death in the CCM based on information available to them at the time of reporting and in accordance with definitions provided by Public Health Ontario. The medical certificate of death is the official record and the cause of death could be different. Deaths are defined per the outcome field in CCM marked as “Fatal”. Deaths in COVID-19 cases identified as unrelated to COVID-19 are not included in the Deaths involving COVID-19 reported. Rates for the most recent days are subject to reporting lags All data reflects totals from 8 p.m. the previous day. This dataset is subject to change.

The emergence of several SARS-CoV-2 lineages presenting adaptive mutations is a matter of concern worldwide due to their potential ability to increase transmission and/or evade the immune response. While performing epidemiological and genomic surveillance of SARS-CoV-2 in samples from Porto Ferreira—São Paulo—Brazil, we identified sequences classified by pangolin as B.1.1.28 harboring Spike L452R mutation, in the RBD region. Phylogenetic analysis revealed that these sequences grouped into a monophyletic branch, with others from Brazil, mainly from the state of São Paulo. The sequences had a set of 15 clade defining amino acid mutations, of which six were in the Spike protein. A new lineage was proposed to Pango and it was accepted and designated P.4. In samples from the city of Porto Ferreira, P.4 lineage has been increasing in frequency since it was first detected in March 2021, corresponding to 34.7% of the samples sequenced in June, the second in prevalence after P.1. Also, it is circulating in 30 cities from the state of São Paulo, and it was also detected in one sample from the state of Sergipe and two from the state of Rio de Janeiro. Further studies are needed to understand whether P.4 should be considered a new threat.

CDC is collaborating with Vitalant Research Institute, American Red Cross, and Westat Inc. to conduct a nationwide COVID-19 seroprevalence survey of blood donors. De-identified blood samples are tested for antibodies to SARS-CoV-2 to better understand the percentage of people in the United States who have antibodies against SARS-CoV-2 (the virus that causes COVID-19) and to track how this percentage changes over time. Both SARS-CoV-2 infection and COVID-19 vaccines currently used in the United States result in production of anti-spike (anti-S) antibodies but only infection results in production of anti-nucleocapsid (anti-N) antibodies. Infection-induced seroprevalence estimates the proportion of the population with antibody evidence of previous SARS-CoV-2 infection and refers to the percent of the population with anti-nucleocapsid antibodies. Combined infection-Induced and Vaccination-Induced seroprevalence estimates the proportion of the population with antibody evidence of previous SARS-CoV-2 infection, COVID-19 vaccination, or both, and refers to the percent of the population that has anti-spike antibodies, anti-nucleocapsid antibodies, or both. This link connects to a webpage that displays the data from the Nationwide Blood Donor Seroprevalence Survey. It offers an interactive visualization available at https://covid.cdc.gov/covid-data-tracker/#nationwide-blood-donor-seroprevalence-2022

The COVID-19 pandemic has spread worldwide. However, as soon as the first vaccines—the only scientifically verified and efficient therapeutic option thus far—were released, mutations combined into variants of SARS-CoV-2 that are more transmissible and virulent emerged, raising doubts about their efficiency. This study aims to explain possible molecular mechanisms responsible for the increased transmissibility and the increased rate of hospitalizations related to the new variants. A combination of theoretical methods was employed. Constant-pH Monte Carlo simulations were carried out to quantify the stability of several spike trimeric structures at different conformational states and the free energy of interactions between the receptor-binding domain (RBD) and angiotensin-converting enzyme II (ACE2) for the most worrying variants. Electrostatic epitopes were mapped using the PROCEEDpKa method. These analyses showed that the increased virulence is more likely to be due to the improved stability to the S trimer in the opened state, in which the virus can interact with the cellular receptor, ACE2, rather than due to alterations in the complexation RBD-ACE2, since the difference observed in the free energy values was small (although more attractive in general). Conversely, the South African/Beta variant (B.1.351), compared with the SARS-CoV-2 wild type (wt), is much more stable in the opened state with one or two RBDs in the up position than in the closed state with three RBDs in the down position favoring the infection. Such results contribute to understanding the natural history of disease and indicate possible strategies for developing new therapeutic molecules and adjusting the vaccine doses for higher B-cell antibody production.

Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, has emerged as a global pandemic worldwide. In this study, we used ARTIC primers–based amplicon sequencing to profile 225 SARS-CoV-2 genomes from India. Phylogenetic analysis of 202 high-quality assemblies identified the presence of all the five reported clades 19A, 19B, 20A, 20B, and 20C in the population. The analyses revealed Europe and Southeast Asia as two major routes for introduction of the disease in India followed by local transmission. Interestingly, the19B clade was found to be more prevalent in our sequenced genomes (17%) compared to other genomes reported so far from India. Haplotype network analysis showed evolution of 19A and 19B clades in parallel from predominantly Gujarat state in India, suggesting it to be one of the major routes of disease transmission in India during the months of March and April, whereas 20B and 20C appeared to evolve from 20A. At the same time, 20A and 20B clades depicted prevalence of four common mutations 241 C > T in 5′ UTR, P4715L, F942F along with D614G in the Spike protein. D614G mutation has been reported to increase virus shedding and infectivity. Our molecular modeling and docking analysis identified that D614G mutation resulted in enhanced affinity of Spike S1–S2 hinge region with TMPRSS2 protease, possibly the reason for increased shedding of S1 domain in G614 as compared to D614. Moreover, we also observed an increased concordance of G614 mutation with the viral load, as evident from decreased Ct value of Spike and the ORF1ab gene.

This file contains a list of sequences from GISAID’s EpiFlu Database on which this research is based and their corresponding authors and laboratories. (CSV)

Mapping ISM sites to the reference viral genome.

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by beta-coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that has rapidly spread across the globe starting from February 2020. It is well established that during viral infection, extracellular vesicles become delivery/presenting vectors of viral material. However, studies regarding extracellular vesicle function in COVID-19 pathology are still scanty. Here, we performed a comparative study on exosomes recovered from the plasma of either MILD or SEVERE COVID-19 patients. We show that although both types of vesicles efficiently display SARS-CoV-2 spike-derived peptides and carry immunomodulatory molecules, only those of MILD patients are capable of efficiently regulating antigen-specific CD4+ T-cell responses. Accordingly, by mass spectrometry, we show that the proteome of exosomes of MILD patients correlates with a proper functioning of the immune system, while that of SEVERE patients is associated with increased and chronic inflammation. Overall, we show that exosomes recovered from the plasma of COVID-19 patients possess SARS-CoV-2-derived protein material, have an active role in enhancing the immune response, and possess a cargo that reflects the pathological state of patients in the acute phase of the disease.

The dataset contains a total of 40 snapshots of the four trajectories (10 snapshots each system = two per replica x 5 replicas/system):

SARS-CoV-2002 spike protein without ACE2

SARS-CoV-2 spike protein without ACE2

SARS-CoV-2002 spike protein with ACE2

SARS-CoV-2 spike protein with ACE2

Molecular dynamics simulation trajectories (320ns each) have been performed using the Amber ff14SB force field running with the Amber18 package at the the NSF-funded (OAC-1826915, OAC-1828163) ELSA high performance computing cluster at The College of New Jersey. Under the following simulation methodology:

All-atom simulations were carried out with Amber18 (ambermd.org), and system components (protein, ions, water) were modeled with the included FF14SB and TIP3P parameter sets. Energy minimization used CPU pmemd, while later simulation stages used GPU pmemd. CoV2 and CoV1 systems with one RBD up (with/without ACE2) were solvated in 12 angstrom water shells. Cysteine residues identified in the initial models as having a disulfide bond (DB) were bonded using tLeap. All simulations used 0.150 M NaCl. Hydrogen mass repartitioning was applied only to the protein to enable a 4 fs timestep (https://pubs.acs.org/doi/abs/10.1021/ct5010406). The SHAKE algorithm was applied to hydrogens, and a real-space cutoff of 8 angstroms was used. Periodic boundary conditions were applied and PME was used for long-range electrostatics. Minimization was by steepest descent (2000 steps) followed by conjugate gradient (3000 steps). Heating used two stages: (1) NVT heating from 0 K to 100 K (50 ps), and (2) NPT heating from 100 K to 300 K (100 ps). Restraints of 10 kcal mol-1 angstrom-2 were applied during minimization and heating to C-alpha atoms. During 6 ns of equilibration at 300 K C-alpha restraints were gradually reduced from 10 kcal mol-1 angstrom-2 to 0.1 kcal mol-1 angstrom-2. Finally, restraints were released and 320 ns unrestrained production simulations were carried out for CoV2 and CoV1 systems. Production simulations began from the final equilibrated snapshots, and five copies of each system were simulated. As unrestrained systems can freely rotate we monitored simulations for any close contacts and found that in one copy of the CoV1 simulation without ACE2 and one RBD up that a few contacts close to 8 angstrom occur near the end of the 320 ns between the RBD and a different subdomain of the spike complex in a periodic image. However this did not influence analyzed structural properties which is verified by comparing results across simulations. The Monte Carlo barostat was used to maintain pressure (1 atm), and the Langevin thermostat was used to maintain 300 K temperature (collision frequency 1 ps-1), as implemented in Amber18. In aggregate, nearly 7 microseconds of simulation of systems ranging from 396,147 to 879,100 atoms was carried out for this work. For further details on the trajectories, please contact Joseph Baker (bakerj@tcnj.edu).

Regarding the contact map analysis scripts (contactMaps_Analysis.tar.gz), they contain the following workflow:

contactmap --> source files from contact_map executable process_nc.sh --> convert raw data from all-atom simulation to numbered PDB files and get the contact maps frequency.lua --> read a set of PDB files and output the frequency count for each contact consensus.fasta --> align sequence of Covid19 and SARS from Chimera consensus.lua --> read data previously generated and compute the frequency per residue, among other things. consensus.sh --> input information to consensus.lua consensus.gp --> gnuplot script to plot figures

This dataset and the code is part of tripartite collaboration between:

The Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland (supported by the National Science Centre, Poland, under grant No. 2017/26/D/NZ1/0046)

Department of Chemistry, The College of New Jersey, New Jersey, United States (supported by National Science Foundation under grant numbers OAC-1826915 and OAC-1828163).

Jozef Stefan Institute, Ljubljana, Slovenia (supported by the Slovenian Research Agency (Funding No. P1-0055)).

IntroductionResearch has confirmed the safety and comparable seroconversion rates following SARS-CoV-2 vaccination in patients with solid cancers. However, the impact of cancer treatment on vaccine-induced T cell responses remains poorly understood.MethodsIn this study, we expand on previous findings within the VOICE trial by evaluating the functional and phenotypic composition of mRNA-1273-induced T cell responses in patients with solid tumors undergoing immunotherapy, chemotherapy, or both, compared to individuals without cancer. We conducted an ELISpot analysis on 386 participants to assess spike-specific T cell responses 28 days after full vaccination. Further in-depth characterization of using flow cytometry was performed on a subset of 63 participants to analyze the functional phenotype and differentiation state of spike-specific T cell responses.ResultsELISpot analysis showed robust induction of spike-specific T cell responses across all treatment groups, with response rates ranging from 75% to 80%. Flow cytometry analysis revealed a distinctive cytokine production pattern across cohorts, with CD4 T cells producing IFNγ, TNF, and IL-2, and CD8 T cells producing IFNγ, TNF, and CCL4. Variations were observed in the proportion of monofunctional CD4 T cells producing TNF, particularly higher in individuals without cancer and patients treated with chemotherapy alone, while those treated with immunotherapy or chemoimmunotherapy predominantly produced IFNγ. Despite these differences, polyfunctional spike-specific memory CD4 and CD8 T cell responses were comparable across cohorts. Notably, immunotherapy-treated patients exhibited an expansion of spike-specific CD4 T cells with a terminally differentiated effector memory phenotype.DiscussionThese findings demonstrate that systemic treatment in patients with solid tumors does not compromise the quality of polyfunctional mRNA-1273-induced T cell responses. This underscores the importance of COVID-19 vaccination in patients with solid cancers undergoing systemic treatment.

Since the COVID-19 outbreak emerged, SARS-CoV-2 has continuously evolved into variants with underlying mutations associated with increased transmissibility, potential escape from neutralizing antibodies, and disease severity. Although intensive research is ongoing around the world to understand the mutational patterns of the virus, there are concerns about the potential to alter the dynamics and the resultant public health impact. The SARS-CoV-2 pandemic in South Africa has been characterized by periods of infections with four major epidemic waves. Here, we report on the genomic epidemiology of SARS-CoV-2 variants circulating in the Free State province in each of the four waves during the 2020-2021 genomic surveillance period. For analysis of the circulating variants, a total of 1290 samples from qPCR confirmed SARS-CoV-2 positive individuals were subjected to viral RNA extraction, genomic amplification, and sequencing. Variant assignment of the viral sequences and mutation identification were conducted using PANGOLIN and SARS-CoV-2 genome annotator, respectively. Our analysis revealed that during the initial part of the first wave, B.1, B.1.1, B.1.1.53, B.1.1.448 and B.1.237 circulated in the Free State province, followed by Beta variant, B.1.351 later in the wave. Although most of the initially detected variants disappeared during the second wave, the Beta variant, B.1.351, persisted. Early in the third wave, the Beta variant, B.1.351, predominated but was replaced by the Delta sub-lineage, AY.45. The fourth wave was characterized by unique emergence of the Omicron sub-variant, BA.1. The data further indicates that SARS-CoV-2 in the Free State accumulated amino acid mutations on the spike protein across the four waves of infections. Each wave of infection was driven by a unique combination of SARS-CoV-2 variants. Findings from this study highlight the importance of continued genomic surveillance and monitoring of the circulating SARS-CoV-2 variants to inform public health efforts and ensure adequate control of the ongoing pandemic.

Map between 20-NT ISM and 11-NT compressed ISM.

IntroductionWe sought to determine pre-infection correlates of protection against SARS-CoV-2 post-vaccine inzfections (PVI) acquired during the first Omicron wave in the United States.MethodsSerum and saliva samples from 176 vaccinated adults were collected from October to December of 2021, immediately before the Omicron wave, and assessed for SARS-CoV-2 Spike-specific IgG and IgA binding antibodies (bAb). Sera were also assessed for bAb using commercial assays, and for neutralization activity against several SARS-CoV-2 variants. PVI duration and severity, as well as risk and precautionary behaviors, were assessed by questionnaires.ResultsSerum anti-Spike IgG levels assessed by research assay, neutralization titers against Omicron subvariants, and low home risk scores correlated with protection against PVIs after multivariable regression analysis. Commercial assays did not perform as well as research assay, likely due to their lower dynamic range.DiscussionIn the 32 participants that developed PVI, anti-Spike IgG bAbs correlated with lower disease severity and shorter duration of illness.

Excel spread sheet of the 9550 blood donors that were evaluated in this study broken into columns that shows the date of visit to the blood collection center, the State, the geographic region as east (E), west (W) or Kentucky (KY), the blood type, the age, gender, race and raw S protein ELISA OD value. (XLSX)