There have been almost 60 thousand COVID-19 deaths in New York State as of December 16, 2022. A majority of those deaths have been recorded in New York City: Staten Island, Queens, Brooklyn, Bronx, and Manhattan.

Pandemic takes hold in U.S. Across the United States, over one million COVID-19 deaths had been confirmed by the middle of December 2022. New York has been hit particularly hard throughout the pandemic and is among the states with the highest number of deaths from the coronavirus. The neighboring state of New Jersey was also at the heart of the initial outbreak in March 2020, and the two states continue to have some of the highest death rates from the coronavirus in the United States.

Deaths in New York City The number of new daily deaths from COVID-19 in New York City peaked early in the pandemic. Since then there have been waves in which the number of daily deaths rose, but they have not gotten close to the levels seen early in the pandemic. The impact of the coronavirus has been thoroughly analyzed, and the fatality rates by age in New York City support the evidence that the risk of developing more severe COVID-19 symptoms increases with age.

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

As of March 10, 2023, there have been 1.1 million deaths related to COVID-19 in the United States. There have been 101,159 deaths in the state of California, more than any other state in the country – California is also the state with the highest number of COVID-19 cases.

The vaccine rollout in the U.S. Since the start of the pandemic, the world has eagerly awaited the arrival of a safe and effective COVID-19 vaccine. In the United States, the immunization campaign started in mid-December 2020 following the approval of a vaccine jointly developed by Pfizer and BioNTech. As of March 22, 2023, the number of COVID-19 vaccine doses administered in the U.S. had reached roughly 673 million. The states with the highest number of vaccines administered are California, Texas, and New York.

Vaccines achieved due to work of research groups Chinese authorities initially shared the genetic sequence to the novel coronavirus in January 2020, allowing research groups to start studying how it invades human cells. The surface of the virus is covered with spike proteins, which enable it to bind to human cells. Once attached, the virus can enter the cells and start to make people ill. These spikes were of particular interest to vaccine manufacturers because they hold the key to preventing viral entry.

Reporting of new Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. This dataset will receive a final update on June 1, 2023, to reconcile historical data through May 10, 2023, and will remain publicly available.

Aggregate Data Collection Process Since the start of the COVID-19 pandemic, data have been gathered through a robust process with the following steps:

• A CDC data team reviews and validates the information obtained from jurisdictions’ state and local websites via an overnight data review process.
• If more than one official county data source exists, CDC uses a comprehensive data selection process comparing each official county data source, and takes the highest case and death counts respectively, unless otherwise specified by the state.
• CDC compiles these data and posts the finalized information on COVID Data Tracker.
• County level data is aggregated to obtain state and territory specific totals.

This process is collaborative, with CDC and jurisdictions working together to ensure the accuracy of COVID-19 case and death numbers. County counts provide the most up-to-date numbers on cases and deaths by report date. CDC may retrospectively update counts to correct data quality issues.

Methodology Changes Several differences exist between the current, weekly-updated dataset and the archived version:

• Source: The current Weekly-Updated Version is based on county-level aggregate count data, while the Archived Version is based on State-level aggregate count data.
• Confirmed/Probable Cases/Death breakdown:  While the probable cases and deaths are included in the total case and total death counts in both versions (if applicable), they were reported separately from the confirmed cases and deaths by jurisdiction in the Archived Version.  In the current Weekly-Updated Version, the counts by jurisdiction are not reported by confirmed or probable status (See Confirmed and Probable Counts section for more detail).
• Time Series Frequency: The current Weekly-Updated Version contains weekly time series data (i.e., one record per week per jurisdiction), while the Archived Version contains daily time series data (i.e., one record per day per jurisdiction).
• Update Frequency: The current Weekly-Updated Version is updated weekly, while the Archived Version was updated twice daily up to October 20, 2022.

Important note: The counts reflected during a given time period in this dataset may not match the counts reflected for the same time period in the archived dataset noted above. Discrepancies may exist due to differences between county and state COVID-19 case surveillance and reconciliation efforts.

Confirmed and Probable Counts In this dataset, counts by jurisdiction are not displayed by confirmed or probable status. Instead, confirmed and probable cases and deaths are included in the Total Cases and Total Deaths columns, when available. Not all jurisdictions report probable cases and deaths to CDC.* Confirmed and probable case definition criteria are described here:

Council of State and Territorial Epidemiologists (ymaws.com).

Deaths CDC reports death data on other sections of the website: CDC COVID Data Tracker: Home, CDC COVID Data Tracker: Cases, Deaths, and Testing, and NCHS Provisional Death Counts. Information presented on the COVID Data Tracker pages is based on the same source (total case counts) as the present dataset; however, NCHS Death Counts are based on death certificates that use information reported by physicians, medical examiners, or coroners in the cause-of-death section of each certificate. Data from each of these pages are considered provisional (not complete and pending verification) and are therefore subject to change. Counts from previous weeks are continually revised as more records are received and processed.

Number of Jurisdictions Reporting There are currently 60 public health jurisdictions reporting cases of COVID-19. This includes the 50 states, the District of Columbia, New York City, the U.S. territories of American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, Puerto Rico, and the U.S Virgin Islands as well as three independent countries in compacts of free association with the United States, Federated States of Micronesia, Republic of the Marshall Islands, and Republic of Palau. New York State’s reported case and death counts do not include New York City’s counts as they separately report nationally notifiable conditions to CDC.

CDC COVID-19 data are available to the public as summary or aggregate count files, including total counts of cases and deaths, available by state and by county. These and other data on COVID-19 are available from multiple public locations, such as:

https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/cases-in-us.html

https://www.cdc.gov/covid-data-tracker/index.html

https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html

https://www.cdc.gov/coronavirus/2019-ncov/php/open-america/surveillance-data-analytics.html

Additional COVID-19 public use datasets, include line-level (patient-level) data, are available at: https://data.cdc.gov/browse?tags=covid-19.

Archived Data Notes:

November 3, 2022: Due to a reporting cadence issue, case rates for Missouri counties are calculated based on 11 days’ worth of case count data in the Weekly United States COVID-19 Cases and Deaths by State data released on November 3, 2022, instead of the customary 7 days’ worth of data.

November 10, 2022: Due to a reporting cadence change, case rates for Alabama counties are calculated based on 13 days’ worth of case count data in the Weekly United States COVID-19 Cases and Deaths by State data released on November 10, 2022, instead of the customary 7 days’ worth of data.

November 10, 2022: Per the request of the jurisdiction, cases and deaths among non-residents have been removed from all Hawaii county totals throughout the entire time series. Cumulative case and death counts reported by CDC will no longer match Hawaii’s COVID-19 Dashboard, which still includes non-resident cases and deaths. 

November 17, 2022: Two new columns, weekly historic cases and weekly historic deaths, were added to this dataset on November 17, 2022. These columns reflect case and death counts that were reported that week but were historical in nature and not reflective of the current burden within the jurisdiction. These historical cases and deaths are not included in the new weekly case and new weekly death columns; however, they are reflected in the cumulative totals provided for each jurisdiction. These data are used to account for artificial increases in case and death totals due to batched reporting of historical data.

December 1, 2022: Due to cadence changes over the Thanksgiving holiday, case rates for all Ohio counties are reported as 0 in the data released on December 1, 2022.

January 5, 2023: Due to North Carolina’s holiday reporting cadence, aggregate case and death data will contain 14 days’ worth of data instead of the customary 7 days. As a result, case and death metrics will appear higher than expected in the January 5, 2023, weekly release.

January 12, 2023: Due to data processing delays, Mississippi’s aggregate case and death data will be reported as 0. As a result, case and death metrics will appear lower than expected in the January 12, 2023, weekly release.

January 19, 2023: Due to a reporting cadence issue, Mississippi’s aggregate case and death data will be calculated based on 14 days’ worth of data instead of the customary 7 days in the January 19, 2023, weekly release.

January 26, 2023: Due to a reporting backlog of historic COVID-19 cases, case rates for two Michigan counties (Livingston and Washtenaw) were higher than expected in the January 19, 2023 weekly release.

January 26, 2023: Due to a backlog of historic COVID-19 cases being reported this week, aggregate case and death counts in Charlotte County and Sarasota County, Florida, will appear higher than expected in the January 26, 2023 weekly release.

January 26, 2023: Due to data processing delays, Mississippi’s aggregate case and death data will be reported as 0 in the weekly release posted on January 26, 2023.

February 2, 2023: As of the data collection deadline, CDC observed an abnormally large increase in aggregate COVID-19 cases and deaths reported for Washington State. In response, totals for new cases and new deaths released on February 2, 2023, have been displayed as zero at the state level until the issue is addressed with state officials. CDC is working with state officials to address the issue.

February 2, 2023: Due to a decrease reported in cumulative case counts by Wyoming, case rates will be reported as 0 in the February 2, 2023, weekly release. CDC is working with state officials to verify the data submitted.

February 16, 2023: Due to data processing delays, Utah’s aggregate case and death data will be reported as 0 in the weekly release posted on February 16, 2023. As a result, case and death metrics will appear lower than expected and should be interpreted with caution.

February 16, 2023: Due to a reporting cadence change, Maine’s

Deaths involving coronavirus disease 2019 (COVID-19) reported to NCHS by time-period, HHS region, race and Hispanic origin, and age groups (<65, 65-74. 75-84, 85+, and 65+). United States death counts include the 50 states, plus the District of Columbia and New York City. The ten (10) United States Department of Health and Human Services (HHS) regions include the following jurisdictions. Region 1: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont; Region 2: New Jersey, New York, New York City; Region 3: Delaware, District of Columbia, Maryland, Pennsylvania, Virginia, West Virginia; Region 4: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee; Region 5: Illinois, Indiana, Michigan, Minnesota, Ohio, Wisconsin; Region 6: Arkansas, Louisiana, New Mexico, Oklahoma, Texas; Region 7: Iowa, Kansas, Missouri, Nebraska; Region 8: Colorado, Montana, North Dakota, South Dakota, Utah, Wyoming; Region 9: Arizona, California, Hawaii, Nevada; Region 10: Alaska, Idaho, Oregon, Washington.

Note: Reporting of new COVID-19 Case Surveillance data will be discontinued July 1, 2024, to align with the process of removing SARS-CoV-2 infections (COVID-19 cases) from the list of nationally notifiable diseases. Although these data will continue to be publicly available, the dataset will no longer be updated.

Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Kentucky (1/1/24), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

This case surveillance public use dataset has 19 elements for all COVID-19 cases shared with CDC and includes demographics, geography (county and state of residence), any exposure history, disease severity indicators and outcomes, and presence of any underlying medical conditions and risk behaviors.

Currently, CDC provides the public with three versions of COVID-19 case surveillance line-listed data: this 19 data element dataset with geography, a 12 data element public use dataset, and a 33 data element restricted access dataset.

The following apply to the public use datasets and the restricted access dataset:

• Data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.
• Data are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers.
• Some data are suppressed to protect individual privacy.
• Datasets will include all cases with the earliest date available in each record (date received by CDC or date related to illness/specimen collection) at least 14 days prior to the creation of the current datasets. This 14-day lag allows case reporting to be stabilized and ensure that time-dependent outcome data are accurately captured.
• Datasets are updated monthly.
• Datasets are created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access and include protections designed to protect individual privacy.
• For more information about data collection and reporting, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/about-us-cases-deaths.html.
• For more information about the COVID-19 case surveillance data, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/faq-surveillance.html
  

Overview

The COVID-19 case surveillance database includes individual-level data reported to U.S. states and autonomous reporting entities, including New York City and the District of Columbia (D.C.), as well as U.S. territories and affiliates. On April 5, 2020, COVID-19 was added to the Nationally Notifiable Condition List and classified as “immediately notifiable, urgent (within 24 hours)” by a Council of State and Territorial Epidemiologists (CSTE) Interim Position Statement (<a href="https://cdn.ymaws.com/www.cste.org/resource/resmgr/ps/positionstatement2020/Interim-20-ID-01_COVID

As of March 10, 2023, the death rate from COVID-19 in the state of New York was 397 per 100,000 people. New York is one of the states with the highest number of COVID-19 cases.

Provisional deaths involving coronavirus disease 2019 (COVID-19) and deaths from all causes reported to NCHS by week the death occurred, HHS region of occurrence, race and Hispanic origin, and age group (0-24, 25-64, 65+ years), from 2015-2021.

United States death counts include the 50 states, plus the District of Columbia and New York City. The ten (10) United States Department of Health and Human Services (HHS) regions include the following jurisdictions. Region 1: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont; Region 2: New Jersey, New York, New York City; Region 3: Delaware, District of Columbia, Maryland, Pennsylvania, Virginia, West Virginia; Region 4: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee; Region 5: Illinois, Indiana, Michigan, Minnesota, Ohio, Wisconsin; Region 6: Arkansas, Louisiana, New Mexico, Oklahoma, Texas; Region 7: Iowa, Kansas, Missouri, Nebraska; Region 8: Colorado, Montana, North Dakota, South Dakota, Utah, Wyoming; Region 9: Arizona, California, Hawaii, Nevada; Region 10: Alaska, Idaho, Oregon, Washington.

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

Patient outcomes by BMI categories.

Deaths from all causes and deaths involving coronavirus disease 2019 (COVID-19) reported to NCHS by time-period, HHS region, race and Hispanic origin, and age group.

United States death counts include the 50 states, plus the District of Columbia and New York City. The ten (10) United States Department of Health and Human Services (HHS) regions include the following jurisdictions. Region 1: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont; Region 2: New Jersey, New York, New York City, Puerto Rico; Region 3: Delaware, District of Columbia, Maryland, Pennsylvania, Virginia, West Virginia; Region 4: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee; Region 5: Illinois, Indiana, Michigan, Minnesota, Ohio, Wisconsin; Region 6: Arkansas, Louisiana, New Mexico, Oklahoma, Texas; Region 7: Iowa, Kansas, Missouri, Nebraska; Region 8: Colorado, Montana, North Dakota, South Dakota, Utah, Wyoming; Region 9: Arizona, California, Hawaii, Nevada; Region 10: Alaska, Idaho, Oregon, Washington.

Russia had over 23 million COVID-19 cases as of October 22, 2023. Over the past week, that figure increased by nearly 20 thousand. Russia had the 10th-highest number of coronavirus (COVID-19) cases worldwide. Debate about COVID-19 deaths in Russia The number of deaths from the disease was lower than in other countries most affected by the pandemic. Several foreign media sources, including New York Times and Financial Times, published articles suggesting that the official statistics on the COVID-19 death toll in Russia could be lowered. A narrow definition of a death from COVID-19 and a general increase in mortality in Moscow were pointed out while suggesting why actual death figures could be higher than reported. Russian explanation of lower COVID-19 deaths Experts and lawmakers from Russia provided several answers to the accusations. Among them were the fact that Russians timely reported symptoms to doctors, a high number of tests conducted, as well as a higher herd immunity of the population compared to other countries. In a letter to the New York Times, Moscow’s health department head argued that even if all the additional death cases in the Russian capital in April 2020 were categorized as caused by the COVID-19, the city’s mortality rate from the disease would still be lower than in cities like New York or London.For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

As of March 10, 2023, the state with the highest number of COVID-19 cases was California. Almost 104 million cases have been reported across the United States, with the states of California, Texas, and Florida reporting the highest numbers.

From an epidemic to a pandemic The World Health Organization declared the COVID-19 outbreak a pandemic on March 11, 2020. The term pandemic refers to multiple outbreaks of an infectious illness threatening multiple parts of the world at the same time. When the transmission is this widespread, it can no longer be traced back to the country where it originated. The number of COVID-19 cases worldwide has now reached over 669 million.

The symptoms and those who are most at risk Most people who contract the virus will suffer only mild symptoms, such as a cough, a cold, or a high temperature. However, in more severe cases, the infection can cause breathing difficulties and even pneumonia. Those at higher risk include older persons and people with pre-existing medical conditions, including diabetes, heart disease, and lung disease. People aged 85 years and older have accounted for around 27 percent of all COVID-19 deaths in the United States, although this age group makes up just two percent of the U.S. population

IntroductionIn December 2019, several cases of pneumonia of an unknown origin appeared in China. Previously, in that same year, the World Health Organization (WHO) had already published a list of the ten major global health issues which included the risk of a pandemic from respiratory diseases [1]. Later, in January 2020, the cause of the pneumonia cases detected in China was identified as being a new coronavirus of the Severe Acute Respiratory Syndrome (SARS-CoV-2) [2].The first records of SARS-CoV-2 infection, identified in Wuhan, China, spread quickly causing the territorial spread of contagions across dozens of countries. This lead the World Health Organization (WHO) to declare a pandemic, at the time more than 100 thousand cases of infection had already been detected in 114 countries and a total number of deaths higher than 4.000 [3].Geographical analysis of diseases are common in scientific literature [4, 5, 6] and Geographic Information Systems (GIS) and Spatial Analysis techniques have proven to be useful for studying how they spread across space and time [7, 8]. The spatial dimension plays a key role in epidemiological studies partly due to the growing development of technologies in terms of algorithms and processing capacity that allows the modeling of epidemiological phenomena [8]. COVID-19 studies GIS-based are just as important to understand unknown attributes of the disease in this time of great uncertainty, although, only a few studies have focused on geographic hotspots analysis and have tried to unveil the community drivers associated with the spatial patterns of local transmission [9].ObjectivesThis applied study is twofold. First seeks to highlight the importance of geographical factors in the current context; and second, uses geographic analysis methods and techniques, especially spatial statistic methods, to create evidence-based knowledge upon COVID-19 spatial spread, as well as its patterns and trends.In this way, ArcGIS Pro, Esri’s GIS software, is used in a space-time approach to synthetize the most relevant spatial dynamics. The specific objectives of the study are:1. Analyze the spatial patterns of the pandemic diffusion;2. Identify important transmission clusters;3. Identify spatial determinants of the disease spread.Study Area and DataThe study area is mainland Portugal at a municipal scale, due to being the finest scale of analysis with epidemiological information available in the official reports of the Direção-Geral da Saúde (DGS). Portugal has been severely affected by the pandemic and various spatial dynamics can be identified through time, since the patterns of incidence have changed in successive waves. In this way, the study is focused on various moments during the first year of incidence of the disease, capturing the most important patterns, tendencies and processes. Data used for this analysis is the epidemiological information of DGS [10], for the epidemiologic dimension, and Instituto Nacional de Estatística (INE) database [11] and Carta Social [12] for the variables that will be used as independent variables grouped in 3 dimensions: economic, sociodemographic and mobility (Figure 1).Figure 1 - Variables and respective dimensions of analysisMethodologyThe methodology is divided in 2 parts (Figure 2): the first is related to data acquisition, editing, management and integration in GIS, and the second is in relation to the modeling itself, in order to respond to the objectives which comprises of 3 phases: (i) space-time analysis of confirmed cases of infections to understand the diffusion processes; (ii) analysis of hot spots, clusters and outliers to identify the different patterns and tendencies over time and (iii) ordinary least squares regression (OLS) to identify the most important determinant spatial factors and drivers of the virus propagation.Figure 2 - Methodology flowResultsResults demonstrate an initial tendency of a hierarchical diffusion process, from centers of larger population densities to those of which are less dense (Figure 3), which is replaced and dominated in following periods by contagion expansion. Geographically, the first confirmed cases appeared in coastal cities and progressively penetrated into the interior of the country with a strong spatial association with the main roads and the population size of the territorial units.Figure 3 - Evolution of confirmed cases and hot spots, clusters and outliers of incidence rate by municipalityThe Norte region, namely the Porto metropolitan region, recorded a very high rate of incidence in all periods and broke records in the numbers of new cases, except in the third wave, after the Christmas and New Year festivities, in which the number of new cases was the highest ever in every region and specially in Centro region inland municipalities.The results of OLS (Figure 4) are in line with other studies [13, 14] and show that there is a significant relationship in regard to family size that is visible during almost every period, demonstrating that it is difficult to avoid contagion between cohabitants. Population density also appears as important in various moments, although with lower coefficients.Figure 4 – Ordinary Least Squares resultsEmployment concentrations also appear with a strong spatial relationship with the incidences, as well as the socioeconomic conditions that appear to be represented by different variables (beneficiaries of unemployment benefits, social reintegration allowance, declared income, proportion of house-ownership).The importance of mobility in the virus’s propagation is confirmed, both by type of usual mode of transport and commuting time. The interrelation between school students and incidence may also indicate that increased mobility associated with school attendance is relevant for propagation.ConclusionsArcGIS Pro proved to be crucial and an added value for geographical visualization and for the use of spatial statistics methods, essential in providing evidence-based knowledge about the spatial dynamics of COVID-19 in mainland Portugal. The COVID-19 waves demonstrated different spatial behaviours, with different patterns and thus different community drivers. Income, mobility, population density, family size and employment concentrations appear as the most important spatial determinants. Results are in line with scientific literature and prove the relevance of spatial approaches in epidemiology.References1 - WHO - World Health Organization. (2019). Ten threats to global health in 2019. https://www.who.int/news-room/spotlight/ten-threats-to-global-health-in-20192 - WHO - World Health Organization. (2020a). Coronavirus disease 2019 (COVID-19): situation report, 94. https://apps.who.int/iris/handle/10665/3318653 - WHO - World Health Organization. (2020e). WHO Director-General’s opening remarks at the media briefing on COVID-19 - 11 March 2020. https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-20204 - Gould, P. (1993). The slow plague : a geography of the AIDS pandemic. Blackwell Publishers. https://books.google.pt/books?id=u3Z9QgAACAAJ&dq5 - Cliff, A. D., Hagget, P., Ord, J. K., & Versey, G. R. (1981). Spatial diffusion : an historical geography of epidemics in an island community. Cambridge University Press Cambridge ; New York. https://books.google.pt/books?id=OIaqxwEACAAJ&dq6 - Arroz, M. E. (1979). Difusão espacial da hepatite infecciosa. Finisterra - Revista Portuguesa de Geografia, LV(14) DOI: https://doi.org/10.18055/Finis22377 - Lyseen, A.K.; Nøhr, C.; Sørensen, E.M.; Gudes, O.; Geraghty, E.M.; Shaw, N.T.; Bivona-Tellez, C. (2014). A review and framework for categorizing current research and development in health related geographical information systems (GIS) studies. Yearb Med. Inform. https://doi.org/10.15265%2FIY-2014-00088 - Pfeiffer, D.; Robinson, T; Stevenson, M.; Stevens, K.; Rogers, D.; Clements, A. (2008). Spatial Analysis in Epidemiology. Oxford University Press. https://books.google.pt/books/about/Spatial_Analysis_in_Epidemiology.html?id=niTDr3SIEhUC&redir_esc=y9 - Franch-Pardo, I.; Napoletano, B.M.; Rosete-Verges, F.; Billa, L. Spatial analysis and GIS in the study of COVID-19. A review. (2020). Sci.10 – DGS – Direção-Geral da Saúde (2020). Relatório de Situação. Lisboa: Ministério da Saúde – Direção-Geral da Saúde. https://covid19.min-saude.pt/relatorio-de-situacao/11 – INE – Instituto Nacional de Estatística (s.d.). Portal do INE. Base de dados. https://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_base_dados&contexto=bd&selTab=tab212 – GEP – Gabinete de Estratégia e Planeamento (2018). Carta Social. Ministério do Trabalho, Solidariedade e Segurança Social. www.cartasocial.pt13 – Sousa, P., Costa, N. M., Costa, E. M., Rocha, J., Peixoto, V. R., Fernandes, A. C., Gaspar, R., Duarte-Ramos, F., Abrantes, P., & Leite, A. (2021). COMPRIME - Conhecer mais para intervir melhor: Preliminary mapping of municipal level determinants of covid-19 transmission in Portugal at different moments of the 1st epidemic wave. Portuguese Journal of Public Health. https://doi.org/10.1159/00051433414 – Andersen, L. M.; Harden, S. R.; Sugg, M. M.; Runkle, J. D.; Lundquist, T. E. (2021). Analyzing the spatial determinants of local Covid-19 transmission in the United States. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2020.142396

（停止更新）超额死亡（ED）不包括因新冠而死亡者：预期：上界：纽约市在09-16-2023达1,108.000数量，相较于09-09-2023的1,100.000数量有所增长。（停止更新）超额死亡（ED）不包括因新冠而死亡者：预期：上界：纽约市数据按周更新，01-07-2017至09-16-2023期间平均值为1,122.500数量，共350份观测结果。该数据的历史最高值出现于01-19-2019，达1,240.000数量，而历史最低值则出现于07-07-2018，为1,039.000数量。CEIC提供的（停止更新）超额死亡（ED）不包括因新冠而死亡者：预期：上界：纽约市数据处于定期更新的状态，数据来源于Centers for Disease Control and Prevention，数据归类于全球数据库的美国 – Table US.G012: Number of Excess Deaths: by States: All Causes excluding COVID-19: Predicted (Discontinued)。

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first reported in Wuhan, China in December 2019 and caused a global pandemic resulting in millions of deaths and tens of millions of patients positive tests. While studies have shown a D614G mutation in the viral spike protein are more transmissible, the effects of this and other mutations on the host response, especially at the cellular level, are yet to be fully elucidated. In this experiment we infected normal human bronchial epithelial (NHBE) cells with the Washington (D614) strain or the New York (G614) strains of SARS-CoV-2. We generated RNA sequencing data at 6, 12, and 24 hours post-infection (hpi) to improve our understanding of how the intracellular host response differs between infections with these two strains. We analyzed these data with a bioinformatics pipeline that identifies differentially expressed genes (DEGs), enriched Gene Ontology (GO) terms and dysregulated signaling pathways. We detected over 2,000 DEGs, over 600 GO terms, and 29 affected pathways between the two infections. Many of these entities play a role in immune signaling and response. A comparison between strains and time points showed a higher similarity between matched time points than across different time points with the same strain in DEGs and affected pathways, but found more similarity between strains across different time points when looking at GO terms. A comparison of the affected pathways showed that the 24hpi samples of the New York strain were more similar to the 12hpi samples of the Washington strain, with a large number of pathways related to translation being inhibited in both strains. These results suggest that the various mutations contained in the genome of these two viral isolates may cause distinct effects on the host transcriptional response in infected host cells, especially relating to how quickly translation is dysregulated after infection. This comparison of the intracellular host response to infection with these two SARS-CoV-2 isolates suggest that some of the mechanisms associated with more severe disease from these viruses could include virus replication, metal ion usage, host translation shutoff, host transcript stability, and immune inhibition.

As of September 27, 2020, there were around 125 COVID-19 deaths per 1,000 residents in nursing homes in Massachusetts. This statistic illustrates the rate of COVID-19 deaths in nursing homes in the United States as of September 27, 2020, by state.

BackgroundStudies examining outcomes among individuals with COronaVIrus Disease 2019 (COVID-19) have consistently demonstrated that men have worse outcomes than women, with a higher incidence of myocardial injury, respiratory failure, and death. However, mechanisms of higher morbidity and mortality among men remain poorly understood. We aimed to identify mediators of the relationship between sex and COVID-19-associated mortality.MethodsPatients hospitalized at two quaternary care facilities, New York Presbyterian Hospital (CUIMC/NYPH) and Massachusetts General Hospital (MGH), for SARS-CoV-2 infection between February and May 2020 were included. Five independent biomarkers were identified as mediators of sex effects, including high-sensitivity cardiac troponin T (hs-cTNT), high sensitivity C-reactive protein (hs-CRP), ferritin, D-dimer, and creatinine.ResultsIn the CUIMC/NYPH cohort (n = 2,626, 43% female), male sex was associated with significantly greater mortality (26 vs. 21%, p = 0.0146) and higher peak hs-cTNT, hs-CRP, ferritin, D-dimer, and creatinine (p < 0.001). The effect of male sex on the primary outcome of death was partially mediated by peak values of all five biomarkers, suggesting that each pathophysiological pathway may contribute to increased risk of death in men. Hs-cTnT, creatinine, and hs-CRP were the strongest mediators. Findings were highly consistent in the MGH cohort with the exception of D-dimer.ConclusionsThis study suggests that the effect of sex on COVID-19 outcomes is mediated by cardiac and kidney injury, as well as underlying differences in inflammation and iron metabolism. Exploration of these specific pathways may facilitate sex-directed diagnostic and therapeutic strategies for patients with COVID-19 and provides a framework for the study of sex differences in other complex diseases.