Between the beginning of January 2020 and June 14, 2023, of the 1,134,641 deaths caused by COVID-19 in the United States, around 307,169 had occurred among those aged 85 years and older. This statistic shows the number of coronavirus disease 2019 (COVID-19) deaths in the U.S. from January 2020 to June 2023, by age.

Provisional deaths registration data for single year of age and average age of death (median and mean) of persons whose death involved coronavirus (COVID-19), England and Wales. Includes deaths due to COVID-19 and breakdowns by sex.

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.

The coronavirus (COVID-19) has led to over 183,000 deaths in Germany, as of 2024. When looking at the distribution of deaths by age, based on the figures currently available, most death occurred in the age group 80 years and older at approximately 118,938 deaths.

The seven-day average number of COVID-19 deaths in the U.S. decreased significantly from April to July 2020, but it remained higher than in other countries. Seven-day rolling averages are used to adjust for administrative delays in the reporting of deaths by authorities, commonly over weekends.

The challenges of tracking and reporting the disease The U.S. confirmed its first coronavirus case in mid-January 2020 – the virus was detected in a passenger who arrived in Seattle from China. Since that first case, around 945 people have died every day from COVID-19 in the United States as of August 23, 2020. In total, the U.S. has recorded more coronavirus deaths than any other country worldwide. Accurately tracking the number of COVID-19 deaths has proved complicated, with countries having different rules for what deaths to include in their official figures. Some nations have even changed which deaths they can attribute to the disease during the pandemic.

Young people urged to act responsibly Between January and May 2020, case fatality rates among COVID-19 patients in the United States increased with age, highlighting the particular risks faced by the elderly. However, COVID-19 is not only a disease that affects older adults. Surges in the number of new cases throughout July 2020 were blamed on young people. The World Health Organization has urged young people not to become complacent, reminding them to maintain social distancing guidelines and take precautions to protect themselves and others.

As of April 26, 2023, around 27 percent of total COVID-19 deaths in the United States have been among adults 85 years and older, despite this age group only accounting for two percent of the U.S. population. This statistic depicts the distribution of total COVID-19 deaths in the United States as of April 26, 2023, by age group.

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.

As of January 11, 2023, the highest number of deaths due to the coronavirus in Sweden was among individuals aged 80 to 90 years old. In this age group there were 9,124 deaths as a result of the virus. The overall Swedish death toll was 22,645 as of January 11, 2023.

The first case of coronavirus (COVID-19) in Sweden was confirmed on February 4, 2020. The number of cases has since risen to over 2.68 million, as of January 2023. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

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.

It is estimated that in 2020 the COVID-19 pandemic caused around 762,927 excess deaths among females worldwide aged 80 years and older. This statistic shows the mean number of excess deaths associated with the COVID-19 pandemic from all-causes worldwide in 2020, by age and gender.

Age-standardised mortality rates for deaths involving coronavirus (COVID-19), non-COVID-19 deaths and all deaths by vaccination status, broken down by age group.

The spread of coronavirus (COVID-19) in Italy has hit every age group uniformly and claimed over 190 thousand lives since it entered the country. As the chart shows, however, mortality rate appeared to be much higher for the elderly patient. In fact, for people between 80 and 89 years of age, the fatality rate was 6.1 percent. For patients older than 90 years, this figure increased to 12.1 percent. On the other hand, the death rate for individuals under 60 years of age was well below 0.5 percent. Overall, the mortality rate of coronavirus in Italy was 0.7 percent.

Italy's death toll was one of the most tragic in the world. In the last months, however, the country started to see the end of this terrible situation: as of May 2023, roughly 84.7 percent of the total Italian population was fully vaccinated.

Since the first case was detected at the end of January in Italy, coronavirus has been spreading fast. As of May, 2023, the authorities reported over 25.8 million cases in the country. The area mostly hit by the virus is the North, in particular the region of Lombardy.

For a global overview visit Statista's webpage exclusively dedicated to coronavirus, its development, and its impact.

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

Context

From World Health Organization - On 31 December 2019, WHO was alerted to several cases of pneumonia in Wuhan City, Hubei Province of China. The virus did not match any other known virus. This raised concern because when a virus is new, we do not know how it affects people.

So daily level information on the affected people can give some interesting insights when it is made available to the broader data science community.

The European CDC publishes daily statistics on the COVID-19 pandemic. Not just for Europe, but for the entire world. We rely on the ECDC as they collect and harmonize data from around the world which allows us to compare what is happening in different countries.

Content

This dataset has daily level information on the number of affected cases, deaths and recovery etc. from coronavirus. It also contains various other parameters like average life expectancy, population density, smocking population etc. which users can find useful in further prediction that they need to make.

The data is available from 31 Dec,2019.

Inspiration

Give people weekly data so that they can use it to make accurate predictions.

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.

After entering Italy, coronavirus (COVID-19) has been spreading fast. An analysis of the individuals who died after contracting the virus revealed that the vast majority of deaths occurred among the elderly. As of May, 2023, roughly 85 percent were patients aged 70 years and older.

Italy's death toll was one of the most tragic in the world. In the last months, however, the country saw the end to this terrible situation: as of May 2023, roughly 84.7 percent of the total Italian population was fully vaccinated.

As of May, 2023, the total number of cases reported in the country were over 25.8 million. The North of the country was the mostly hit area, and the region with the highest number of cases was Lombardy.

For a global overview visit Statista's webpage exclusively dedicated to coronavirus, its development, and its impact.

This study estimates the economic losses (GDP), particularly the impact of COVID-19 deaths on non-health components of GDP in West Bengal state. The NHGDP losses were evaluated using cost-of-illness approach. Future NHGDP losses were discounted at 3%. Excess death estimates by the World Health Organisation (WHO) and Global Burden of Disease (GBD) were used. Sensitivity analysis was carried out by varying discount rates and Average Age of Death (AAD). 21,532 deaths in West Bengal since 17th March 2020 till 31st December 2022 decreased the future NHGDP by $0.92 billion. Nearly 90% of loss was due to deaths occurring in above 30 years age-group. The majority of the loss was borne among the 46–60 years age-group. The NHGDP loss/death was $42,646, however, the average loss/death declined with a rise in age. The loss increased to $9.38 billion and $9.42 billion respectively based on GBD and WHO excess death estimates. The loss increased to $1.3 billion by considering the lower age of the interval as AAD. At 5% and 10% discount rates, the losses reduced to $0.769 billion and $0.549 billion respectively. Results from the study suggest that COVID-19 contributed to major economic loss in West Bengal. The mortality and morbidity caused by COVID-19, the substantial economic costs at individual and population levels in West Bengal, and probably across India and other countries, is another argument for better infection control strategies across the globe to end the impact of this epidemic. Methods Various open domains were used to gather data on COVID-19 deaths in West Bengal and the aforementioned estimates. Economic losses in terms of Non-Health Gross Domestic Product (NHGDP)among six age-group brackets viz. 0–15, 16–30, 31–45, 46–60, 61–75 and 75 and above were estimated to facilitate comparisons and to initiate advocacy for an increase in health investments against COVID-19. This study used midpoint age as the age of death for all the age brackets. The legal minimum age for working i.e., 15 years. A sensitivity analysis was conducted to determine the effect of age on the overall total NHGDP loss estimate. The model was re-estimated assuming an average age at death to be the starting age of each age-group bracket. Based on existing literature discounted rate of interest to measure the value of life is taken as 2.9%. As a sensitivity analysis, NHGDP loss has also been computed using 5% and 10% of discounted rates of interest.

ABSTRACT Background : The Covid-19 pandemic has caused very high death tolls across the world in the last two years. Geographic latitude, climate factors, and other human related conditions such as epidemiologic and demographic history are taught to have played a role in the prevalence of Covid-19. Objective : This observational study aimed to investigate possible relations between geographic latitude-associated climate factors and Covid-19 death numbers in 29 countries. The study also aimed to investigate the relationship between geographic latitude and the history of epidemiologic (cancer, Alzheimer's disease) and demographic factors (birth rate, mortality rate, fertility rate, people aged 65 and over), as well as alcohol intake habits. And finally, the study also aimed to evaluate the relationships between epidemiologic and demographic factors, as well as alcohol intake habits with Covid-19 deaths. Methods : We sought the Covid-19 death toll in 29 countries in Europe, Africa, and the Middle East (located in both hemispheres and between the meridian lines "-15°" and "+50°"). We obtained the death numbers for Covid-19 and other geographic (latitude, longitude) and climate factors (average annual temperature, sunshine hours, and UV index) and epidemiologic and demographic parameters as well as data on alcohol intake per capita from official web pages. Based on records of epidemiologic and demographic history, and alcohol intake data, we have calculated a General Immune Capacity (GIC) score for each country. Geographic latitude and climate factors were plotted against each of Covid-19 death numbers, epidemiologic and demographic parameters, and alcohol intake per capita. Data was analysed by simple linear regression or polynomial regression. All statistical data was collected using Microsoft Excell software (2016). Results : Our observational study found higher death numbers in the higher geographic latitudes of both hemispheres, while lower scores of deaths were registered in countries located around the equator line and low latitudes. When the Covid-19 death numbers were plotted against the geographic latitude of each country, an inverted bell-shaped curve was obtained (coefficient of determination R2=0.553). In contrast, bell-shaped curves were obtained when latitude was plotted against annual average temperature (coefficient of determination R2= 0.91), average annual sunshine hours (coefficient of determination R2= 0.79) and average annual UV index (coefficient of determination R2= 0.89). In addition, plotting the latitude of each country against the General Immune Capacity score of each country gave an inverted bell-shaped curve (coefficient of determination R2=0.755). Linear regression analysis of the General Immune Capacity score of each country and its Covid-19 deaths showed a very significant negative correlation (coefficient of determination R² = 0,71, p=6.79x10-9). Linear regression analysis of the Covid-19 death number plotted against the average annual temperature temperature and the average annual sunshine hours or the average annual UV index gave very significant negative correlations with the following coefficients of determination: (R2 = 0.69, p = 1.94x10-8), (R2 = 0.536, p = 6.31x10-6) and (R2 = 0.599, p = 8.30x10-7), respectively. Linear regression analysis of the General Immune Capacity score of each country plotted against its average annual temperature temperature and the average annual sunshine hours or the average annual UV index gave very significant negative correlations, with the following coefficients of determination: (R2 = 0.86, p = 3.63x10-13), (R2 = 0.69, p = 2.18x10-8) and (R2 = 0.77, p= 2.47x10-10), respectively. Conclusion : The results of the present study prove that at certain geographic latitudes and their three associated climate parameters are negatively correlated to Covid-19 mortality. On the other hand, our data showed that the General Immune Capacity score, which includes many human related parameters, is inversely correlated to Covid-19 mortality. Likewise, geographic location and health and demographic history were key elements in the prevalence of the Covid-19 pandemic in a given country. On the other hand, the study points to the possible protective role of UV light against Covid-19. The therapeutic potential of UV light against the Covid-19 associated with SARS-Cov-2 is discussed.

ABSTRACT Background: Following two years of the Covid-19 pandemic, thousands of deaths were registered around the world, however, death tolls differed from a country to another. A question on whether climate parameters in each country could or not affects coronavirus incidence and Covid-19 death toll is under debate. Objective: In the present work, it is aimed to check the numbers of deaths caused by Covid-19 in 39 countries of four continents (America, Europe, Africa and Asia), and to analyse their possible correlation with climate parameters in a given country, such as the mean of annual temperature, the annual average sunshine hours and the annual average UV index in each country. Methods: We have sought the deaths number caused by Covid-19 in 39 countries and have analysed its correlation degree with the mean annual temperature, the average annual sunshine hours and the average annual UV index. Correlation and determination factors were obtained by Microsoft Exell software (2016). Results: In the present study, higher numbers of deaths related to Covid-19 were registered in many countries of Europe and America compared to other countries in Africa and Asia. On the other hand, after both the first year and the second year of the pandemic, the death numbers registered in the 39 countries of our study were very negatively correlated with the three climate factors of our study, namely, annual average temperature, sunshine hours and UV index. Conclusion:The results of the present study prove that the above climate parameters may have some kind of influence on the coronavirus incidence through a yet unknown mechanism. Our data support the hypothesis that countries which have elevated annual temperatures and elevated sunshine hours may be less vulnerable to the coronavirus SARS-CoV-2 and to its associated Covid-19 disease. Countries with the above characteristics have also elevated levels of average annual UV rays that might play a key role against the spread of the coronavirus.Thus, geographical latitude and longitude of a given country could have been the key points for the outcome of virus incidence and Covid-19 spread around the globe during the past two years. The results prove that elevated levels of temperature, sunshine hours and UV index could play a protective effect against the coronavirus, although their mechanisms of action are still unknown.

BackgroundThis study aimed to describe the temporal trends in the age and sex burdens of lower respiratory infections (LRIs) in China and globally from 1990 to 2021 and to analyze their epidemiological characteristics to formulate corresponding strategies to control LRIs.MethodsThis study utilized open data from the Global Burden of Disease (GBD) database from 1990 to 2021 to assess the burden of disease based on the prevalence, incidence, mortality, years lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) of LRIs in China and globally. Moreover, a comprehensive comparative analysis of the epidemiological characteristics of LRIs in China and globally was conducted via the Joinpoint regression model, age-period-cohort model (APC model), and stratified analysis of the study method from multiple dimensions, such as age, sex, and period. Finally, we used an autoregressive integrated moving average (ARIMA) model to predict the disease burden in LRIs over the next 15 years.ResultsFrom 1990 to 2021, China's age-standardized incidence, deaths, and disability-adjusted life year (DALY) rates per 100,000 people decreased from 5,481.13 (95% CI: 5,149.05, 5,836.35) to 2,853.81 (95% CI: 2,663.94, 3,067.55), from 60.65 (95% CI. 52.96, 66.66) to 14.03 (95% CI: 11.68, 17) and from 3,128.39 (95% CI: 2,724.11, 3,579.57) to 347.67 (95% CI: 301.28, 402.94). The global age-standardized incidence, deaths, and DALY rates per 100,000 people, on the other hand, decreased from 6,373.17 (95% CI: 5,993.51, 6,746.04) to 4,283.61 (95% CI: 4,057.03, 4,524.89) and from 61.81 (95% CI: 56.66, 66.74) to 28.67 (95% CI: 25.92, 31.07) and from 3,472.9 (95% CI: 3,090.71, 3,872.11) to 1,168.8 (95% CI: 1,016.96, 1,336.95). The decline in the aforementioned indicators is greater in the female population than in the male population, and the decrease in China is more pronounced than the global trend. In China, the age-standardized incidence and mortality rates of LRIs showed an annual average percentage change (AAPC) of −2.12 (95% CI: −2.20, −2.03) and −4.77 (95% CI: −5.14, −4.39), respectively. Globally, the age-standardized incidence and mortality rates for LRIs decreased by −1.28 (95% CI: −1.37, −1.18) and −2.47 (95% CI: −2.61, −2.32). By 2036, the incidence of lower respiratory infections (LRI) among men and women in China is projected to decrease by 36.55 and 46.87%, respectively, while the mortality rates are expected to decline to 12.67% for men and increase by 71.85% for women. In comparison, the global decline in LRI incidence is lower than that observed in China, yet the reduction in mortality rates is greater globally than in China.ConclusionsAge-standardized incidence, mortality and disability-adjusted life years (DALYs) decreased more in China than at the global level between 1990 and 2021. Compared with the previous period, the COVID-19 pandemic has led to a significant decrease in the disease burden of LRIs. As the population continues to age, the disease burden of LRIs in the old adult population will become a major new public health challenge.