The California Department of Public Health (CDPH) is identifying ​the prevalence of circulating SARS-CoV-2 variants by analysing ​CDPH Genomic Surveillance Data and ​CalREDIE, CDPH's communicable disease reporting and surveillance system. Viruses mutate into new strains or variants over time. Some variants emerge and then disappear. Other variants become common and circulate for a long time. Several specialized laboratories state-wide sequence the genomes of a fraction of all positive COVID-19 tests to determine which variants are circulating. Sequencing and reporting of variant results takes several days after a test is identified as a positive for COVID-19. Not all ​viruses from positive COVID-19 tests are ​sequenced. Knowing what variants are circulating in California informs public health and clinical action.

Note: This dataset is no longer being updated due to the end of the COVID-19 Public Health Emergency.

The California Department of Public Health (CDPH) is identifying ​the prevalence of circulating SARS-CoV-2 variants by analyzing ​CDPH Genomic Surveillance Data and ​CalREDIE, CDPH's communicable disease reporting and surveillance system. Viruses mutate into new strains or variants over time. Some variants emerge and then disappear. Other variants become common and circulate for a long time. Several specialized laboratories statewide sequence the genomes of a fraction of all positive COVID-19 tests to determine which variants are circulating. Sequencing and reporting of variant results takes several days after a test is identified as a positive for COVID-19. Not all ​viruses from positive COVID-19 tests are ​sequenced. Knowing what variants are circulating in California informs public health and clinical action.

Note: There is a natural reporting lag in these data due to the time commitment to complete whole genome sequencing; therefore, a 14 day lag is applied to these datasets to allow for data completeness. Please note that more recent data should be used with caution.

For more information, please see: https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/COVID-19/COVID-Variants.aspx

This dataset includes three tables with the model-based projections and estimates as shown on CalCAT in 2025 (http://calcat.cdph.ca.gov) for California state, regions, and counties.

(1) COVID-19 Nowcasts includes the R-effective estimates for COVID-19 from the different models available for the past 80 days from the archive date and the median ensemble thereof.

(2) CalCAT Forecasts includes hospital census and admissions forecasts for COVID-19 and Influenza, and the corresponding ensemble metrics for a 4 week horizon from the archive date.

(3) Variant Proportion Nowcasts contains the Integrated Genomic Epidemiology Dataset (IGED)-based and Terra-based estimates of COVID-19 variants circulating over the past 3 months as well as model-based predictions for the proportions of the variants of concern for dates leading up to the archive date. Prediction intervals are included when available.

This dataset provides CalCAT users with programmatic access to the downloadable datasets on CalCAT.

This dataset also includes a zipped file with the historical archives of the COVID-19 Nowcasts, CalCAT Forecasts and Variant Proportion Nowcasts through 2023.

To characterize the genomic variation within a circulating variant and identifying potential mutations associated with breakthrough infection among persons with Delta variant SARS-CoV-2 infection

This dataset includes three tables with the model-based projections and estimates as shown on CalCAT in 2024 (http://calcat.cdph.ca.gov) for California state, regions, and counties. (1) COVID-19 Nowcasts includes the R-effective estimates for COVID-19 from the different models available for the past 80 days from the archive date and the median ensemble thereof. (2) CalCAT Forecasts includes hospital census and admissions forecasts for COVID-19 and Influenza, ICU census forecasts for COVID-19, and the corresponding ensemble metrics for a 4 week horizon from the archive date. (3) Variant Proportion Nowcasts contains the Integrated Genomic Epidemiology Dataset (IGED)-based estimates of COVID-19 variants circulating over the past 3 months as well as model-based predictions for the proportions of the variants of concern for dates leading up to the archive date. Prediction intervals are included when available. This dataset provides CalCAT users with programmatic access to the downloadable datasets on CalCAT. This dataset also includes a zipped file with the historical archives of the COVID-19 Nowcasts, CalCAT Forecasts and Variant Proportion Nowcasts through 2023.

To identify risk factors for severe clinical outcomes among persons with SARS-CoV-2 infection and persons with varying vaccination status for COVID-19 during periods of Omicron versus Delta variant circulation

Note: Date last updated is 2022-07-11, dataset is no longer provided.

Alberta is monitoring for variant strains of COVID-19 that have a higher infection rate. Case numbers are updated every weekday.

Data prior to 2021-03-23 came from a table at https://www.alberta.ca/covid-19-alberta-data.aspx; from 2021-03-23 onwards the source is Table 13 at https://www.alberta.ca/stats/covid-19-alberta-statistics.htm#variants-of-concern.

This dataset was last updated 2022-07-13 16:08 with data as of end of day 2022-07-11.

The project is a collaborative effort of investigators from the University of California, Berkeley’s Innovative Genomics Institute (IGI) and School of Public Health (SPH); Kaiser Permanente Northern California (KPNC); and the California Department of Public Health (CDPH), with administrative and programmatic support provided by Heluna Health. Over the project period, the collaborating investigators will analyze approximately 35,000 genomes of SARS-CoV-2 specimens obtained from KPNC members and sequenced by the CDPH through its COVIDNet activities. By combining results from the genomic analysis of low-frequency alleles with clinical and epidemiologic data available in patient records, including demographic variables, COVID-19 vaccination status (dates of vaccination; number of doses; manufacturer), COVID-19 disease severity, and underlying medical conditions, we assessed which shared genomic variations are associated with a greater risk of symptomatic infection and severe clinical outcomes; COVID-19 vaccine effectiveness; and transmission of SARS-CoV-2 in the household. The project and its results can serve as a model for community-based monitoring of the evolution and spread of SARS-CoV-2 and use of the data to inform decisions about the formulation and use of COVID-19 vaccines, including booster doses and next-generation vaccines. Methods Sample collection Our samples are from Kaiser Northern California patients testing positive for SARS-CoV-2 starting June 1, 2021, and through the present. The RNA is sent to the California Department of Public Health (CDPH) lab to be sequenced by COVIDNet–a consortium of primarily UC system labs helping CDPH with the overflow and backlog of samples. Once the genomes have been sequenced, the lineage information and unique deidentified PAUI number are returned to Kaiser where this information is recorded. Metadata from this list of PAUI’s is sent weekly to UC Berkeley. The KPNC sequencing data is returned to us through a third party that is processing all CDPH genomes and stored on a server at UC Berkeley and matched with metadata using PAUI’s. Sequence analysis The raw sequencing data is processed through a SARS-CoV-2 analysis pipeline that has been modified for this work as follows. Adapter removal and trimming are performed using bbduk. The reads are then aligned to the Wuhan reference genome using minimap2 followed by primer trimming using iVAR . We next create a pileup file using samtools and use that input to create a consensus file. This consensus file is created with iVAR using a minimum depth of 10 reads and majority rule for base calling. We next use iVAR to call variants from the pileup file where we set the threshold for calling a mutation to be 0.01. This will call mutations for any loci where at least one percent of the reads are non-reference. This very low threshold allows us to capture all variation that is seen in the sequencing data. The list of variants is then annotated with the gene and amino acid change (if there is one), and whether the mutation is considered defining in any SARS-CoV-2 variants and whether that mutation is seen in only one variant. This dataset includes the fasta consensus sequences and mutation calls for each genome.

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

We are collaborating with other national regulatory authorities to align the requirements for evaluating, authorizing and post-market surveillance of variant COVID-19 vaccines as much as possible.

Variant strains of SARS-CoV-2 are emerging that may affect the level of protection provided by currently authorized COVID-19 vaccines. As a result, manufacturers are adapting authorized COVID-19 vaccines to provide protection against infection and disease caused by virus variants.

This Points to Consider document lays out a regulatory approach for updating authorised coronavirus vaccines should mutations at any time make them less efficacious due to insufficient cross-reactivity.

To investigate neutralization resistance of XBB.1.5, CH.1.1, and CA.3.1 variants after stimulation by three doses of mRNA vaccine or BA.4/5 wave infection.

Supplement of the paper
"Adaptive trends of sequence compositional complexity over pandemic time in the SARS-CoV-2 coronavirus”
During the spread of the COVID-19 pandemic, the SARS-CoV-2 coronavirus underwent mutation and recombination events that altered its genome compositional structure, thus providing an unprecedented opportunity to check an evolutionary process in real time. The mutation rate is known to be lower than expected for neutral evolution, suggesting natural selection and convergent evolution. We begin by summarizing the compositional heterogeneity of each viral genome by computing its Sequence Compositional Complexity (SCC). To analyze the full range of SCC diversity, we select random samples of high quality coronavirus genomes covering the full span of the pandemic. We then search for evolutionary trends that could inform us on the adaptive process of the virus to its human host by computing the phylogenetic ridge regression of SCC against time (i.e., the collection date of each viral isolate). In early samples, we find no statistical support for any trend in SCC values, although the viral genome appears to evolve faster than Brownian Motion (BM) expectation. However, in samples taken after the emergence of high fitness variants, and despite the brief time span elapsed, a driven decreasing trend for SCC and an increasing one for its absolute evolutionary rate are detected, pointing to a role for selection in the evolution of SCC in the coronavirus. We conclude that the higher fitness of variant genomes may have leads to adaptive trends of SCC over pandemic time in the coronavirus. Supplementary files File Description SupplementaryTables S1-S19.zip Excel supplementary tables: The strain name, the collection date, and the SCC values for each analyzed genome. nextstrain_ncov_open_global_timetree.nwk ML phylodynamic tree for the Nextstrain sample SupplementaryTable S20.pdf A complete list acknowledging the authors, originating and submitting laboratories of the genetic sequences we used for the analysis of the Nextstrain sample. Nextstrain_sample_fasta_3059.zip Nextstrain sample (sequences in Fasta format) PhylogeneticTimetrees_NewickFormat.zip Phylogenetic timetrees (Newick format).

The Government of Canada has put in place emergency measures under the Quarantine Act to slow the introduction and spread of COVID-19 and variants in Canada. Fully vaccinated travellers without signs and symptoms of COVID-19 are not required to quarantine upon entering Canada if they comply with the requirements in this handout.