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.

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.

Updates

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

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

• April 9, 2020

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

• April 20, 2020

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

• April 29, 2020

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

• September 1st, 2020

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

• February 12, 2021

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

• February 16, 2021

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

  Overview

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

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

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

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

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

Queries

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

• Filter cases by state here

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

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

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

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

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

Interactive

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

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

Interactive Embed Code

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Caveats

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

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

Attribution

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

The death rate in Mozambique declined to seven deaths per 1,000 inhabitants in 2023. As a result, the death rate in Mozambique saw its lowest number in 2023 with seven deaths per 1,000 inhabitants. Notably, the death rate is continuously decreasing over the last years.The crude death rate is the annual number of deaths in a given population, expressed per 1,000 people. When looked at in unison with the crude birth rate, the rate of natural increase can be determined.Find more statistics on other topics about Mozambique with key insights such as crude birth rate, total life expectancy at birth, and total fertility rate.

NOTE: This dataset has been retired and marked as historical-only.

This dataset is a companion to the COVID-19 Daily Cases and Deaths dataset (https://data.cityofchicago.org/d/naz8-j4nc). The major difference in this dataset is that the case, death, and hospitalization corresponding rates per 100,000 population are not those for the single date indicated. They are rolling averages for the seven-day period ending on that date. This rolling average is used to account for fluctuations that may occur in the data, such as fewer cases being reported on weekends, and small numbers. The intent is to give a more representative view of the ongoing COVID-19 experience, less affected by what is essentially noise in the data.

All rates are per 100,000 population in the indicated group, or Chicago, as a whole, for “Total” columns.

Only Chicago residents are included based on the home address as provided by the medical provider.

Cases with a positive molecular (PCR) or antigen test are included in this dataset. Cases are counted based on the date the test specimen was collected. Deaths among cases are aggregated by day of death. Hospitalizations are reported by date of first hospital admission. Demographic data are based on what is reported by medical providers or collected by CDPH during follow-up investigation.

Denominators are from the U.S. Census Bureau American Community Survey 1-year estimate for 2018 and can be seen in the Citywide, 2018 row of the Chicago Population Counts dataset (https://data.cityofchicago.org/d/85cm-7uqa).

All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. At any given time, this dataset reflects cases and deaths currently known to CDPH.

Numbers in this dataset may differ from other public sources due to definitions of COVID-19-related cases and deaths, sources used, how cases and deaths are associated to a specific date, and similar factors.

Data Source: Illinois National Electronic Disease Surveillance System, Cook County Medical Examiner’s Office, U.S. Census Bureau American Community Survey

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

The child mortality rate in the United States, for children under the age of five, was 462.9 deaths per thousand births in 1800. This means that for every thousand babies born in 1800, over 46 percent did not make it to their fifth birthday. Over the course of the next 220 years, this number has dropped drastically, and the rate has dropped to its lowest point ever in 2020 where it is just seven deaths per thousand births. Although the child mortality rate has decreased greatly over this 220 year period, there were two occasions where it increased; in the 1870s, as a result of the fourth cholera pandemic, smallpox outbreaks, and yellow fever, and in the late 1910s, due to the Spanish Flu pandemic.

BackgroundThe exponential growth of the cancer burden attributable to metabolic factors deserves global attention. We investigated the trends of cancer mortality attributable to metabolic factors in 204 countries and regions between 1990 and 2019.MethodsWe extracted data from the Global Burden of Disease Study (GBD) 2019 and assessed the mortality, age-standardized death rate (ASDR), and population attributable fractions (PAFs) of cancers attributable to metabolic factors. Average annual percentage changes (AAPCs) were calculated to assess the changes in the ASDR. The cancer mortality burden was evaluated according to geographic location, SDI quintiles, age, sex, and changes over time.ResultsCancer attributable to metabolic factors contributed 865,440 (95% UI, 447,970-140,590) deaths in 2019, a 167.45% increase over 1990. In the past 30 years, the increase in the number of deaths and ASDR in lower SDI regions have been significantly higher than in higher SDI regions (from high to low SDIs: the changes in death numbers were 108.72%, 135.7%, 288.26%, 375.34%, and 288.26%, and the AAPCs were 0.42%, 0.58%, 1.51%, 2.36%, and 1.96%). Equatorial Guinea (AAPC= 5.71%), Cabo Verde (AAPC=4.54%), and Lesotho (AAPC=4.42%) had the largest increase in ASDR. Large differences were observed in the ASDRs by sex across different SDIs, and the male-to-female ratios of ASDR were 1.42, 1.50, 1.32, 0.93, and 0.86 in 2019. The core population of death in higher SDI regions is the age group of 70 years and above, and the lower SDI regions are concentrated in the age group of 50-69 years. The proportion of premature deaths in lower SDI regions is significantly higher than that in higher SDI regions (from high to low SDIs: 2%, 4%, 7%, 7%, and 9%). Gastrointestinal cancers were the core burden, accounting for 50.11% of cancer deaths attributable to metabolic factors, among which the top three cancers were tracheal, bronchus, and lung cancer, followed by colon and rectum cancer and breast cancer.ConclusionsThe cancer mortality burden attributable to metabolic factors is shifting from higher SDI regions to lower SDI regions. Sex differences show regional heterogeneity, with men having a significantly higher burden than women in higher SDI regions but the opposite is observed in lower SDI regions. Lower SDI regions have a heavier premature death burden. Gastrointestinal cancers are the core of the burden of cancer attributable to metabolic factors.

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

The Global Diphtheria Vaccine Sales Market size is expected to grow from $1.6 billion in 2018 to $XX billion by 2028, representing a compound annual growth rate (CAGR) of 5.3%. The growth in this market can be attributed to factors such as growing awareness about the disease and vaccination programs for infants and children, increasing concern over the inadequacy of current vaccines against certain strains of diphtheria, and increased government support for research on new vaccines against diphtheria in countries.

Diphtheria Vaccine Sales:

Diphtheria is a highly contagious bacterial disease that can cause severe respiratory problems, heart failure, and death. The diphtheria vaccine is used to prevent this disease. There are different types of diphtheria vaccine available, such as DTaP (diphtheria, Tetanus, and pertussis), DT (diphtheria and tetanus), Tdap (tetanus, diphtheria, and pertussis) for adults. Diphtheria is a bacterial infection that can cause serious problems, such as difficulty breathing and heart failure. The diphtheria vaccine is used to help protect against this disease. There are different types of the vaccine, depending on when it is given: DTaP for infants and children younger than seven years of age, DT for children seven years of age and older, Tdap for adolescents and adults.

On the basis of Types, the market is segmented into DTaP, DT, Td, Tdap.

DTaP:

DTaP is a Combination Vaccine that combines protection against three diseases: diphtheria, tetanus, and pertussis (whooping cough). DTaP protects children from these serious diseases caused by bacteria. Diphtheria and whooping cough are spread from person to person through coughing or sneezing. Tetanus enters the body through cuts or wounds. DTaP is a vaccine that helps protect infants and children younger than seven years of age from three diseases: diphtheria, tetanus, and pertussis. DTaP is usually given as a series of five shots.

DT:

DT is diphtheria and tetanus vaccine that is given as a shot. DT helps protect children seven years of age and older, adolescents, and adults from two diseases: diphtheria and tetanus. Diphtheria is a bacterial infection that can cause serious problems, such as difficulty breathing and heart failure. DT is a vaccine that protects against diphtheria and tetanus (lockjaw). The diphtheria part of DTaP helps children younger than seven years of age develop immunity to the bacteria that cause this disease. Tetanus enters the body through cuts or wounds.

Td:

Td is tetanus and diphtheria vaccine. Td protects adolescents and adults from two diseases: tetanus and diphtheria. Td is a booster shot that helps protect adolescents and adults from two diseases: diphtheria and tetanus. Diphtheria is a bacterial infection that can cause serious problems, such as difficulty breathing and heart failure. Tetanus enters the body through cuts or wounds. Td is usually given every ten years as a booster shot to help protect adolescents and adults from two diseases: diphtheria and tetanus.

On the basis of Application, the market is segmented into For infants and children younger than seven years of age, For adolescents and adults.

For infants and children younger than seven years of age:

The diphtheria vaccine is used to prevent this disease in infants and children. The diphtheria vaccine is given to infants and children younger than seven years of age. It helps protect them from getting the bacterial infection that causes diphtheria. Also, DTaP is required for school entry in some states, so it's important your child gets all doses on schedule. The vaccination schedule begins with a series of two shots at two and four months of age, followed by a booster shot at six years of age.

For adolescents and adults:

The diphtheria vaccine is also used to protect adolescents and adults from getting the bacterial infection that causes diphtheria. The vaccination schedule for adolescents and adults begins with a series of two shots, followed by a booster shot every ten years. Protection against diphtheria can last up to 20 years. The diphtheria vaccine is used to prevent this disease in infants and children, adolescents, and adults.

On the basis of Region, the market is segmented into North America, Latin America, Europe, Asia Pacific, and the Middle East & Africa. North America is expected to hold the largest market share in 2028. Latin America is expected to be the fastest-growing market during the forecast period. Europe is expected to be the second-largest market for Diphtheria Vaccines dur

The global Caspase Activity Assay Kit market is experiencing robust growth, driven by the increasing prevalence of chronic diseases necessitating extensive research in apoptosis and cell death mechanisms. The market, estimated at $150 million in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033. This growth is fueled by advancements in research techniques, the rising demand for accurate and reliable assay kits in research laboratories and hospitals, and the expanding application of caspase assays in drug discovery and development. Key market segments include Caspase 3/7, Caspase 8, and Caspase 9 activity assay kits, reflecting the significance of these caspases in various apoptotic pathways. Geographical expansion, particularly in rapidly developing economies of Asia-Pacific, is also contributing to market expansion. The market's growth trajectory is influenced by several factors. Technological innovations leading to more sensitive and user-friendly assay kits are attracting wider adoption. Increased government funding for research and development in life sciences further bolsters market growth. However, challenges such as the high cost of advanced assay kits and the potential for variability in assay results due to experimental conditions pose some restraints. Competitive landscape analysis reveals a mix of established players and emerging companies offering a diverse range of caspase assay kits, contributing to market dynamism. Future growth will depend on the continuous development of innovative products, strategic partnerships, and effective market penetration strategies by key players. This report provides a detailed analysis of the global Caspase Activity Assay Kit market, projecting substantial growth in the coming years. We delve into market segmentation, key players, emerging trends, and challenges impacting this crucial sector of life science research. The report leverages extensive market data and industry expertise to provide actionable insights for businesses and researchers alike. Keywords: Caspase 3 Assay, Caspase 8 Assay, Apoptosis Assay, Caspase Activity, Cell Death Assay, Caspase 3/7 Assay, Apoptosis Research, Drug Discovery, Biotechnology.

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The child mortality rate in Africa has steadily declined over the past seven decades. In 2023, it reached 63 deaths per thousand births. In 1950, child mortality was significantly higher, estimated at 327 deaths per thousand births, meaning that almost one-third of all children born in these years did not make it to their fifth birthday. While the reduction rate varies on a country-by-country basis, the overall decline can be attributed in large part to the expansion of healthcare services, improvements in nutrition and access to clean drinking water, and the implementation of large-scale immunization campaigns across the continent. The temporary slowdown in the 1980s and 1990s has been attributed in part to rapid urbanization of many parts of the continent that coincided with poor economic performance, resulting in the creation of overcrowded slums with poor access to health and sanitation services. Despite significant improvements in the continent-wide averages, there remains a significant imbalance in the continent, with Sub-Saharan countries experiencing much higher child mortality rates than those in North Africa.

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Abstract

The 2008 Nigeria Demographic Health Survey (NDHS) is a nationally representative survey of 33,385 women age 15-49 and 15,486 men age 15-59. The 2008 NDHS is the fourth comprehensive survey conducted in Nigeria as part of the Demographic and Health Surveys (DHS) programme. The data are intended to furnish programme managers and policymakers with detailed information on levels and trends in fertility; nuptiality; sexual activity; fertility preferences; awareness and use of family planning methods; infants and young children feeding practices; nutritional status of mothers and young children; early childhood mortality and maternal mortality; maternal and child health; and awareness and behaviour regarding HIV/AIDS and other sexually transmitted infections. Additionally, the 2008 NDHS collected information on malaria prevention and treatment, neglected tropical diseases, domestic violence, fistulae, and female genital cutting (FGC).

The 2008 Nigeria Demographic and Health Survey (2008 NDHS) was implemented by the National Population Commission from June to October 2008 on a nationally representative sample of more than 36,000 households. All women age 15-49 in these households and all men age 15-59 in a sub-sample of half of the households were individually interviewed.

While significantly expanded in content, the 2008 NDHS is a follow-up to the 1990, 1999, and 2003 NDHS surveys and provides updated estimates of basic demographic and health indicators covered in these earlier surveys. In addition, the 2008 NDHS includes the collection of information on violence against women. Although previous surveys collected data at the national and zonal levels, the 2008 NDHS is the first NDHS survey to collect data on basic demographic and health indicators at the state level.

The primary objectives of the 2008 NDHS project were to provide up-to-date information on fertility levels; nuptiality; sexual activity; fertility preferences; awareness and use of family planning methods; breastfeeding practices; nutritional status of mothers and young children; early childhood mortality and maternal mortality; maternal and child health; and awareness and behaviour regarding HIV/AIDS and other sexually transmitted infections.

MAIN FINDINGS

FERTILITY

The survey results show fertility in Nigeria has remained at a high level over the last 17 years from 5.9 births per woman in 1991 to 5.7 births in 2008. On average, rural women are having two children more than urban women (6.3 and 4.7 children, respectively). Fertility differentials by education and wealth are noticeable. Women who have no formal education and women in the lowest wealth quintile on average are having 7 children, while women with higher than a secondary education are having 3 children and women in the highest wealth quintile are having 4 children.

FAMILY PLANNING

In the 2008 NDHS, 72 percent of all women and 90 percent of all men know at least one contraceptive method. Male condoms, the pill, and injectables are the most widely known methods.

Twenty-nine percent of currently married women have used a family planning method at least once in their lifetime. Fifteen percent of currently married women are using any contraceptive method and 10 percent are using a modern method. The most commonly used methods among currently married women are injectables (3 percent), followed by male condoms and the pill (2 percent each).

Current use of contraception in Nigeria has increased from 6 percent in 1990 and 13 percent in 2003 to 15 percent in 2008. There has been a corresponding increase in the use of modern contraceptive methods, from 4 percent in 1990 and 8 percent in 2003 to 10 percent in 2008.

CHILD HEALTH

Data from the 2008 NDHS indicate that the infant mortality rate is 75 deaths per 1,000 live births, while the under-five mortality rate is 157 per 1,000 live births for the five-year period immediately preceding the survey. The neonatal mortality rate is 40 per 1,000 births. Thus, almost half of childhood deaths occurred during infancy, with one-quarter taking place during the first month of life.

Child mortality is consistently lower in urban areas than in rural areas. There is also variation in the mortality level across zones. The infant mortality and under-five mortality rates are highest in the North East, and lowest in the South West.

In Nigeria, children are considered fully vaccinated when they receive one dose of BCG vaccine, three doses of DPT vaccine, three doses of polio vaccine, and one dose of measles vaccine. Overall, 23 percent of children 12-23 months have received all vaccinations at the time of the survey. Fifty percent of children have received the BCG vaccination, and 41 percent have been vaccinated against measles. The coverage of the first dose of DPT vaccine and polio 1 is 52 and 68 percent, respectively). However, only 35 percent of children have received the third dose of DPT vaccine, and 39 percent have received the third dose of polio vaccine. A comparison of the 2008 NDHS results with those of the earlier surveys shows there has been an increase in the overall vaccination coverage in Nigeria from 13 percent in 2003 to the current rate of 23 percent. However, the percentage of children with no vaccinations has not improved for the same period, 27 percent in 2003 and 29 percent in 2008.

MATERNAL HEALTH

In Nigeria more than half of women who had a live birth in the five years preceding the survey received antenatal care from a health professional (58 percent); 23 percent from a doctor, 30 percent from a nurse or midwife, and 5 percent from an auxiliary nurse or midwife. Thirty-six percent of mothers did not receive any antenatal care.

Tetanus toxoid injections are given during pregnancy to prevent neonatal tetanus. Overall, 48 percent of last births in Nigeria were protected against neonatal tetanus.

More than one-third of births in the five years before the survey were delivered in a health facility (35 percent). Twenty percent of births occurred in public health facilities and 15 percent occurred in private health facilities. Almost two-thirds (62 percent) of births occurred at home. Nine percent of births were assisted by a doctor, 25 percent by a nurse or midwife, 5 percent by an auxiliary nurse or midwife, and 22 percent by a traditional birth attendant. Nineteen percent of births were assisted by a relative and 19 percent of births had no assistance at all. Two percent of births were delivered by a caesarean section.

Overall, 42 percent of mothers received a postnatal check-up for the most recent birth in the five years preceding the survey, with 38 percent having the check-up within the critical 48 hours after delivery.

Results from the 2008 NDHS show that the estimated maternal mortality ratio during the seven-year period prior to the survey is 545 maternal deaths per 100,000 live births.

BREASTFEEDING AND NUTRITION

Ninety-seven percent of Nigerian children under age five were breastfed at some point in their life. The median breastfeeding duration in Nigeria is long (18.1 months). On the other hand, the median duration for exclusive breastfeeding is only for half a month. A small proportion of babies (13 percent) are exclusively breastfed throughout the first six months of life. More than seven in ten (76 percent) children age 6-9 months receive complementary foods. Sixteen percent of babies less than six months of age are fed with a bottle with a nipple, and the proportion bottle-fed peaks at 17 percent among children in the age groups 2-3 months and 4-5 months.

Anthropometric measurements carried out at the time of the survey indicate that, overall, 41 percent of Nigerian children are stunted (short for their age), 14 percent are wasted (thin for their height), and 23 percent are underweight. The indices show that malnutrition in young children increases with age, starting with wasting, which peaks among children age 6-8 months, underweight peaks among children age 12-17 months, and stunting is highest among children age 18-23 months. Stunting affects half of children in this age group and almost one-third of children age 18-23 months are severely stunted.

Overall, 66 percent of women have a body mass index (BMI) in the normal range; 12 percent of women are classified as thin and 4 percent are severely thin. Twenty-two percent of women are classified as overweight or obese, with 6 percent in the latter category.

MALARIA

Seventeen percent of all households interviewed during the survey had at least one mosquito net, while 8 percent had more than one. Sixteen percent of households had at least one net that had been treated at some time (ever-treated) with an insecticide. Eight percent of households had at least one insecticide-treated net (ITN).

Mosquito net usage is low among young children and pregnant women, groups that are particularly vulnerable to the effects of malaria. Overall, 12 percent of children under five slept under a mosquito net the night before the survey. Twelve percent of children slept under an ever-treated net and 6 percent slept under an ITN. Among pregnant women, 12 percent slept under any mosquito net the night before the interview. Twelve percent slept under an ever-treated net and 5 percent slept under an ITN.

Among women who had their last birth in the two years before the survey, 18 percent took an anti-malarial drug during the pregnancy. Eleven percent of all pregnant women took at least one dose of a sulphadoxine-pyrimethamine (SP) drug such as Fansidar, Amalar, or Maloxine, while 7 percent reported taking two or more doses of an SP drug. Eight percent of the women who took an SP drug were given the drug during an antenatal care visit, a practice known as intermittent preventive treatment (IPT).

HIV/AIDS KNOWLEDGE AND BEHAVIOUR

The majority of

Road Traffic Accidents (RTA) are a major worldwide public health problem. The aim of this study was to use the growth mixture model for clustering countries on the basis of the mortality rate patterns of RTAs from 2007 to 2013. We obtained the data on RTA death rates from World Health Organization reports and Human Development Index (HDI) of United Nations Development Programme reports for the years 2007, 2010 and 2013. Simple Latent Growth Models (LGM) in 181 countries were applied to estimate overall RTA mortality rate growth trajectories and the latent growth mixture modeling utilized to cluster them. According to non-linear LGM, the overall mortality rate of RTAs showed a decrease from 2007 to 2010 followed by an increase from 2010 to 2013. The HDI covariate had a significant negative and positive effect on intercept and slope of the LGM, respectively. The extracted mixture model appeared to have seven classes with different trends in RTA mortality rates. The worldwide countries were clustered into seven classes. Further studies on each of the seven classes are suggested to provide recommendations for reducing the mortality rate of the RTAs. Additionally, increasing HDI in some countries could have a significant effect on reducing the RTA death rates.

Summary statistics of pollutant concentrations and meteorological variables in Rio de Janeiro, Brazil (2012–2017).

The massive meat production of broiler chickens make them continuously exposed to potential stressors that stimulate releasing of stress-related hormones like corticosterone (CORT) which is responsible for specific pathways in biological mechanisms and physiological activities. Therefore, this research was conducted to evaluate a wide range of responses related to broiler performance, immune function, plasma biochemistry, related gene expressions and cell death morphology during and after a 7-day course of CORT injection. A total number of 200 one-day-old commercial Cobb broiler chicks were used in this study. From 21 to 28 d of age, broilers were randomly assigned to one of 2 groups with 5 replicates of 20 birds each; the first group received a daily intramuscular injection of 5 mg/kg BW corticosterone dissolved in 0.5 ml ethanol:saline solution (CORT group), while the second group received a daily intramuscular injection of 0.5 ml ethanol:saline only (CONT group). Growth performance, including body weight (BW), daily weight gain (DG), feed intake (FI) and feed conversion ratio (FC), were calculated at 0, 3 and 7 d after the start of the CORT injections. At the same times, blood samples were collected in each group for hematological (TWBC’s and H/L ratio), T- and B-lymphocytes proliferation and plasma biochemical assays (total protein, TP; free triiodothyronine hormone, fT3; aspartate amino transaminase, AST; and alanine amino transaminase, ALT). The liver, thymus, bursa of Fabricius and spleen were dissected and weighed, and the mRNA expression of insulin-like growth factor 1 gene (IGF-1) in liver and cell-death-program gene (caspase-9) in bursa were analyzed for each group and time; while the apoptotic/necrotic cells were morphologically detected in the spleen. From 28 to 35 d of age, broilers were kept for recovery period without CORT injection and the same sampling and parameters were repeated at the end (at 14 d after initiation of the CORT injection). In general, all parameters of broiler performance were negatively affected by the CORT injection. In addition, CORT treatment decreased the plasma concentration of fT3 and the mRNA expression of hepatic IGF-1. A significant increase in liver weight accompanied by an increase in plasma TP, AST and ALT was observed with CORT treatment, indicating an incidence of liver malfunction by CORT. Moreover, the relative weights of thymus, bursa and spleen decreased by the CORT treatment with low counts of TWBC’s and low stimulation of T & B cells while the H/L ratio increased; indicating immunosuppressive effect for CORT treatment. Furthermore, high expression of caspase-9 gene occurred in the bursa of CORT-treated chickens, however, it was associated with a high necrotic vs. low apoptotic cell death pathway in the spleen. Seven days after termination of the CORT treatment in broilers, most of these aspects remained negatively affected by CORT and did not recover to its normal status. The current study provides a comprehensive view of different physiological modulations in broiler chickens by CORT treatment and may set the potential means to mount a successful defense against stress in broilers and other animals as well.

Since 1970, the worst terrorist attack in the United Kingdom was the downing of Pan Am Flight 103 in December 1988 above Lockerbie, Scotland, which caused 270 fatalities. Of the ten attacks worst terrorist attacks in the UK, seven were related to the troubles in Northern Ireland, while two were related to Islamic extremism. The London 7/7 bombings in 2005, which caused the deaths of 56 people, and the Manchester Arena bombing in May 2017, which claimed the lives of 23 people. Are terrorist attacks in the UK on the rise? When compared with earlier decades, the number of terrorist attacks in the UK has declined considerably. Throughout the 1970s, there were approximately 1,644 terrorist attacks, 1,315 in the 1980s, and 1,214 in the 1990s, compared with just 325 in the 2000s, and 925 in the 2010s. During this period, the year with the highest number of attacks was 1972 when there were 292. Although the period between 2013 and 2018 saw a high volume of terrorist attacks, this has started to decline in more recent years. In 2021, for example there were 40 terrorist attacks, the fewest since 2009. A global threat In 2023, 21,596 people lost their lives due to terrorism, with Syria suffering the highest number of attacks at 1,405. The UK was not alone in seeing the intensity of terror attacks increase in the mid-2010s, either. The Paris Attacks of November 2015 caused 130 fatalities, while the Nice attacks in 2016 led to 86 deaths, some of the worst terrorist incidents to have ever taken place in Europe. Due to these attacks, the number of deaths caused by terrorism was significantly higher in 2015 and 2016 than in other years, at 152, and 142 respectively.