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.

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.

Among COVID-19 patients in the United States from February 12 to March 16, 2020, estimated case-fatality rates were highest for adults aged 85 years and older. Younger people appeared to have milder symptoms, and there were no deaths reported among persons aged 19 years and under.

Tracking the virus in the United States The outbreak of a previously unknown viral pneumonia was first reported in China toward the end of December 2019. The first U.S. case of COVID-19 was recorded in mid-January 2020, confirmed in a patient who had returned to the United States from China. The virus quickly started to spread, and the first community-acquired case was confirmed one month later in California. Overall, there had been approximately 4.5 million coronavirus cases in the country by the start of August 2020.

U.S. health care system stretched California, Florida, and Texas are among the states with the most coronavirus cases. Even the best-resourced hospitals in the United States have struggled to cope with the crisis, and certain areas of the country were dealt further blows by new waves of infections in July 2020. Attention is rightly focused on fighting the pandemic, but as health workers are redirected to care for COVID-19 patients, the United States must not lose sight of other important health care issues.

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.

In 2022, the states with the highest death rates due to heart disease were Oklahoma, Mississippi, and Alabama. That year, there were around 257 deaths due to heart disease per 100,000 population in the state of Oklahoma. In comparison, the overall death rate from heart disease in the United States was 167 per 100,000 population. The leading cause of death in the United States Heart disease is the leading cause of death in the United States, accounting for 21 percent of all deaths in 2022. That year, cancer was the second leading cause of death, followed by unintentional injuries and COVID-19. In the United States, a person has a one in six chance of dying from heart disease. Death rates for heart disease are higher among men than women, but both have seen steady decreases in heart disease death rates since the 1950s. What are risk factors for heart disease? Although heart disease is the leading cause of death in the United States, the risk of heart disease can be decreased by avoiding known risk factors. Some of the leading preventable risk factors for heart disease include smoking, heavy alcohol use, physical inactivity, an unhealthy diet, and being overweight or obese. It is no surprise that the states with the highest rates of death from heart disease are also the states with the highest rates of heart disease risk factors. For example, Oklahoma, the state with the highest heart disease death rate, is also the state with the third-highest rate of obesity. Furthermore, Mississippi is the state with the highest levels of physical inactivity, and it has the second-highest heart disease death rate in the United States.

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.

Heart Disease Deaths in Oklahoma

Current Trends and Target Rates

By Oklahoma [source]

About this dataset

This dataset contains an overview of historical heart disease death rates in Oklahoma from 2000 to 2018. The dataset consists of yearly figures and target figures for the numbers of deaths due to heart diseases, allowing a comparison between the expected rate and the actual rate over time. This data is important as it can be used to analyze trends in heart disease death rates, helping inform public health initiatives and policy decisions

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How to use the dataset

This dataset includes the number of death due to heart disease in Oklahoma. It provides a single, comprehensive data set that captures detailed information on the historical prevalence of heart disease death rates in the state. This dataset can be used for various research or analytical purposes such as epidemiological studies or health services planning.

To use this dataset, one must first understand that it contains three main pieces: the year of reported deaths, the actual number of deaths related to heart disease during each year and a target total for expected deaths from heart disease per year, which are used as reference points when analyzing other years. The years column includes all relevant dates while historical data column provides more specifics such as exact numbers and percentages related to those who perished due to heart-related conditions.

By utilizing this data set users can easily find out how many persons died due to cardiac-related diseases along with what risks were most prevalent at certain times over that period by comparing provided figures with reference targets at any given time slice in question (time point). Additionally, one can observe trends carefully within different groups such as males versus females or rural versus urban locations thus allowing them more robust insight into factors associated with mortality from cardiac conditions across different demographics

Research Ideas

• Identifying which geographic areas in Oklahoma are at highest risk for heart disease and creating targeted public health initiatives to reduce its incidence.
• Determining correlations between changes in vital health indicators (e.g., increase of physical activity) with changes in heart disease death rates to better inform policy and research direction.
• Analyzing overall mortality rates compared to other counties or states with comparable demographics to assess the effectiveness of existing public health interventions over time

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: res_heart_disease_deaths_kdjx-hayj.csv | Column name | Description | |:--------------------|:-----------------------------------------------------------------------------------------------------------------------------------------| | Years | The year associated with the data. (Integer) | | Historical Data | The number of deaths due to heart disease in Oklahoma in that particular year from 2000-2018. (Integer) | | Target | A value generated based on Historical Data indicating what should be targeted as a baseline performance measure going forward. (Integer) |

File: res_heart_disease_deaths_-_column_chart_3a28-gndr.csv | Column name | Description | |:--------------------|:-----------------------------------------------------------------------------------------------------------------------------------------| | Years | The year associated with the data. (Integer) | | Historical Data | The number of deaths due to heart disease in Oklahoma in that particular year from 2000-2018. (Integer) | | Target | A value generated based on Historical Data indicating what should be targeted as a baseline performance measure going forward. (Integer) |

Acknowledgements

...

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

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

Note: This COVID-19 data set is no longer being updated as of December 1, 2023. Access current COVID-19 data on the CDPH respiratory virus dashboard (https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/Respiratory-Viruses/RespiratoryDashboard.aspx) or in open data format (https://data.chhs.ca.gov/dataset/respiratory-virus-dashboard-metrics).

As of August 17, 2023, data is being updated each Friday.

For death data after December 31, 2022, California uses Provisional Deaths from the Center for Disease Control and Prevention’s National Center for Health Statistics (NCHS) National Vital Statistics System (NVSS). Prior to January 1, 2023, death data was sourced from the COVID-19 registry. The change in data source occurred in July 2023 and was applied retroactively to all 2023 data to provide a consistent source of death data for the year of 2023.

As of May 11, 2023, data on cases, deaths, and testing is being updated each Thursday. Metrics by report date have been removed, but previous versions of files with report date metrics are archived below.

All metrics include people in state and federal prisons, US Immigration and Customs Enforcement facilities, US Marshal detention facilities, and Department of State Hospitals facilities. Members of California's tribal communities are also included.

The "Total Tests" and "Positive Tests" columns show totals based on the collection date. There is a lag between when a specimen is collected and when it is reported in this dataset. As a result, the most recent dates on the table will temporarily show NONE in the "Total Tests" and "Positive Tests" columns. This should not be interpreted as no tests being conducted on these dates. Instead, these values will be updated with the number of tests conducted as data is received.

ObjectivesThe aim of this study is to estimate the excess mortality burden of influenza virus infection in China from 2012 to 2021, with a concurrent analysis of its associated disease manifestations.MethodsLaboratory surveillance data on influenza, relevant population demographics, and mortality records, including cause of death data in China, spanning the years 2012 to 2021, were incorporated into a comprehensive analysis. A negative binomial regression model was utilized to calculate the excess mortality rate associated with influenza, taking into consideration factors such as year, subtype, and cause of death.ResultsThere was no evidence to indicate a correlation between malignant neoplasms and any subtype of influenza, despite the examination of the effect of influenza on the mortality burden of eight diseases. A total of 327,520 samples testing positive for influenza virus were isolated between 2012 and 2021, with a significant decrease in the positivity rate observed during the periods of 2012–2013 and 2019–2020. China experienced an average annual influenza-associated excess deaths of 201721.78 and an average annual excess mortality rate of 14.53 per 100,000 people during the research period. Among the causes of mortality that were examined, respiratory and circulatory diseases (R&C) accounted for the most significant proportion (58.50%). Fatalities attributed to respiratory and circulatory diseases exhibited discernible temporal patterns, whereas deaths attributable to other causes were dispersed over the course of the year.ConclusionTheoretically, the contribution of these disease types to excess influenza-related fatalities can serve as a foundation for early warning and targeted influenza surveillance. Additionally, it is possible to assess the costs of prevention and control measures and the public health repercussions of epidemics with greater precision.

As of 2022, the countries with the highest death rates worldwide were Ukraine, Bulgaria, and Moldova. In these countries, there were 17 to 21 deaths per 1,000 people. The country with the lowest death rate is Qatar, where there is just one death per 1,000 people. Leading causes of death The leading causes of death worldwide are by far, ischaemic heart disease and stroke, accounting for a combined 27 percent of all deaths in 2019. In that year, there were 8.89 million deaths worldwide from ischaemic heart disease and 6.19 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 2022, there were around 3.27 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 40 percent of all deaths in 2022. 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 65,790 deaths among men alone in the year 2024. Prostate cancer is the second-deadliest cancer for men in the U.S. while breast cancer is the second deadliest for women. In 2022, the fourth 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.

HIV/AIDS deaths in the U.S. have dropped significantly in recent years. In 1995, the death rate from HIV per 100,000 people was 16.2. That number has since decreased to 1.3 per 100,000 as of 2023. The reduction in the HIV death rate in the U.S. can be attributed to an increase in access to HIV medications. HIV/AIDS in the U.S. Human immunodeficiency virus (HIV) and acquired immune deficiency syndrome (AIDS) cause a systemic viral infection that damages the immune system. AIDS is a syndrome that is caused by HIV. AIDS is when the immune system is severely weakened by HIV and the body can no longer fight off infections. Among all U.S. states, Georgia, followed by Florida and Louisiana, had the highest rates of new HIV diagnoses in 2022. HIV/AIDS treatments in the U.S. HIV/AIDS treatments include antiretroviral medications to reduce the levels of HIV within the body. The largest funders for HIV/AIDS medications and research are the National Institutes of Health (NIH) and the Ryan White Program. The top HIV drug worldwide, based on revenue generated in 2023 was Biktarvy. Around the world, access to antiretroviral treatment has increased dramatically in recent years, a huge step in reducing the number of HIV-related deaths. There is currently no cure for HIV.

COVID-19 Trends MethodologyOur goal is to analyze and present daily updates in the form of recent trends within countries, states, or counties during the COVID-19 global pandemic. The data we are analyzing is taken directly from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.Revisions added on 4/23/2020 are highlighted.Revisions added on 4/30/2020 are highlighted.Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020. Correction on 6/1/2020Methodology update on 6/2/2020: This sets the length of the tail of new cases to 6 to a maximum of 14 days, rather than 21 days as determined by the last 1/3 of cases. This was done to align trends and criteria for them with U.S. CDC guidance. The impact is areas transition into Controlled trend sooner for not bearing the burden of new case 15-21 days earlier.Reasons for undertaking this work:The popular online maps and dashboards show counts of confirmed cases, deaths, and recoveries by country or administrative sub-region. Comparing the counts of one country to another can only provide a basis for comparison during the initial stages of the outbreak when counts were low and the number of local outbreaks in each country was low. By late March 2020, countries with small populations were being left out of the mainstream news because it was not easy to recognize they had high per capita rates of cases (Switzerland, Luxembourg, Iceland, etc.). Additionally, comparing countries that have had confirmed COVID-19 cases for high numbers of days to countries where the outbreak occurred recently is also a poor basis for comparison.The graphs of confirmed cases and daily increases in cases were fit into a standard size rectangle, though the Y-axis for one country had a maximum value of 50, and for another country 100,000, which potentially misled people interpreting the slope of the curve. Such misleading circumstances affected comparing large population countries to small population counties or countries with low numbers of cases to China which had a large count of cases in the early part of the outbreak. These challenges for interpreting and comparing these graphs represent work each reader must do based on their experience and ability. Thus, we felt it would be a service to attempt to automate the thought process experts would use when visually analyzing these graphs, particularly the most recent tail of the graph, and provide readers with an a resulting synthesis to characterize the state of the pandemic in that country, state, or county.The lack of reliable data for confirmed recoveries and therefore active cases. Merely subtracting deaths from total cases to arrive at this figure progressively loses accuracy after two weeks. The reason is 81% of cases recover after experiencing mild symptoms in 10 to 14 days. Severe cases are 14% and last 15-30 days (based on average days with symptoms of 11 when admitted to hospital plus 12 days median stay, and plus of one week to include a full range of severely affected people who recover). Critical cases are 5% and last 31-56 days. Sources:U.S. CDC. April 3, 2020 Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Accessed online. Initial older guidance was also obtained online. Additionally, many people who recover may not be tested, and many who are, may not be tracked due to privacy laws. Thus, the formula used to compute an estimate of active cases is: Active Cases = 100% of new cases in past 14 days + 19% from past 15-30 days + 5% from past 31-56 days - total deaths.We’ve never been inside a pandemic with the ability to learn of new cases as they are confirmed anywhere in the world. After reviewing epidemiological and pandemic scientific literature, three needs arose. We need to specify which portions of the pandemic lifecycle this map cover. The World Health Organization (WHO) specifies six phases. The source data for this map begins just after the beginning of Phase 5: human to human spread and encompasses Phase 6: pandemic phase. Phase six is only characterized in terms of pre- and post-peak. However, these two phases are after-the-fact analyses and cannot ascertained during the event. Instead, we describe (below) a series of five trends for Phase 6 of the COVID-19 pandemic.Choosing terms to describe the five trends was informed by the scientific literature, particularly the use of epidemic, which signifies uncontrolled spread. The five trends are: Emergent, Spreading, Epidemic, Controlled, and End Stage. Not every locale will experience all five, but all will experience at least three: emergent, controlled, and end stage.This layer presents the current trends for the COVID-19 pandemic by country (or appropriate level). There are five trends:Emergent: Early stages of outbreak. Spreading: Early stages and depending on an administrative area’s capacity, this may represent a manageable rate of spread. Epidemic: Uncontrolled spread. Controlled: Very low levels of new casesEnd Stage: No New cases These trends can be applied at several levels of administration: Local: Ex., City, District or County – a.k.a. Admin level 2State: Ex., State or Province – a.k.a. Admin level 1National: Country – a.k.a. Admin level 0Recommend that at least 100,000 persons be represented by a unit; granted this may not be possible, and then the case rate per 100,000 will become more important.Key Concepts and Basis for Methodology: 10 Total Cases minimum threshold: Empirically, there must be enough cases to constitute an outbreak. Ideally, this would be 5.0 per 100,000, but not every area has a population of 100,000 or more. Ten, or fewer, cases are also relatively less difficult to track and trace to sources. 21 Days of Cases minimum threshold: Empirically based on COVID-19 and would need to be adjusted for any other event. 21 days is also the minimum threshold for analyzing the “tail” of the new cases curve, providing seven cases as the basis for a likely trend (note that 21 days in the tail is preferred). This is the minimum needed to encompass the onset and duration of a normal case (5-7 days plus 10-14 days). Specifically, a median of 5.1 days incubation time, and 11.2 days for 97.5% of cases to incubate. This is also driven by pressure to understand trends and could easily be adjusted to 28 days. Source used as basis:Stephen A. Lauer, MS, PhD *; Kyra H. Grantz, BA *; Qifang Bi, MHS; Forrest K. Jones, MPH; Qulu Zheng, MHS; Hannah R. Meredith, PhD; Andrew S. Azman, PhD; Nicholas G. Reich, PhD; Justin Lessler, PhD. 2020. The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Annals of Internal Medicine DOI: 10.7326/M20-0504.New Cases per Day (NCD) = Measures the daily spread of COVID-19. This is the basis for all rates. Back-casting revisions: In the Johns Hopkins’ data, the structure is to provide the cumulative number of cases per day, which presumes an ever-increasing sequence of numbers, e.g., 0,0,1,1,2,5,7,7,7, etc. However, revisions do occur and would look like, 0,0,1,1,2,5,7,7,6. To accommodate this, we revised the lists to eliminate decreases, which make this list look like, 0,0,1,1,2,5,6,6,6.Reporting Interval: In the early weeks, Johns Hopkins' data provided reporting every day regardless of change. In late April, this changed allowing for days to be skipped if no new data was available. The day was still included, but the value of total cases was set to Null. The processing therefore was updated to include tracking of the spacing between intervals with valid values.100 News Cases in a day as a spike threshold: Empirically, this is based on COVID-19’s rate of spread, or r0 of ~2.5, which indicates each case will infect between two and three other people. There is a point at which each administrative area’s capacity will not have the resources to trace and account for all contacts of each patient. Thus, this is an indicator of uncontrolled or epidemic trend. Spiking activity in combination with the rate of new cases is the basis for determining whether an area has a spreading or epidemic trend (see below). Source used as basis:World Health Organization (WHO). 16-24 Feb 2020. Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Obtained online.Mean of Recent Tail of NCD = Empirical, and a COVID-19-specific basis for establishing a recent trend. The recent mean of NCD is taken from the most recent fourteen days. A minimum of 21 days of cases is required for analysis but cannot be considered reliable. Thus, a preference of 42 days of cases ensures much higher reliability. This analysis is not explanatory and thus, merely represents a likely trend. The tail is analyzed for the following:Most recent 2 days: In terms of likelihood, this does not mean much, but can indicate a reason for hope and a basis to share positive change that is not yet a trend. There are two worthwhile indicators:Last 2 days count of new cases is less than any in either the past five or 14 days. Past 2 days has only one or fewer new cases – this is an extremely positive outcome if the rate of testing has continued at the same rate as the previous 5 days or 14 days. Most recent 5 days: In terms of likelihood, this is more meaningful, as it does represent at short-term trend. There are five worthwhile indicators:Past five days is greater than past 2 days and past 14 days indicates the potential of the past 2 days being an aberration. Past five days is greater than past 14 days and less than past 2 days indicates slight positive trend, but likely still within peak trend time frame.Past five days is less than the past 14 days. This means a downward trend. This would be an

A dataset to advance the study of life-cycle interactions of biomedical and socioeconomic factors in the aging process. The EI project has assembled a variety of large datasets covering the life histories of approximately 39,616 white male volunteers (drawn from a random sample of 331 companies) who served in the Union Army (UA), and of about 6,000 African-American veterans from 51 randomly selected United States Colored Troops companies (USCT). Their military records were linked to pension and medical records that detailed the soldiers������?? health status and socioeconomic and family characteristics. Each soldier was searched for in the US decennial census for the years in which they were most likely to be found alive (1850, 1860, 1880, 1900, 1910). In addition, a sample consisting of 70,000 men examined for service in the Union Army between September 1864 and April 1865 has been assembled and linked only to census records. These records will be useful for life-cycle comparisons of those accepted and rejected for service. Military Data: The military service and wartime medical histories of the UA and USCT men were collected from the Union Army and United States Colored Troops military service records, carded medical records, and other wartime documents. Pension Data: Wherever possible, the UA and USCT samples have been linked to pension records, including surgeon''''s certificates. About 70% of men in the Union Army sample have a pension. These records provide the bulk of the socioeconomic and demographic information on these men from the late 1800s through the early 1900s, including family structure and employment information. In addition, the surgeon''''s certificates provide rich medical histories, with an average of 5 examinations per linked recruit for the UA, and about 2.5 exams per USCT recruit. Census Data: Both early and late-age familial and socioeconomic information is collected from the manuscript schedules of the federal censuses of 1850, 1860, 1870 (incomplete), 1880, 1900, and 1910. Data Availability: All of the datasets (Military Union Army; linked Census; Surgeon''''s Certificates; Examination Records, and supporting ecological and environmental variables) are publicly available from ICPSR. In addition, copies on CD-ROM may be obtained from the CPE, which also maintains an interactive Internet Data Archive and Documentation Library, which can be accessed on the Project Website. * Dates of Study: 1850-1910 * Study Features: Longitudinal, Minority Oversamples * Sample Size: ** Union Army: 35,747 ** Colored Troops: 6,187 ** Examination Sample: 70,800 ICPSR Link: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06836

Disease induced effects on host survival are important to understand the evolution of parasitic virulence and host resistance/tolerance. Unfortunately, experiments evaluating such effects are in most cases logistically demanding justifying the measurement of survival proxies. For plant hosts commonly used proxies are leaf area and the nature and severity of visual qualitative disease symptoms. In this study we tested whether these traits are indeed correlated to the host mortality rate induced by viral infection. We infected Brassica rapa and Arabidopsis thaliana plants with different natural isolates of Cauliflower mosaic virus (CaMV) and estimated over time the development of symptoms and the relative reduction of leaf area compared to healthy plants and followed plant mortality. We observed that the mortality of infected plants was correlated with the relative reduction of leaf area of both B. rapa and A. thaliana. Measures of mortality were also correlated with the severity of visual qualitative symptoms but the magnitude of the correlations and the time frame at which they were significant depended on the host plant: stronger and earlier correlations were observed on A. thaliana.

In March 2020, the World Health Organization declared the coronavirus disease 2019 (COVID-19) to be a pandemic, stating that those with underlying health conditions are most susceptible, including motor neuron disease (MND). To examine the effect the COVID-19 pandemic had on deaths from MND in the United States. Death certificate data for all MND deaths aged 20 years and older were analyzed from 2017 to 2019 (pre-COVID), then expanded to include 2020 and 2021 (COVID) deaths to evaluate if COVID-19 impacted MND deaths. The average number of MND deaths documented during the COVID-19 years was 8009, up from 7485 MND deaths pre-COVID. The age-adjusted mortality rate among the non-Hispanic population increased during COVID to 2.78 per 100,000 persons (95% CI = 2.73–2.82) from 1.81 (95% CI = 1.78–1.84). The Hispanic population also saw an increase in mortality rate during COVID (1.61, 95% CI = 1.51–1.71) compared with pre-COVID (1.10, 95% CI = 1.03–1.17). Decedent’s home as a place of death also saw a mortality rate increase during COVID (1.51, 95% CI = 1.48–1.54) compared with pre-COVID (1.30, 95% CI = 1.27–1.32). For the Hispanic population, the rate peaked at 80–84 years pre-COVID, but for the COVID years, the rate peaked earlier, at 75–79 years. The total number of MND deaths was greater during COVID than in the preceding years. The analysis suggests there might have been a consequence of circumstances surrounding the global pandemic and the associated restrictions.

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 728 ferrets inoculated with 126 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 Virol98: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. doi: 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