As of July 2023, the Omicron variant was the most prevalent among selected countries in Latin America. The share of COVID-19 cases corresponding to the Omicron variant amounted to 100 percent of the analyzed sequences of SARS-CoV-2 in Colombia. The variant Omicron (XBB.1.5) accounted for nearly 81 percent of the sequenced cases in the country, while Omicron (XBB.1.9) added up to 14 percent. Similarly, Peru reported over 90 percent of its reviewed sequences corresponding to the variant Omicron (XBB.1.5), while Omicron (XBB) accounted for around 2.4 percent of cases studied. A regional overview The Omicron variant of SARS-CoV-2 - the virus causing COVID-19 - was designated as a variant of concern by the World Health Organization in November 2021. Since then, it has been rapidly spreading, causing an unprecedented increase in the number of cases reported worldwide. In Latin America, Brazil had been the most affected country by the disease already before the emergence of the Omicron variant, with nearly 37.4 million cases and around 701,494 confirmed deaths as of May 2, 2023. However, it is Peru that has the largest mortality rate per 100,000 inhabitants due to the SARS-Cov-2 in the region, with roughly 672 deaths per 100,000 people. Vaccination campaigns in Latin America As the COVID-19 pandemic continues to cause social and economic harm worldwide, most Latin American and Caribbean countries advance their immunization programs. As of August 14, 2023, Brazil had administered the largest number of vaccines in the region, with over 486.4 million doses. Mexico and Argentina followed, with about 223.1 million and 116 million COVID-19 doses administered, respectively. However, Cuba had the highest vaccination rate not only in the region, but also the world, with around 391 vaccines given per 100 people.Find the most up-to-date information about the coronavirus pandemic in the world under Statista’s COVID-19 facts and figures site.

Data is from Health and Human Services Region 5 (MN, WI, IL, IN, MI, OH)

To identify and track SARS-CoV-2 variants, CDC uses genomic surveillance. CDC's national genomic surveillance system collects SARS-CoV-2 specimens for sequencing through the National SARS-CoV-2 Strain Surveillance (NS3) program, as well as SARS-CoV-2 sequences generated by commercial or academic laboratories contracted by CDC and state or local public health laboratories. Viral genomic sequences are analyzed and classified as a particular variant. The proportions of variants in a population are estimated nationally, by HHS region, and by jurisdiction. The thousands of sequences analyzed every week through CDC’s national genomic sequencing and bioinformatics efforts fuel this comprehensive and population-based U.S. surveillance system established to identify and monitor the spread of variants.

These data appear on the CDC COVID Data Tracker at the following URL: https://covid.cdc.gov/covid-data-tracker/#variant-proportions

For more information on how these data are generated and used to provide estimates of variant proportions, please see the following references:

Paul P, France AM, Aoki Y, et al. Genomic Surveillance for SARS-CoV-2 Variants Circulating in the United States, December 2020–May 2021. MMWR Morb Mortal Wkly Rep 2021;70:846–850. DOI: http://dx.doi.org/10.15585/mmwr.mm7023a3

Lambrou AS, Shirk P, Steele MK, et al. Genomic Surveillance for SARS-CoV-2 Variants: Predominance of the Delta (B.1.617.2) and Omicron (B.1.1.529) Variants — United States, June 2021–January 2022. MMWR Morb Mortal Wkly Rep 2022;71:206–211. DOI: http://dx.doi.org/10.15585/mmwr.mm7106a4

As of April 10, 2021, there had been 20,915 reported cases of the B.1.1.7 SARS-CoV-2 variant. SARS-CoV-2 variants act differently than the original disease and therefore may spread more quickly or cause more severe disease. The rise of SARS-CoV-2 variants has become increasingly worrisome in many countries. This statistic shows the number of reported cases of SARS-CoV-2 variants in the United States as of April 10, 2021, by variant type.

As of April 10, 2021, there had been 20,915 reported cases of the B.1.1.7 SARS-CoV-2 variant, with Florida accounting for the highest number of cases. SARS-CoV-2 variants act differently than the original disease and therefore may spread more quickly or cause more severe disease. The rise of SARS-CoV-2 variants has become increasingly worrisome in many countries. This statistic shows the number of reported cases of SARS-CoV-2 variant B.1.1.7 - a variant first discovered in the United Kingdom in late December 2020 - in the United States as of April 10, 2021, by state or territory.

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

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Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

This dataset includes the cumulative (total) number of COVID-19 cases, hospitalizations, and deaths for each health district in Virginia by report date and by age group.

Data for CDC’s COVID Data Tracker site on Rates of COVID-19 Cases and Deaths by Vaccination Status. Click 'More' for important dataset description and footnotes

Dataset and data visualization details: These data were posted on October 21, 2022, archived on November 18, 2022, and revised on February 22, 2023. These data reflect cases among persons with a positive specimen collection date through September 24, 2022, and deaths among persons with a positive specimen collection date through September 3, 2022.

Vaccination status: A person vaccinated with a primary series had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after verifiably completing the primary series of an FDA-authorized or approved COVID-19 vaccine. An unvaccinated person had SARS-CoV-2 RNA or antigen detected on a respiratory specimen and has not been verified to have received COVID-19 vaccine. Excluded were partially vaccinated people who received at least one FDA-authorized vaccine dose but did not complete a primary series ≥14 days before collection of a specimen where SARS-CoV-2 RNA or antigen was detected. Additional or booster dose: A person vaccinated with a primary series and an additional or booster dose had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after receipt of an additional or booster dose of any COVID-19 vaccine on or after August 13, 2021. For people ages 18 years and older, data are graphed starting the week including September 24, 2021, when a COVID-19 booster dose was first recommended by CDC for adults 65+ years old and people in certain populations and high risk occupational and institutional settings. For people ages 12-17 years, data are graphed starting the week of December 26, 2021, 2 weeks after the first recommendation for a booster dose for adolescents ages 16-17 years. For people ages 5-11 years, data are included starting the week of June 5, 2022, 2 weeks after the first recommendation for a booster dose for children aged 5-11 years. For people ages 50 years and older, data on second booster doses are graphed starting the week including March 29, 2022, when the recommendation was made for second boosters. Vertical lines represent dates when changes occurred in U.S. policy for COVID-19 vaccination (details provided above). Reporting is by primary series vaccine type rather than additional or booster dose vaccine type. The booster dose vaccine type may be different than the primary series vaccine type. ** Because data on the immune status of cases and associated deaths are unavailable, an additional dose in an immunocompromised person cannot be distinguished from a booster dose. This is a relevant consideration because vaccines can be less effective in this group. Deaths: A COVID-19–associated death occurred in a person with a documented COVID-19 diagnosis who died; health department staff reviewed to make a determination using vital records, public health investigation, or other data sources. Rates of COVID-19 deaths by vaccination status are reported based on when the patient was tested for COVID-19, not the date they died. Deaths usually occur up to 30 days after COVID-19 diagnosis. Participating jurisdictions: Currently, these 31 health departments that regularly link their case surveillance to immunization information system data are included in these incidence rate estimates: Alabama, Arizona, Arkansas, California, Colorado, Connecticut, District of Columbia, Florida, Georgia, Idaho, Indiana, Kansas, Kentucky, Louisiana, Massachusetts, Michigan, Minnesota, Nebraska, New Jersey, New Mexico, New York, New York City (New York), North Carolina, Philadelphia (Pennsylvania), Rhode Island, South Dakota, Tennessee, Texas, Utah, Washington, and West Virginia; 30 jurisdictions also report deaths among vaccinated and unvaccinated people. These jurisdictions represent 72% of the total U.S. population and all ten of the Health and Human Services Regions. Data on cases among people who received additional or booster doses were reported from 31 jurisdictions; 30 jurisdictions also reported data on deaths among people who received one or more additional or booster dose; 28 jurisdictions reported cases among people who received two or more additional or booster doses; and 26 jurisdictions reported deaths among people who received two or more additional or booster doses. This list will be updated as more jurisdictions participate. Incidence rate estimates: Weekly age-specific incidence rates by vaccination status were calculated as the number of cases or deaths divided by the number of people vaccinated with a primary series, overall or with/without a booster dose (cumulative) or unvaccinated (obtained by subtracting the cumulative number of people vaccinated with a primary series and partially vaccinated people from the 2019 U.S. intercensal population estimates) and multiplied by 100,000. Overall incidence rates were age-standardized using the 2000 U.S. Census standard population. To estimate population counts for ages 6 months through 1 year, half of the single-year population counts for ages 0 through 1 year were used. All rates are plotted by positive specimen collection date to reflect when incident infections occurred. For the primary series analysis, age-standardized rates include ages 12 years and older from April 4, 2021 through December 4, 2021, ages 5 years and older from December 5, 2021 through July 30, 2022 and ages 6 months and older from July 31, 2022 onwards. For the booster dose analysis, age-standardized rates include ages 18 years and older from September 19, 2021 through December 25, 2021, ages 12 years and older from December 26, 2021, and ages 5 years and older from June 5, 2022 onwards. Small numbers could contribute to less precision when calculating death rates among some groups. Continuity correction: A continuity correction has been applied to the denominators by capping the percent population coverage at 95%. To do this, we assumed that at least 5% of each age group would always be unvaccinated in each jurisdiction. Adding this correction ensures that there is always a reasonable denominator for the unvaccinated population that would prevent incidence and death rates from growing unrealistically large due to potential overestimates of vaccination coverage. Incidence rate ratios (IRRs): IRRs for the past one month were calculated by dividing the average weekly incidence rates among unvaccinated people by that among people vaccinated with a primary series either overall or with a booster dose. Publications: Scobie HM, Johnson AG, Suthar AB, et al. Monitoring Incidence of COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Status — 13 U.S. Jurisdictions, April 4–July 17, 2021. MMWR Morb Mortal Wkly Rep 2021;70:1284–1290. Johnson AG, Amin AB, Ali AR, et al. COVID-19 Incidence and Death Rates Among Unvaccinated and Fully Vaccinated Adults with and Without Booster Doses During Periods of Delta and Omicron Variant Emergence — 25 U.S. Jurisdictions, April 4–December 25, 2021. MMWR Morb Mortal Wkly Rep 2022;71:132–138. Johnson AG, Linde L, Ali AR, et al. COVID-19 Incidence and Mortality Among Unvaccinated and Vaccinated Persons Aged ≥12 Years by Receipt of Bivalent Booster Doses and Time Since Vaccination — 24 U.S. Jurisdictions, October 3, 2021–December 24, 2022. MMWR Morb Mortal Wkly Rep 2023;72:145–152. Johnson AG, Linde L, Payne AB, et al. Notes from the Field: Comparison of COVID-19 Mortality Rates Among Adults Aged ≥65 Years Who Were Unvaccinated and Those Who Received a Bivalent Booster Dose Within the Preceding 6 Months — 20 U.S. Jurisdictions, September 18, 2022–April 1, 2023. MMWR Morb Mortal Wkly Rep 2023;72:667–669.

This repository within the ACTIV TRACE initiative houses a comprehensive collection of datasets related to SARS-CoV-2. The processing of SARS-CoV-2 Sequence Read Archive (SRA) files has been optimized to identify genetic variations in viral samples. This information is then presented in the Variant Call Format (VCF). Each VCF file corresponds to the SRA parent-run's accession ID. Additionally, the data is available in the parquet format, making it easier to search and filter using the Amazon Athena Service. The SARS-CoV-2 Variant Calling Pipeline is designed to handle new data every six hours, with updates to the AWS ODP bucket occurring daily.

Information related to Fig 5. (CSV)

The ongoing COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global threat. Despite strict control measures implemented worldwide and immunization using novel vaccines, the pandemic continues to rage due to emergence of several variants of SARS-CoV-2 with increased transmission and immune escape. The rapid spread of variants of concern (VOC) in the recent past has created a massive challenge for the control of COVID-19 pandemic via the currently used vaccines. Vaccines that are safe and effective against the current and future variants of SARS-CoV-2 are essential in controlling the COVID-19 pandemic. Rapid production and massive rollout of next-generation vaccines against the variants are key steps to control the COVID-19 pandemic and to help us return to normality. Coordinated surveillance of SARS-CoV-2, rapid redesign of new vaccines and extensive vaccination are needed to counter the current SARS-CoV-2 variants and prevent the emergence of new variants. In this article, we review the latest information on the VOCs and variants of interest (VOIs) and present the information on the clinical trials that are underway on evaluating the effectiveness of COVID-19 vaccines on VOCs. We also discuss the current challenges posed by the VOCs in controlling the COVID-19 pandemic and future strategies to overcome the threat posed by the highly virulent and rapidly transmissible variants of SARS-CoV2. The COVID-19 is a contagious respiratory disease caused by a virus known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that emerged in 2019. The COVID-19 has now spread to all part of the world and has become a global threat. Even after the strict control measures and immunization programs to prevent the disease, COVID-19 is still causing destruction due to appearance of new strains of SARS-CoV-2 that transmit faster and capable of escaping the immunity. The faster spread of the new strains of viruses that cause more severe disease is the biggest challenge to control the COVID-19 pandemic by using the presently available vaccines. To control the COVID-19 pandemic we urgently need safe and effective vaccines against the corona viral variants. This can be achieved by tracking the appearance of new viral types, design and rapid production and supply of new vaccines against the virus. This article presents the latest information on the new types of SARS-CoV-2, and on the status of vaccine trials and their effectiveness against these viruses. Similarly, the information on the challenges posed by the new viral strains in controlling the COVID-19 and future strategies to overcome the threat posed by corona viruses is also provided.

SARS-CoV-2 prevalence in wastewater influent is determined from multiple samples of wastewater each day. Units are in millions of copies of N1 and N2 genes, per person in the sewage treatment area, per day. Viral load data are from Metropolitan Council and the University of Minnesota Genomics Center.

Variant presence and frequency are inferred from the N501Y mutation (Alpha, Beta and Gamma); the L452R mutation (Delta); and the K417N mutation (Omicron). K417N mutations present before November 18, 2020 are assumed to be Beta variants, and are marked as Other in the variant column.

As of January 2022, the share of COVID-19 cases corresponding to the Omicron variant in Mexico amounted to over 90 percent of the country's analyzed sequences of the SARS-CoV-2 virus. A month earlier, this figure amounted to 60 percent of cases studied in the country. The Omicron variant of SARS-CoV-2 - the virus causing COVID-19 - was designated as a variant of concern by the World Health Organization in November 2021 based on its trasmisibility level.

An increasing amount of cases

In Mexico, the spread of the Omicron variant led the Latin American country to reach over 5.6 million confirmed cases of COVID-19 by March 2022, with the surge of close to two million cases in a matter of four months. Never before since the start of the pandemic had there been so many cases recorded in such a short period of time in the country. During those months, approximately 30 thousand people died due to complications stemming from the disease, reaching 320 thousand deaths by March 2022.

A relatively low testing rate

Within the Latin American region, Mexico was the fourth country with the largest number of people infected, following Brazil, Argentina, and Colombia. However, the country is considered to have had a relatively low testing rate. According to recent estimates, around 117 thousand tests per million people were reported in Mexico as of March 2022, one of the lowest COVID-19 testing rates among the countries most affected by the pandemic. In contrast, Peru reached over 836 million tests per million population.

Find the most up-to-date information about the coronavirus pandemic in the world under Statista’s COVID-19 facts and figures site.

Data includes COVID-19 case trends by county, age, new cases by date, case rates by county (including recoveries and deaths), COVID-19 data by race, and COVID-19 Maine cases vs other states. The website contains also information about hospitalization, contact tracing, testing, cases demographics, vaccination, cases by ZIP and COVID variants. The data is compiled by Maine CDC and events are reported according to the day they happened. Data is open access and available to the public.

This feature layer contains the most up-to-date COVID-19 cases for the US, Canada. Data sources: WHO, CDC, ECDC, NHC, DXY, 1point3acres, Worldometers.info, BNO, state and national government health departments, and local media reports. The China data is automatically updating at least once per hour, and non China data is updating manually. This layer is created and maintained by the Center for Systems Science and Engineering (CSSE) at the Johns Hopkins University. This feature layer is supported by Esri Living Atlas team and JHU Data Services. This layer is opened to the public and free to share. Contact Johns Hopkins.

IMPORTANT NOTICE:

1. Fields for Active Cases and Recovered Cases are set to 0 in all locations. John Hopkins has not found a reliable source for this information at the county level but will continue to look and carry the fields.

2. Fields for Incident Rate and People Tested are placeholders for when this becomes available at the county level.

3. In some instances, cases have not been assigned a location at the county scale. those are still assigned a state but are listed as unassigned and given a Lat Long of 0,0.

Data Field Descriptions by Alias Name:

• Province/State: (Text) Country Province or State Name (Level 2 Key)
  
• Country/Region: (Text) Country or Region Name (Level 1 Key)
  
• Last Update: (Datetime) Last data update Date/Time in UTC
• Latitude: (Float) Geographic Latitude in Decimal Degrees (WGS1984)
• Longitude: (Float) Geographic Longitude in Decimal Degrees (WGS1984)
• Confirmed: (Long) Best collected count of Confirmed Cases reported by geography
  
• Recovered: (Long) Not Currently in Use, JHU is looking for a source
• Deaths: (Long) Best collected count for Case Deaths reported by geography
  
• Active: (Long) Confirmed - Recovered - Deaths (computed) Not Currently in Use due to lack of Recovered data
  
• County: (Text) US County Name (Level 3 Key)
  
• FIPS: (Text) US State/County Codes
  
• Combined Key: (Text) Comma separated concatenation of Key Field values (L3, L2, L1)
  
• Incident Rate: (Long)
  
• People Tested: (Long) Not Currently in Use Placeholder for additional data
  
• People Hospitalized: (Long) Not Currently in Use Placeholder for additional data

Brazil is the Latin American country affected the most by the COVID-19 pandemic. As of May 2025, the country had reported around 38 million cases. It was followed by Argentina, with approximately ten million confirmed cases of COVID-19. In total, the region had registered more than 83 million diagnosed patients, as well as a growing number of fatal COVID-19 cases. The research marathon Normally, the development of vaccines takes years of research and testing until options are available to the general public. However, with an alarming and threatening situation as that of the COVID-19 pandemic, scientists quickly got on board in a vaccine marathon to develop a safe and effective way to prevent and control the spread of the virus in record time. Over two years after the first cases were reported, the world had around 1,521 drugs and vaccines targeting the COVID-19 disease. As of June 2022, a total of 39 candidates were already launched and countries all over the world had started negotiations and acquisition of the vaccine, along with immunization campaigns. COVID vaccination rates in Latin America As immunization against the spread of the disease continues to progress, regional disparities in vaccination coverage persist. While Brazil, Argentina, and Mexico were among the Latin American nations with the most COVID-19 cases, those that administered the highest number of COVID-19 doses per 100 population are Cuba, Chile, and Peru. Leading the vaccination coverage in the region is the Caribbean nation, with more than 406 COVID-19 vaccines administered per every 100 inhabitants as of January 5, 2024.For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased its collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit: World Health Organization (WHO)For more information, visit the Johns Hopkins Coronavirus Resource Center.COVID-19 Trends MethodologyOur goal is to analyze and present daily updates in the form of recent trends within countries, states, or counties during the COVID-19 global pandemic. The data we are analyzing is taken directly from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.DOI: https://doi.org/10.6084/m9.figshare.125529863/7/2022 - Adjusted the rate of active cases calculation in the U.S. to reflect the rates of serious and severe cases due nearly completely dominant Omicron variant.6/24/2020 - Expanded Case Rates discussion to include fix on 6/23 for calculating active cases.6/22/2020 - Added Executive Summary and Subsequent Outbreaks sectionsRevisions on 6/10/2020 based on updated CDC reporting. This affects the estimate of active cases by revising the average duration of cases with hospital stays downward from 30 days to 25 days. The result shifted 76 U.S. counties out of Epidemic to Spreading trend and no change for national level trends.Methodology update on 6/2/2020: This sets the length of the tail of new cases to 6 to a maximum of 14 days, rather than 21 days as determined by the last 1/3 of cases. This was done to align trends and criteria for them with U.S. CDC guidance. The impact is areas transition into Controlled trend sooner for not bearing the burden of new case 15-21 days earlier.Correction on 6/1/2020Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020. Revisions added on 4/30/2020 are highlighted.Revisions added on 4/23/2020 are highlighted.Executive SummaryCOVID-19 Trends is a methodology for characterizing the current trend for places during the COVID-19 global pandemic. Each day we assign one of five trends: Emergent, Spreading, Epidemic, Controlled, or End Stage to geographic areas to geographic areas based on the number of new cases, the number of active cases, the total population, and an algorithm (described below) that contextualize the most recent fourteen days with the overall COVID-19 case history. Currently we analyze the countries of the world and the U.S. Counties. The purpose is to give policymakers, citizens, and analysts a fact-based data driven sense for the direction each place is currently going. When a place has the initial cases, they are assigned Emergent, and if that place controls the rate of new cases, they can move directly to Controlled, and even to End Stage in a short time. However, if the reporting or measures to curtail spread are not adequate and significant numbers of new cases continue, they are assigned to Spreading, and in cases where the spread is clearly uncontrolled, Epidemic trend.We analyze the data reported by Johns Hopkins University to produce the trends, and we report the rates of cases, spikes of new cases, the number of days since the last reported case, and number of deaths. We also make adjustments to the assignments based on population so rural areas are not assigned trends based solely on case rates, which can be quite high relative to local populations.Two key factors are not consistently known or available and should be taken into consideration with the assigned trend. First is the amount of resources, e.g., hospital beds, physicians, etc.that are currently available in each area. Second is the number of recoveries, which are often not tested or reported. On the latter, we provide a probable number of active cases based on CDC guidance for the typical duration of mild to severe cases.Reasons for undertaking this work in March of 2020:The popular online maps and dashboards show counts of confirmed cases, deaths, and recoveries by country or administrative sub-region. Comparing the counts of one country to another can only provide a basis for comparison during the initial stages of the outbreak when counts were low and the number of local outbreaks in each country was low. By late March 2020, countries with small populations were being left out of the mainstream news because it was not easy to recognize they had high per capita rates of cases (Switzerland, Luxembourg, Iceland, etc.). Additionally, comparing countries that have had confirmed COVID-19 cases for high numbers of days to countries where the outbreak occurred recently is also a poor basis for comparison.The graphs of confirmed cases and daily increases in cases were fit into a standard size rectangle, though the Y-axis for one country had a maximum value of 50, and for another country 100,000, which potentially misled people interpreting the slope of the curve. Such misleading circumstances affected comparing large population countries to small population counties or countries with low numbers of cases to China which had a large count of cases in the early part of the outbreak. These challenges for interpreting and comparing these graphs represent work each reader must do based on their experience and ability. Thus, we felt it would be a service to attempt to automate the thought process experts would use when visually analyzing these graphs, particularly the most recent tail of the graph, and provide readers with an a resulting synthesis to characterize the state of the pandemic in that country, state, or county.The lack of reliable data for confirmed recoveries and therefore active cases. Merely subtracting deaths from total cases to arrive at this figure progressively loses accuracy after two weeks. The reason is 81% of cases recover after experiencing mild symptoms in 10 to 14 days. Severe cases are 14% and last 15-30 days (based on average days with symptoms of 11 when admitted to hospital plus 12 days median stay, and plus of one week to include a full range of severely affected people who recover). Critical cases are 5% and last 31-56 days. Sources:U.S. CDC. April 3, 2020 Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Accessed online. Initial older guidance was also obtained online. Additionally, many people who recover may not be tested, and many who are, may not be tracked due to privacy laws. Thus, the formula used to compute an estimate of active cases is: Active Cases = 100% of new cases in past 14 days + 19% from past 15-25 days + 5% from past 26-49 days - total deaths. On 3/17/2022, the U.S. calculation was adjusted to: Active Cases = 100% of new cases in past 14 days + 6% from past 15-25 days + 3% from past 26-49 days - total deaths. Sources: https://www.cdc.gov/mmwr/volumes/71/wr/mm7104e4.htm https://covid.cdc.gov/covid-data-tracker/#variant-proportions If a new variant arrives and appears to cause higher rates of serious cases, we will roll back this adjustment. We’ve never been inside a pandemic with the ability to learn of new cases as they are confirmed anywhere in the world. After reviewing epidemiological and pandemic scientific literature, three needs arose. We need to specify which portions of the pandemic lifecycle this map cover. The World Health Organization (WHO) specifies six phases. The source data for this map begins just after the beginning of Phase 5: human to human spread and encompasses Phase 6: pandemic phase. Phase six is only characterized in terms of pre- and post-peak. However, these two phases are after-the-fact analyses and cannot ascertained during the event. Instead, we describe (below) a series of five trends for Phase 6 of the COVID-19 pandemic.Choosing terms to describe the five trends was informed by the scientific literature, particularly the use of epidemic, which signifies uncontrolled spread. The five trends are: Emergent, Spreading, Epidemic, Controlled, and End Stage. Not every locale will experience all five, but all will experience at least three: emergent, controlled, and end stage.This layer presents the current trends for the COVID-19 pandemic by country (or appropriate level). There are five trends:Emergent: Early stages of outbreak. Spreading: Early stages and depending on an administrative area’s capacity, this may represent a manageable rate of spread. Epidemic: Uncontrolled spread. Controlled: Very low levels of new casesEnd Stage: No New cases These trends can be applied at several levels of administration: Local: Ex., City, District or County – a.k.a. Admin level 2State: Ex., State or Province – a.k.a. Admin level 1National: Country – a.k.a. Admin level 0Recommend that at least 100,000 persons be represented by a unit; granted this may not be possible, and then the case rate per 100,000 will become more important.Key Concepts and Basis for Methodology: 10 Total Cases minimum threshold: Empirically, there must be enough cases to constitute an outbreak. Ideally, this would be 5.0 per 100,000, but not every area has a population of 100,000 or more. Ten, or fewer, cases are also relatively less difficult to track and trace to sources. 21 Days of Cases minimum threshold: Empirically based on COVID-19 and would need to be adjusted for any other event. 21 days is also the minimum threshold for analyzing the “tail” of the new cases curve, providing seven cases as the basis for a likely trend (note that 21 days in the tail is preferred). This is the minimum needed to encompass the onset and duration of a normal case (5-7 days plus 10-14 days). Specifically, a median of 5.1 days incubation time, and 11.2 days for 97.5% of cases to incubate. This is also driven by pressure to understand trends and could easily be adjusted to 28 days. Source

The global market for Human Covid-19 proteins experienced significant growth during the pandemic (2019-2024), driven by the urgent need for research, diagnostics, and vaccine development. While precise market figures are unavailable, a reasonable estimation, based on the scale of the pandemic response and the involvement of numerous major players like Abcam, Thermo Fisher Scientific, and Merck KGaA (Sigma-Aldrich), suggests a market size exceeding $500 million in 2025. The Compound Annual Growth Rate (CAGR) during the initial phase of the pandemic likely exceeded 20%, fueled by high demand for research reagents and diagnostic tools. Post-pandemic, the market is expected to experience a moderate yet sustained growth, driven by ongoing research into long COVID, variant-specific diagnostics, and the development of next-generation vaccines and therapeutics. This sustained growth will likely be in the range of 5-10% CAGR from 2025-2033, reflecting a maturing market with continued, albeit less intense, demand. Key market segments include research reagents (antibodies, recombinant proteins), diagnostic components, and vaccine production materials. Factors such as ongoing research into COVID-19 pathogenesis, the emergence of new variants, and the need for improved diagnostic tools are likely to sustain market growth. However, restraints include the decrease in immediate pandemic-related research funding, the growing availability of alternative technologies, and potential supply chain disruptions. The competitive landscape is characterized by numerous players, with established firms holding a significant market share. This competition drives innovation, pushing for better quality, greater efficiency and affordability of Human Covid-19 proteins. Geographic distribution is likely to favor North America and Europe initially due to established research infrastructure and high spending on healthcare, followed by a rise in demand from Asia Pacific regions as their research capabilities develop further.

Long-term immunity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires the identification of T-cell epitopes affecting host immunogenicity. In this computational study, we explored the CD8+ epitope diversity estimated in 27 of the most common HLA-A and HLA-B alleles, representing most of the United States population. Analysis of 16 SARS-CoV-2 variants [B.1, Alpha (B.1.1.7), five Delta (AY.100, AY.25, AY.3, AY.3.1, AY.44), and nine Omicron (BA.1, BA.1.1, BA.2, BA.4, BA.5, BQ.1, BQ.1.1, XBB.1, XBB.1.5)] in analyzed MHC class I alleles revealed that SARS-CoV-2 CD8+ epitope conservation was estimated at 87.6%–96.5% in spike (S), 92.5%–99.6% in membrane (M), and 94.6%–99% in nucleocapsid (N). As the virus mutated, an increasing proportion of S epitopes experienced reduced predicted binding affinity: 70% of Omicron BQ.1-XBB.1.5 S epitopes experienced decreased predicted binding, as compared with ~3% and ~15% in the earlier strains Delta AY.100–AY.44 and Omicron BA.1–BA.5, respectively. Additionally, we identified several novel candidate HLA alleles that may be more susceptible to severe disease, notably HLA-A*32:01, HLA-A*26:01, and HLA-B*53:01, and relatively protected from disease, such as HLA-A*31:01, HLA-B*40:01, HLA-B*44:03, and HLA-B*57:01. Our findings support the hypothesis that viral genetic variation affecting CD8 T-cell epitope immunogenicity contributes to determining the clinical severity of acute COVID-19. Achieving long-term COVID-19 immunity will require an understanding of the relationship between T cells, SARS-CoV-2 variants, and host MHC class I genetics. This project is one of the first to explore the SARS-CoV-2 CD8+ epitope diversity that putatively impacts much of the United States population.

After entering Italy, the coronavirus (COVID-19) spread fast. The strict lockdown implemented by the government during the Spring 2020 helped to slow down the outbreak. However, the country had to face four new harsh waves of contagion. As of January 1, 2025, the total number of cases reported by the authorities reached over 26.9 million. The north of the country was mostly hit, and the region with the highest number of cases was Lombardy, which registered almost 4.4 million of them. The north-eastern region of Veneto and the southern region of Campania followed in the list. When adjusting these figures for the population size of each region, however, the picture changed, with the region of Veneto being the area where the virus had the highest relative incidence. Coronavirus in Italy Italy has been among the countries most impacted by the coronavirus outbreak. Moreover, the number of deaths due to coronavirus recorded in Italy is significantly high, making it one of the countries with the highest fatality rates worldwide, especially in the first stages of the pandemic. In particular, a very high mortality rate was recorded among patients aged 80 years or older. Impact on the economy The lockdown imposed during the Spring 2020, and other measures taken in the following months to contain the pandemic, forced many businesses to shut their doors and caused industrial production to slow down significantly. As a result, consumption fell, with the sectors most severely hit being hospitality and tourism, air transport, and automotive. Several predictions about the evolution of the global economy were published at the beginning of the pandemic, based on different scenarios about the development of the pandemic. According to the official results, it appeared that the coronavirus outbreak had caused Italy’s GDP to shrink by approximately nine percent in 2020.