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.

COVID-19 rate of death, or the known deaths divided by confirmed cases, was over ten percent in Yemen, the only country that has 1,000 or more cases. This according to a calculation that combines coronavirus stats on both deaths and registered cases for 221 different countries. Note that death rates are not the same as the chance of dying from an infection or the number of deaths based on an at-risk population. By April 26, 2022, the virus had infected over 510.2 million people worldwide, and led to a loss of 6.2 million. 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.

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. Note that Statista aims to also provide domestic source material for a more complete picture, and not to just look at one particular source. Examples are these statistics on the confirmed coronavirus cases in Russia or the COVID-19 cases in Italy, both of which are from domestic sources. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

A word on the flaws of numbers like this

People are right to ask whether these numbers are at all representative or not for several reasons. First, countries worldwide decide differently on who gets tested for the virus, meaning that comparing case numbers or death rates could to some extent be misleading. Germany, for example, started testing relatively early once the country’s first case was confirmed in Bavaria in January 2020, whereas Italy tests for the coronavirus postmortem. Second, not all people go to see (or can see, due to testing capacity) a doctor when they have mild symptoms. Countries like Norway and the Netherlands, for example, recommend people with non-severe symptoms to just stay at home. This means not all cases are known all the time, which could significantly alter the death rate as it is presented here. Third and finally, numbers like this change very frequently depending on how the pandemic spreads or the national healthcare capacity. It is therefore recommended to look at other (freely accessible) content that dives more into specifics, such as the coronavirus testing capacity in India or the number of hospital beds in the UK. Only with additional pieces of information can you get the full picture, something that this statistic in its current state simply cannot provide.

Daily updates of Covid-19 Global Excess Deaths from the Economist's GitHub repository: https://github.com/TheEconomist/covid-19-the-economist-global-excess-deaths-model

Interpreting estimates

Estimating excess deaths for every country every day since the pandemic began is a complex and difficult task. Rather than being overly confident in a single number, limited data means that we can often only give a very very wide range of plausible values. Focusing on central estimates in such cases would be misleading: unless ranges are very narrow, the 95% range should be reported when possible. The ranges assume that the conditions for bootstrap confidence intervals are met. Please see our tracker page and methodology for more information.

New variants

The Omicron variant, first detected in southern Africa in November 2021, appears to have characteristics that are different to earlier versions of sars-cov-2. Where this variant is now dominant, this change makes estimates uncertain beyond the ranges indicated. Other new variants may do the same. As more data is incorporated from places where new variants are dominant, predictions improve.

Non-reporting countries

Turkmenistan and the Democratic People's Republic of Korea have not reported any covid-19 figures since the start of the pandemic. They also have not published all-cause mortality data. Exports of estimates for the Democratic People's Republic of Korea have been temporarily disabled as it now issues contradictory data: reporting a significant outbreak through its state media, but zero confirmed covid-19 cases/deaths to the WHO.

Acknowledgements

A special thanks to all our sources and to those who have made the data to create these estimates available. We list all our sources in our methodology. Within script 1, the source for each variable is also given as the data is loaded, with the exception of our sources for excess deaths data, which we detail in on our free-to-read excess deaths tracker as well as on GitHub. The gradient booster implementation used to fit the models is aGTBoost, detailed here.

Calculating excess deaths for the entire world over multiple years is both complex and imprecise. We welcome any suggestions on how to improve the model, be it data, algorithm, or logic. If you have one, please open an issue.

The Economist would also like to acknowledge the many people who have helped us refine the model so far, be it through discussions, facilitating data access, or offering coding assistance. A special thanks to Ariel Karlinsky, Philip Schellekens, Oliver Watson, Lukas Appelhans, Berent Å. S. Lunde, Gideon Wakefield, Johannes Hunger, Carol D'Souza, Yun Wei, Mehran Hosseini, Samantha Dolan, Mollie Van Gordon, Rahul Arora, Austin Teda Atmaja, Dirk Eddelbuettel and Tom Wenseleers.

All coding and data collection to construct these models (and make them update dynamically) was done by Sondre Ulvund Solstad. Should you have any questions about them after reading the methodology, please open an issue or contact him at sondresolstad@economist.com.

Suggested citation The Economist and Solstad, S. (corresponding author), 2021. The pandemic’s true death toll. [online] The Economist. Available at: https://www.economist.com/graphic-detail/coronavirus-excess-deaths-estimates [Accessed ---]. First published in the article "Counting the dead", The Economist, issue 20, 2021.

The first case of COVID-19 in Mexico was detected on March 1, 2020. By the end of the year, the total number of confirmed infections had surpassed 1.4 million. Meanwhile, the number of deaths related to the disease was nearing 148,000. On May 11, 2025, the number of cases recorded had reached 7.6 million, while the number of deaths amounted to around 335,000. The relevance of the Omicron variant Omicron, a highly contagious COVID-19 variant, was declared of concern by the World Health Organization (WHO) at the end of November 2021. As the pandemic unfolded, it became the variant with the highest share of COVID-19 cases in the world. In Latin America, countries such as Colombia, Argentina, Brazil, and Mexico were strongly affected. In fact, by 2023 nearly all analyzed sequences within these countries corresponded to an Omicron subvariant. Beyond a health crisis As the COVID-19 pandemic progressed worldwide, the respiratory disease caused by the virus SARS-CoV-2 virus first detected in Wuhan brought considerable economic consequences for countries and households. While Mexico’s gross domestic product (GDP) in current prices declined in 2020 compared to the previous year, a survey conducted among adults during the first months of 2021 showed COVID-19 impacted families mainly through finances and employment, with around one third of households in Mexico reporting an income reduction and the same proportion having at least one household member suffering from the disease.Find the most up-to-date information about the coronavirus pandemic in the world under Statista’s COVID-19 facts and figures site.

BackgroundThe Omicron variant of SARS-CoV-2 is more highly infectious and transmissible than prior variants of concern. It was unclear which factors might have contributed to the alteration of COVID-19 cases and deaths during the Delta and Omicron variant periods. This study aimed to compare the COVID-19 average weekly infection fatality rate (AWIFR), investigate factors associated with COVID-19 AWIFR, and explore the factors linked to the increase in COVID-19 AWIFR between two periods of Delta and Omicron variants.Materials and methodsAn ecological study has been conducted among 110 countries over the first 12 weeks during two periods of Delta and Omicron variant dominance using open publicly available datasets. Our analysis included 102 countries in the Delta period and 107 countries in the Omicron period. Linear mixed-effects models and linear regression models were used to explore factors associated with the variation of AWIFR over Delta and Omicron periods.FindingsDuring the Delta period, the lower AWIFR was witnessed in countries with better government effectiveness index [β = −0.762, 95% CI (−1.238)–(−0.287)] and higher proportion of the people fully vaccinated [β = −0.385, 95% CI (−0.629)–(−0.141)]. In contrast, a higher burden of cardiovascular diseases was positively associated with AWIFR (β = 0.517, 95% CI 0.102–0.932). Over the Omicron period, while years lived with disability (YLD) caused by metabolism disorders (β = 0.843, 95% CI 0.486–1.2), the proportion of the population aged older than 65 years (β = 0.737, 95% CI 0.237–1.238) was positively associated with poorer AWIFR, and the high proportion of the population vaccinated with a booster dose [β = −0.321, 95% CI (−0.624)–(−0.018)] was linked with the better outcome. Over two periods of Delta and Omicron, the increase in government effectiveness index was associated with a decrease in AWIFR [β = −0.438, 95% CI (−0.750)–(−0.126)]; whereas, higher death rates caused by diabetes and kidney (β = 0.472, 95% CI 0.089–0.855) and percentage of population aged older than 65 years (β = 0.407, 95% CI 0.013–0.802) were associated with a significant increase in AWIFR.ConclusionThe COVID-19 infection fatality rates were strongly linked with the coverage of vaccination rate, effectiveness of government, and health burden related to chronic diseases. Therefore, proper policies for the improvement of vaccination coverage and support of vulnerable groups could substantially mitigate the burden of COVID-19.

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

As of July 2023, the Omicron variant was the most prevalent among selected countries in Latin America. The share of COVID-19 cases corresponding to the Omicron variant amounted to 100 percent of the analyzed sequences of SARS-CoV-2 in Colombia. The variant Omicron (XBB.1.5) accounted for nearly 81 percent of the sequenced cases in the country, while Omicron (XBB.1.9) added up to 14 percent. Similarly, Peru reported over 90 percent of its reviewed sequences corresponding to the variant Omicron (XBB.1.5), while Omicron (XBB) accounted for around 2.4 percent of cases studied. A regional overview The Omicron variant of SARS-CoV-2 - the virus causing COVID-19 - was designated as a variant of concern by the World Health Organization in November 2021. Since then, it has been rapidly spreading, causing an unprecedented increase in the number of cases reported worldwide. In Latin America, Brazil had been the most affected country by the disease already before the emergence of the Omicron variant, with nearly 37.4 million cases and around 701,494 confirmed deaths as of May 2, 2023. However, it is Peru that has the largest mortality rate per 100,000 inhabitants due to the SARS-Cov-2 in the region, with roughly 672 deaths per 100,000 people. Vaccination campaigns in Latin America As the COVID-19 pandemic continues to cause social and economic harm worldwide, most Latin American and Caribbean countries advance their immunization programs. As of August 14, 2023, Brazil had administered the largest number of vaccines in the region, with over 486.4 million doses. Mexico and Argentina followed, with about 223.1 million and 116 million COVID-19 doses administered, respectively. However, Cuba had the highest vaccination rate not only in the region, but also the world, with around 391 vaccines given per 100 people.Find the most up-to-date information about the coronavirus pandemic in the world under Statista’s COVID-19 facts and figures site.

Mexico, one of the countries severely affected by COVID-19, accumulated more than 5. 1 all-cause excess deaths/1,000 inhabitants and 2.5 COVID-19 confirmed deaths/1,000 inhabitants, in 2 years. In this scenario of high SARS-CoV-2 circulation, we analyzed the effectiveness of the country's vaccination strategy that used 7 different vaccines from around the world, and focused on vaccinating the oldest population first. We analyzed the national dataset published by Mexican health authorities, as a retrospective cohort, separating cases, hospitalizations, deaths and excess deaths by wave and age group. We explored if the vaccination strategy was effective to limit severe COVID-19 during the active outbreaks caused by Delta and Omicron variants. Vaccination of the eldest third of the population reduced COVID-19 hospitalizations, deaths and excess deaths by 46–55% in the third wave driven by Delta SARS-CoV-2. These adverse outcomes dropped 74–85% by the fourth wave driven by Omicron, when all adults had access to vaccines. Vaccine access for the pregnant resulted in 85–90% decrease in COVID-19 fatalities in pregnant individuals and 80% decrease in infants 0 years old by the Omicron wave. In contrast, in the rest of the pediatric population that did not access vaccination before the period analyzed, COVID-19 hospitalizations increased >40% during the Delta and Omicron waves. Our analysis suggests that the vaccination strategy in Mexico has been successful to limit population mortality and decrease severe COVID-19, but children in Mexico still need access to SARS-CoV-2 vaccines to limit severe COVID-19, in particular those 1–4 years old.

Content

The WHO coronavirus (COVID-19) dashboard presents official daily counts of COVID-19 cases, deaths and vaccine utilization reported by countries, territories and areas. Through this dashboard, we aim to provide a frequently updated data visualization, data dissemination and data exploration resource, while linking users to other useful and informative resources.

Caution must be taken when interpreting all data presented, and differences between information products published by WHO, national public health authorities, and other sources using different inclusion criteria and different data cut-off times are to be expected. While steps are taken to ensure accuracy and reliability, all data are subject to continuous verification and change. All counts are subject to variations in case detection, definitions, laboratory testing, vaccination strategy, and reporting strategies.

Acknowledgements

© World Health Organization 2020, All rights reserved.

WHO supports open access to the published output of its activities as a fundamental part of its mission and a public benefit to be encouraged wherever possible. Permission from WHO is not required for the use of the WHO coronavirus disease (COVID-19) dashboard material or data available for download. It is important to note that:

WHO publications cannot be used to promote or endorse products, services or any specific organization.

WHO logo cannot be used without written authorization from WHO.

WHO provides no warranty of any kind, either expressed or implied. In no event shall WHO be liable for damages arising from the use of WHO publications.

For further information, please visit WHO Copyright, Licencing and Permissions.

Citation: WHO COVID-19 Dashboard. Geneva: World Health Organization, 2020. Available online: https://covid19.who.int/

Inspiration

Daily cases start increasing suddenly just before the new year and there's a fear for the upcoming wave. Everybody starts to predict the peak cases in the 3rd wave and the date the peak will be reached. Assume you are in the 1st week of January 2022 and there's panic in the country, for the Omicron variant is said to be highly transmittable. Using your machine learning and deep learning skills, you have to create a model that predicts accurately the peak for the 3rd wave.

As of January 2022, the share of COVID-19 cases corresponding to the Omicron variant in Mexico amounted to over 90 percent of the country's analyzed sequences of the SARS-CoV-2 virus. A month earlier, this figure amounted to 60 percent of cases studied in the country. The Omicron variant of SARS-CoV-2 - the virus causing COVID-19 - was designated as a variant of concern by the World Health Organization in November 2021 based on its trasmisibility level.

An increasing amount of cases

In Mexico, the spread of the Omicron variant led the Latin American country to reach over 5.6 million confirmed cases of COVID-19 by March 2022, with the surge of close to two million cases in a matter of four months. Never before since the start of the pandemic had there been so many cases recorded in such a short period of time in the country. During those months, approximately 30 thousand people died due to complications stemming from the disease, reaching 320 thousand deaths by March 2022.

A relatively low testing rate

Within the Latin American region, Mexico was the fourth country with the largest number of people infected, following Brazil, Argentina, and Colombia. However, the country is considered to have had a relatively low testing rate. According to recent estimates, around 117 thousand tests per million people were reported in Mexico as of March 2022, one of the lowest COVID-19 testing rates among the countries most affected by the pandemic. In contrast, Peru reached over 836 million tests per million population.

Find the most up-to-date information about the coronavirus pandemic in the world under Statista’s COVID-19 facts and figures site.

The coronavirus (COVID-19) pandemic has spread swiftly from the Chinese city Wuhan across the world. In Hong Kong, the number of active cases amounted to 260,919 with 9,389 deaths as of June 7, 2022. The financial hub was one of the places which were able to flatten the pandemic curve for a long time before the Omicron variant. To boost the low inoculation rate, Hong Kong government has widened the COVID-19 vaccine access to all residents aged 16 and older.

IntroductionIn November 2021, the SARS-CoV-2 Omicron variant of concern has emerged and is currently dominating the COVID-19 pandemic over the world. Omicron displays a number of mutations, particularly in the spike protein, leading to specific characteristics including a higher potential for transmission. Although Omicron has caused a significant number of deaths worldwide, it generally induces less severe clinical signs compared to earlier variants. As its impact on blood platelets remains unknown, we investigated platelet behavior in severe patients infected with Omicron in comparison to Delta.MethodsClinical and biological characteristics of severe COVID-19 patients infected with the Omicron (n=9) or Delta (n=11) variants were analyzed. Using complementary methods such as flow cytometry, confocal imaging and electron microscopy, we examined platelet activation, responsiveness and phenotype, presence of virus in platelets and induction of selective autophagy. We also explored the direct effect of spike proteins from the Omicron or Delta variants on healthy platelet signaling.ResultsSevere Omicron variant infection resulted in platelet activation and partial desensitization, presence of the virus in platelets and selective autophagy response. The intraplatelet processing of Omicron viral cargo was different from Delta as evidenced by the distribution of spike protein-positive structures near the plasma membrane and the colocalization of spike and Rab7. Moreover, spike proteins from the Omicron or Delta variants alone activated signaling pathways in healthy platelets including phosphorylation of AKT, p38MAPK, LIMK and SPL76 with different kinetics.DiscussionAlthough SARS-CoV-2 Omicron has different biological characteristics compared to prior variants, it leads to platelet activation and desensitization as previously observed with the Delta variant. Omicron is also found in platelets from severe patients where it induces selective autophagy, but the mechanisms of intraplatelet processing of Omicron cargo, as part of the innate response, differs from Delta, suggesting that mutations on spike protein modify virus to platelet interactions.

Characteristics of patients hospitalized with COVID-19 infection before and after receipt of T/C.

As of August 28, 2023, South Korea has confirmed a total of 34,436,586 positive cases of coronavirus (COVID-19), including 35,812 deaths. The first case coronavirus in South Korea was discovered in January 2020. Currently, 25.57 cases per 100,000 people are being confirmed, down from 35.74 cases last month.

Case development trend

In the middle of February 2020, novel coronavirus (COVID-19) began to increase exponentially from patient 31, who was known as a super propagator. With a quick response by the government, the daily new cases once dropped to a single-digit. In May 2020, around three hundreds of new infections were related to cluster infections that occurred in some clubs at Itaewon, an entertainment district in Seoul. Seoul and the metropolitan areas were hit hard by this Itaewon infection. Following the second wave of infections in August, the government announced it was facing the third wave in November with 200 to 300 confirmed cases every day. A fourth wave started in July 2021 from the spread of the delta variant and low vaccination rates. While vaccination rates have risen significantly since then, the highly infectious omicron variant led to a record-breaking rise in cases. This began easing up in March of 2022, though numbers began to rise again around August of 2022. As of October 2022, case numbers are decreasing again.

Economic impact on Korean economy

The Korean economy is interdependent on many countries over the world, so the impact of coronavirus on Korean economy is significant. According to recent OECD forecasts, South Korea's GDP is projected to show positive growth in 2022 and 2023. The first sector the coronavirus impacted was tourism, caused by decreasing numbers of inbound tourists and domestic sales. In the first quarter of 2020, tourism revenue was expected to decrease by 2.9 trillion won. In addition, Korean companies predicted that the damage caused by the losses in sales and exports would be significant. In particular, the South Korean automotive industry was considered to be the most affected industry, as automobile production and parts supply stopped at factories in China.For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Dataset featuring top 10 countries with the number of randomly extracted genome isolates of Omicron variant.

IntroductionOne of the unexpected outcomes of the COVID-19 pandemic was the relatively low levels of morbidity and mortality in Africa compared to the rest of the world. Nigeria, Africa's most populous nation, accounted for less than 0.01% of the global COVID-19 fatalities. The factors responsible for Nigeria's relatively low loss of life due to COVID-19 are unknown. Also, the correlates of protective immunity to SARS-CoV-2 and the impact of pre-existing immunity on the outcome of the COVID-19 pandemic in Africa are yet to be elucidated. Here, we evaluated the natural and vaccine-induced immune responses from vaccinated, non-vaccinated and convalescent individuals in Southern Nigeria throughout the three waves of the COVID-19 pandemic in Nigeria. We also examined the pre-existing immune responses to SARS-CoV-2 from samples collected prior to the COVID-19 pandemic.MethodsWe used spike RBD and N- IgG antibody ELISA to measure binding antibody responses, SARS-CoV-2 pseudotype assay protocol expressing the spike protein of different variants (D614G, Delta, Beta, Omicron BA1) to measure neutralizing antibody responses and nucleoprotein (N) and spike (S1, S2) direct ex vivo interferon gamma (IFNγ) T cell ELISpot to measure T cell responses. ResultOur study demonstrated a similar magnitude of both binding (N-IgG (74% and 62%), S-RBD IgG (70% and 53%) and neutralizing (D614G (49% and 29%), Delta (56% and 47%), Beta (48% and 24%), Omicron BA1 (41% and 21%)) antibody responses from symptomatic and asymptomatic survivors in Nigeria. A similar magnitude was also seen among vaccinated participants. Interestingly, we revealed the presence of preexisting binding antibodies (N-IgG (60%) and S-RBD IgG (44%)) but no neutralizing antibodies from samples collected prior to the pandemic. DiscussionThese findings revealed that both vaccinated, non-vaccinated and convalescent individuals in Southern Nigeria make similar magnitude of both binding and cross-reactive neutralizing antibody responses. It supported the presence of preexisting binding antibody responses among some Nigerians prior to the COVID-19 pandemic. Lastly, hybrid immunity and heterologous vaccine boosting induced the strongest binding and broadly neutralizing antibody responses compared to vaccine or infection-acquired immunity alone.

This file provides the total number of cases and deaths, on 10 January 2022, for 150 countries in the world, as provided by the Center for System Science and Engineering of the John Hopkins University [32]. It also includes the number of hospital beds per inhabitants and the number of people of 60-year old and older from the United Nations [30] and the World Bank [31], respectively. (XLS)

The COVID-19 pandemic has had varying impacts across different regions, necessitating localised data-driven responses. SARS-CoV-2 was first identified in a person in Wuhan, China, in December 2019 and spread globally within three months. While there were similarities in the pandemic’s impact across regions, key differences motivated systematic quantitative analysis of diverse geographical data to inform responses. Malawi reported its first COVID-19 case on 2 April 2020 but had significantly less data than Global North countries to inform its response. Here, we present a modelling analysis of SARS-CoV-2 epidemiology and phylogenetics in Malawi between 2 April 2020 and 19 October 2022. We carried out this analysis using open-source tools and open data on confirmed cases, deaths, geography, demographics, and viral genomics. R was used for data visualisation, while Generalised Additive Models (GAMs) estimated incidence trends, growth rates, and doubling times. Phylogenetic analysis was conducted using IQ-TREE, TreeTime, and interactive tree of life. This analysis identifies five major COVID-19 waves in Malawi, driven by different lineages: (1) Early variants, (2) Beta, (3) Delta, (4) Omicron BA.1, and (5) Other Omicron. While the Alpha variant was present, it did not cause a major wave, likely due to competition from the more infectious Delta variant, since Alpha circulated in Malawi when Beta was phasing out and Delta emerging. Case Fatality Ratios were higher for Delta, and lower for Omicron, than for earlier lineages. Phylogeny reveals separation of the tree into major lineages as would be expected, and early emergence of Omicron, as is consistent with proximity to the likely origin of this variant. Both variant prevalence and overall rates of confirmed cases and confirmed deaths were highly geographically heterogeneous. We suggest that real-time analyses should be considered in Malawi and other countries, where similar computational and data resources are available.