Among the ten major virus outbreaks in the last 50 years, Marburg ranked first in terms of the fatality rate with 80 percent. In comparison, the recent novel coronavirus, originating from the Chinese city of Wuhan, had an estimated fatality rate of 2.2 percent as of January 31, 2020.

Alarming COVID-19 fatality rate in Mexico More than 812,000 people worldwide had died from COVID-19 as of August 24, 2020. Three of the most populous countries in the world have reported particularly large numbers of coronavirus-related deaths: Mexico, Brazil, and the United States. Out of those three nations, Mexico has the highest COVID-19 death rate, with around one in ten confirmed cases resulting in death. The high fatality rate in Mexico indicates that cases may be much higher than reported because testing capacity has been severely stretched.

Post-lockdown complacency a real danger In March 2020, each infected person was estimated to transmit the COVID-19 virus to between 1.5 and 3.5 other people, which was a higher infection rate than the seasonal flu. The coronavirus is primarily spread through respiratory droplets, and transmission commonly occurs when people are in close contact. As lockdowns ease around the world, people are being urged not to become complacent; continue to wear face coverings and practice social distancing, which can help to prevent further infections.

In terms of the number of infected people, the novel coronavirus (SARS-CoV-2) ranked third among ten major virus outbreaks as of the end of January 2020. The virus, which originated from the Chinese city of Wuhan, has since spread to around 215 countries and territories worldwide.

China searching for disease’s origins The cumulative number of COVID-19 cases in China topped 89,000 on August 11, 2020. The SARS-CoV-2 virus and the infectious disease it causes were unknown before the outbreak began in China in December 2019. Experts from the World Health Organization are now working with Chinese counterparts to identify the origins of the virus. The most common symptoms reported by Chinese patients were fever, dry cough, and fatigue.

The rapid global spread of the virus In March 2020, it was estimated that the SARS-CoV-2 virus had an infection rate of between 1.5 and 3.5, which is higher than other outbreaks that have emerged worldwide in the past two decades. According to early estimates in January 2020, the case fatality rate was around two percent, but the spread of the coronavirus has overwhelmed many countries. The case fatality rate in China was as high as 5.5 percent in mid-April 2020.

As of May 2, 2023, the outbreak of the coronavirus disease (COVID-19) had spread to almost every country in the world, and more than 6.86 million people had died after contracting the respiratory virus. Over 1.16 million of these deaths occurred in the United States.

Waves of infections Almost every country and territory worldwide have been affected by the COVID-19 disease. At the end of 2021 the virus was once again circulating at very high rates, even in countries with relatively high vaccination rates such as the United States and Germany. As rates of new infections increased, some countries in Europe, like Germany and Austria, tightened restrictions once again, specifically targeting those who were not yet vaccinated. However, by spring 2022, rates of new infections had decreased in many countries and restrictions were once again lifted.

What are the symptoms of the virus? It can take up to 14 days for symptoms of the illness to start being noticed. The most commonly reported symptoms are a fever and a dry cough, leading to shortness of breath. The early symptoms are similar to other common viruses such as the common cold and flu. These illnesses spread more during cold months, but there is no conclusive evidence to suggest that temperature impacts the spread of the SARS-CoV-2 virus. Medical advice should be sought if you are experiencing any of these symptoms.

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.

Summary of filovirus sequelae and persistence in animal models.

Data from influenza A virus (IAV) infected ferrets (Mustela putorius furo) provides invaluable information towards the study of novel and emerging viruses that pose a threat to human health. This gold standard animal model can recapitulate many clinical signs of infection present in IAV-infected humans, support virus replication of human and zoonotic strains without prior adaptation, and permit evaluation of virus transmissibility by multiple modes. While ferrets have been employed in risk assessment settings for >20 years, results from this work are typically reported in discrete stand-alone publications, making aggregation of raw data from this work over time nearly impossible. Here, we describe a dataset of 746 ferrets inoculated with 129 unique IAV, conducted by a single research group (NCIRD/ID/IPB/Pathogenesis Laboratory Team) under a uniform experimental protocol. This collection of morbidity, mortality, and viral titer data represents the largest publicly available dataset to date of in vivo-generated IAV infection outcomes on a per-individual ferret level.

Published Data Descriptor for more information: Kieran TJ, Sun X, Creager HM, Tumpey TM, Maine TR, Belser JA. 2024. An aggregated dataset of serial morbidity and titer measurements from influenza A virus-infected ferrets. Sci Data 11, 510. https://doi.org/10.1038/s41597-024-03256-6

Additional publications using and describing data: Kieran TJ, Sun X, Maines TR, Beauchemin CAA, Belser JA. 2024. Exploring associations between viral titer measurements and disease outcomes in ferrets inoculated with 125 contemporary influenza A viruses. J Virol. 98:e01661-23. https://doi.org/10.1128/jvi.01661-23

Belser JA, Kieran TJ, Mitchell ZA, Sun X, Mayfield K, Tumpey TM, Spengler JR, Maines TR. 2024. Key considerations to improve the normalization, interpretation and reproducibility of morbidity data in mammalian models of viral disease. Dis Model Mech; 17 (3): dmm050511. https://doi.org/10.1242/dmm.050511

Kieran TJ, Sun X, Maines TR, Belser JA. 2024. Machine learning approaches for influenza A virus risk assessment identifies predictive correlates using ferret model in vivo data. Communications Biology 7, 927. https://doi.org/10.1038/s42003-024-06629-0

Additional publications supporting responsible use and interpretation of data by others: Kieran TJ, Maine TR, Belser JA. 2025. Eleven quick tips to unlock the power of in vivo data science. PLoS Comput Biol, 21(4):e1012947. https://doi.org/10.1371/journal.pcbi.1012947

Kieran TJ, Maine TR, Belser JA. 2025. Data alchemy, from lab to insight: Transforming in vivo experiments into data science gold. PLoS Pathog, 20(8):e1012460. https://doi.org/10.1371/journal.ppat.1012460

Change / Update Log: Nov 7, 2024: Corrected typographical errors in Origin column for A/Ohio/13/2017 and A/Hawaii/28/2020

July 1, 2025: Added 3 viruses (A/Texas/36/1991, A/Texas/37/2024, A/Michigan/90/2024, total n=18 new rows)

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.

BackgroundIn a given population the age pattern of mortality is an important determinant of total number of deaths, age structure, and through effects on age structure, the number of births and thereby growth. Good mortality models exist for most populations except those experiencing generalized HIV epidemics and some developing country populations. The large number of deaths concentrated at very young and adult ages in HIV-affected populations produce a unique ‘humped’ age pattern of mortality that is not reproduced by any existing mortality models. Both burden of disease reporting and population projection methods require age-specific mortality rates to estimate numbers of deaths and produce plausible age structures. For countries with generalized HIV epidemics these estimates should take into account the future trajectory of HIV prevalence and its effects on age-specific mortality. In this paper we present a parsimonious model of age-specific mortality for countries with generalized HIV/AIDS epidemics.Methods and FindingsThe model represents a vector of age-specific mortality rates as the weighted sum of three independent age-varying components. We derive the age-varying components from a Singular Value Decomposition of the matrix of age-specific mortality rate schedules. The weights are modeled as a function of HIV prevalence and one of three possible sets of inputs: life expectancy at birth, a measure of child mortality, or child mortality with a measure of adult mortality. We calibrate the model with 320 five-year life tables for each sex from the World Population Prospects 2010 revision that come from the 40 countries of the world that have and are experiencing a generalized HIV epidemic. Cross validation shows that the model is able to outperform several existing model life table systems.ConclusionsWe present a flexible, parsimonious model of age-specific mortality for countries with generalized HIV epidemics. Combined with the outputs of existing epidemiological and demographic models, this model makes it possible to project future age-specific mortality profiles and number of deaths for countries with generalized HIV epidemics.

As of 2023, the countries with the highest death rates worldwide were Monaco, Bulgaria, and Latvia. In these countries, there were ** to ** deaths per 1,000 people. The country with the lowest death rate is Qatar, where there is just *** death per 1,000 people. Leading causes of death The leading causes of death worldwide are, by far, cardiovascular diseases, accounting for ** percent of all deaths in 2021. That year, there were **** million deaths worldwide from ischaemic heart disease and **** million from stroke. Interestingly, a worldwide survey from that year found that people greatly underestimate the proportion of deaths caused by cardiovascular disease, but overestimate the proportion of deaths caused by suicide, interpersonal violence, and substance use disorders. Death in the United States In 2023, there were around **** million deaths in the United States. The leading causes of death in the United States are currently heart disease and cancer, accounting for a combined ** percent of all deaths in 2023. Lung and bronchus cancer is the deadliest form of cancer worldwide, as well as in the United States. In the U.S. this form of cancer is predicted to cause around ****** deaths among men alone in the year 2025. Prostate cancer is the second-deadliest cancer for men in the U.S. while breast cancer is the second deadliest for women. In 2023, the tenth leading cause of death in the United States was COVID-19. Deaths due to COVID-19 resulted in a significant rise in the total number of deaths in the U.S. in 2020 and 2021 compared to 2019, and it was the third leading cause of death in the U.S. during those years.

Nipah Virus Testing Market Outlook

The Nipah Virus Testing Market is projected to experience significant growth, with the market size valued at approximately USD 250 million in 2023 and expected to reach around USD 750 million by 2032, growing at a CAGR of 12.7%. The global market for Nipah virus testing is driven by the urgent need for effective diagnostic methods due to the recurrent outbreaks of the virus, the high fatality rate associated with Nipah virus infections, and the increasing awareness and preparedness for potential pandemics.

One of the primary growth factors for the Nipah virus testing market is the increased frequency and severity of Nipah virus outbreaks. Countries in Southeast Asia, particularly Bangladesh and India, have reported multiple outbreaks over the years, emphasizing the need for rapid and accurate diagnostics. This demand is further fueled by the high mortality rate associated with Nipah virus infections, which ranges from 40% to 75%, making it imperative to develop reliable testing methods to contain the spread of the virus effectively.

Another significant growth factor is the advancements in diagnostic technologies that have made testing more accessible and accurate. Innovations such as Real-Time Polymerase Chain Reaction (RT-PCR) and Enzyme-Linked Immunosorbent Assay (ELISA) have revolutionized the detection of Nipah virus, allowing for quicker and more precise identification of the virus in infected individuals. These advanced diagnostics not only contribute to better patient outcomes but also help in implementing timely public health interventions to prevent widespread transmission.

Moreover, increasing investments in healthcare infrastructure and research are also contributing to the market growth. Governments and private organizations are investing heavily in the development of diagnostic laboratories and research institutes dedicated to studying and combating emerging infectious diseases. Such investments are crucial in enhancing the capabilities of healthcare systems to respond swiftly to Nipah virus outbreaks, thereby driving the demand for Nipah virus testing solutions.

Regionally, the Asia Pacific region is expected to dominate the Nipah virus testing market due to the higher incidence of Nipah virus outbreaks in this region. Countries like India, Bangladesh, and Malaysia are investing significantly in healthcare infrastructure and diagnostic capabilities to tackle the Nipah virus effectively. In contrast, regions like North America and Europe are witnessing growth in the market due to increased awareness and preparedness for potential outbreaks, further supported by robust healthcare systems and substantial research funding.

Arbovirus Testing is becoming increasingly relevant in the context of global health as the prevalence of arboviruses, such as dengue, Zika, and chikungunya, continues to rise. These viruses, transmitted by arthropods like mosquitoes, pose significant public health challenges, particularly in tropical and subtropical regions. The development of efficient and accurate arbovirus testing methods is crucial for timely diagnosis and management of these infections. Advances in molecular diagnostics, including RT-PCR and serological assays, have enhanced the ability to detect arboviruses quickly and accurately, thereby aiding in the implementation of effective control measures. As the threat of arboviruses grows, the demand for comprehensive testing solutions is expected to increase, paralleling the trends observed in the Nipah virus testing market.

Test Type Analysis

The test type segment in the Nipah virus testing market includes Real-Time Polymerase Chain Reaction (RT-PCR), Enzyme-Linked Immunosorbent Assay (ELISA), Virus Isolation, and other diagnostic methods. Real-Time Polymerase Chain Reaction (RT-PCR) is currently the leading test type due to its high sensitivity and specificity. RT-PCR allows for the rapid detection of Nipah virus RNA in clinical samples, making it an invaluable tool in the timely diagnosis and management of Nipah virus infections. This methodÂ’s ability to detect even low levels of viral RNA contributes to its widespread adoption in diagnostic laboratories.

Enzyme-Linked Immunosorbent Assay (ELISA) is another prominent test type used in Nipah virus testing. ELISA tests are crucial for detecting antibodies against the Nipah virus in patient samples, providing valuable information about the immune response to the infection. T

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

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

Recent increases in emergent infectious diseases have raised concerns about the population stability of some marine species. The complexity and expense of studying diseases in marine systems often dictate that conservation and management decisions are made without quantitative data on population-level impacts of disease. Mark-recapture is a powerful, underutilized, tool for calculating impacts of disease on population size and structure, even in the absence of etiological information. We applied logistic regression models to mark-recapture data to obtain estimates of disease-associated mortality rates in three commercially-important marine species: snow crab (Chionoecetes opilio) in Newfoundland, Canada, that experience sporadic epizootics of bitter crab disease; striped bass (Morone saxatilis) in the Chesapeake Bay, USA, that experience chronic dermal and visceral mycobacteriosis; and American lobster (Homarus americanus) in the Southern New England stock, that experience chronic epizootic shell disease. All three diseases decreased survival of diseased hosts. Survival of diseased adult male crabs was 1% (0.003 – 0.022, 95% CI) that of uninfected crabs indicating nearly complete mortality of infected crabs in this life stage. Survival of moderately and severely diseased striped bass (which comprised 15% and 11% of the population, respectively) was 84% (70 – 100%, 95% CI), and 54% (42- 68%, 95% CI) and that of healthy striped bass. The disease-adjusted yearly natural mortality rate for striped bass was 0.29, nearly double the previously accepted value, which did not include disease. Survival of moderately and severely diseased lobsters was 30% (15 – 60%, 95% CI) that of healthy lobsters and survival of mildly diseased lobsters was 45% (27 – 75%, 95% CI) that of healthy lobsters. High disease mortality in ovigerous females may explain the poor recruitment and rapid declines observed in this population. Stock assessments should account for disease-related mortality when resource management options are evaluated.

The Flaviviridae family consists of single-stranded positive-sense RNA viruses, which contains the genera Flavivirus, Hepacivirus, Pegivirus, and Pestivirus. Currently, there is an outbreak of viral diseases caused by this family affecting millions of people worldwide, leading to significant morbidity and mortality rates. Advances in computational chemistry have greatly facilitated the discovery of novel drugs and treatments for diseases associated with this family. Chemoinformatic techniques, such as the perturbation theory machine learning method, have played a crucial role in developing new approaches based on ML models that can effectively aid drug discovery. The IFPTML models have shown its capability to handle, classify, and process large data sets with high specificity. The results obtained from different models indicates that this methodology is proficient in processing the data, resulting in a reduction of the false positive rate by 4.25%, along with an accuracy of 83% and reliability of 92%. These values suggest that the model can serve as a computational tool in assisting drug discovery efforts and the development of new treatments against Flaviviridae family diseases.

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

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

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

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

The burden of influenza in the United States can vary from year to year depending on which viruses are circulating, how many people receive an influenza vaccination, and how effective the vaccination is in that particular year. During the 2023-2024 flu season, around 28,000 people lost their lives to the disease. Although most people recover from influenza without needing medical care, the disease can be deadly among young children, the elderly, and those with weakened immune systems or chronic illnesses. Deaths due to influenza Even though most people recover from influenza without medical care, influenza and pneumonia can be deadly, especially for older people and those with certain preexisting conditions. Influenza is a common cause of pneumonia and although most cases of influenza do not develop into pneumonia, those that do are often more severe and more deadly. Deaths due to influenza are most common among the elderly, with a mortality rate of around 32 per 100,000 population during the 2023-2024 flu season. In comparison, the mortality rate for those aged 50 to 64 years was 9.1 per 100,000 population. Flu vaccinations The most effective way to prevent influenza is to receive an annual influenza vaccination. These vaccines have proven to be safe and are usually cheap and easily accessible. Nevertheless, every year a large share of the population in the United States still fails to get vaccinated against influenza. For example, in the 2022-2023 flu season, only 35 percent of those aged 18 to 49 years received a flu vaccination. Unsurprisingly, children and the elderly are the most likely to get vaccinated. It is estimated that during the 2022-2023 flu season, vaccinations prevented over 929 thousand influenza cases among children aged 6 months to 4 years.

BackgroundThe prevention and control of public infectious diseases is a significant issue in the global health sector. Controlling infectious diseases is crucial for maintaining public health. As the most populous country in the world, China still faces a series of new challenges in the control of public infectious diseases. Therefore, it is of great significance to conduct an in-depth analysis of the trends in the control of public infectious diseases.MethodologyThis study selects the death rate, incidence rate, proportion of prevention and control funds input, and the proportion of professional technical personnel in China from 2018 to 2023 as research samples and conducts statistical analysis through multiple linear regression. Overall, factors such as the incidence rate, proportion of prevention and control funds input, and proportion of professional technical personnel can explain 98.7% of the trend changes in the infectious disease death rate.ResultsThrough multiple regression analysis, the regression coefficient value of 0.001 for the incidence rate indicates a significant positive impact on the mortality rate, meaning that an increase in the incidence of infectious diseases leads to a rise in mortality. The regression coefficient value of −0.012 for the proportion of funding input suggests a significant negative impact on the mortality rate, implying that increased investment in prevention and control funds will correspondingly reduce the mortality rate of infectious diseases. On the other hand, merely increasing the number of professional and technical personnel is not sufficient to control the spread of infectious diseases; comprehensive use of various prevention and control measures is required for effective public infectious disease control.ConclusionPublic infectious disease prevention and control is a complex process that requires the consideration of multiple factors, rather than merely changing a single factor, particularly in controlling incidence rates and reasonably allocating funds. By refining the analysis of infectious disease control strategies and integrating diverse preventive and intervention measures, it is possible to better control the spread and mortality of infectious diseases, thereby protecting public health and safety.

IntroductionCongenital heart disease (CHD) represents a significant global public health burden, with substantial variability in mortality rates across different regions and age groups.MethodsThis study utilized the Global Burden of Disease (GBD) database to examine trends in CHD-related mortality among children aged 0-14 from 1990 to 2021.ResultsWe report a 55.34% reduction in CHD-related deaths among children, with global mortality rates decreasing from 28.63 per 100,000 in 1990 to 11.06 per 100,000 in 2021. Notably, the decline in mortality was more pronounced in younger children, with the highest burden observed in the Low socio-demographic index (SDI) region, where CHD-related mortality rates remain disproportionately high. In contrast, the high SDI region experienced the greatest improvements in mortality reduction. Regional disparities are also evident, with South Asia bearing the highest number of CHD-related deaths, while Oceania exhibited the highest mortality rate.DiscussionThese trends underscore the need for continued global efforts to reduce CHD-related mortality, particularly in low-income regions, and to address the disparities in healthcare access and outcomes. Our findings highlight the ongoing challenges in pediatric cardiology and the need for targeted interventions to sustain improvements in CHD survival, especially for neonates and infants.

The general sociodemographic characteristics and disease burden of two deaths from JEV infection during pediatric hospitalizations from December 2015 to December 2020.

Marburg Virus Diagnostics Market Outlook

According to our latest research, the global Marburg Virus Diagnostics market size in 2024 stands at USD 312 million, with a robust compound annual growth rate (CAGR) of 9.7% projected through the forecast period. By 2033, the market is anticipated to reach USD 721 million, driven by heightened awareness, increasing outbreaks, and technological advancements in diagnostic platforms. The market's growth is primarily fueled by the urgent need for rapid and accurate detection tools to contain outbreaks and prevent global health crises, as well as the rising investments in healthcare infrastructure and infectious disease surveillance.

One of the primary growth factors for the Marburg Virus Diagnostics market is the increasing incidence and re-emergence of Marburg virus outbreaks across Africa and sporadic cases globally. As the virus poses a significant threat due to its high fatality rates and potential for rapid transmission, governments and health organizations are prioritizing early detection and containment. This has led to an uptick in funding for research and development of advanced diagnostic solutions, including PCR assays and rapid diagnostic tests. Furthermore, the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC) have been actively promoting the implementation of robust surveillance systems, which is further propelling the demand for innovative diagnostic products.

Technological advancements in diagnostic methodologies have also been pivotal in shaping the Marburg Virus Diagnostics market landscape. The integration of molecular diagnostics, such as real-time PCR and next-generation sequencing, has significantly enhanced the accuracy and speed of virus detection. These technologies enable healthcare professionals to identify viral RNA within hours, facilitating timely patient management and outbreak control. Additionally, the development of point-of-care testing devices, which are portable and user-friendly, is expanding the reach of diagnostics to remote and resource-limited settings. This democratization of diagnostic capabilities is crucial for early intervention and reducing mortality rates associated with Marburg virus infections.

Another key growth driver is the increasing collaboration between public and private sectors to address the diagnostic gap in emerging and re-emerging infectious diseases. Pharmaceutical companies, diagnostic manufacturers, and research institutes are forming strategic alliances to accelerate the development and commercialization of novel diagnostic kits. These partnerships are not only fostering innovation but also ensuring the availability of affordable and scalable solutions, particularly in regions with limited healthcare resources. Moreover, regulatory agencies are streamlining approval processes for emergency use authorizations, expediting market entry for new diagnostic products during outbreak situations.

From a regional perspective, Africa continues to be the epicenter of Marburg virus outbreaks, accounting for the largest share of diagnostic demand. However, North America and Europe are witnessing increased adoption of Marburg Virus Diagnostics due to heightened travel, global interconnectedness, and proactive preparedness measures. The Asia Pacific region is also emerging as a significant market, driven by strengthening healthcare infrastructure and growing investments in infectious disease management. Regional disparities in healthcare access and infrastructure, however, remain a challenge, necessitating targeted interventions to ensure equitable access to advanced diagnostics worldwide.

Product Type Analysis

The Marburg Virus Diagnostics market by product type is segmented into PCR assays, ELISA kits, rapid diagnostic tests, and other diagnostic modalities. PCR assays currently dominate the market, owing to their unparalleled sensitivity and specificity in detecting Marburg viral RNA. The