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.

Data for CDC’s COVID Data Tracker site on Rates of COVID-19 Cases and Deaths by Vaccination Status. Click 'More' for important dataset description and footnotes

Dataset and data visualization details: These data were posted on October 21, 2022, archived on November 18, 2022, and revised on February 22, 2023. These data reflect cases among persons with a positive specimen collection date through September 24, 2022, and deaths among persons with a positive specimen collection date through September 3, 2022.

Vaccination status: A person vaccinated with a primary series had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after verifiably completing the primary series of an FDA-authorized or approved COVID-19 vaccine. An unvaccinated person had SARS-CoV-2 RNA or antigen detected on a respiratory specimen and has not been verified to have received COVID-19 vaccine. Excluded were partially vaccinated people who received at least one FDA-authorized vaccine dose but did not complete a primary series ≥14 days before collection of a specimen where SARS-CoV-2 RNA or antigen was detected. Additional or booster dose: A person vaccinated with a primary series and an additional or booster dose had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after receipt of an additional or booster dose of any COVID-19 vaccine on or after August 13, 2021. For people ages 18 years and older, data are graphed starting the week including September 24, 2021, when a COVID-19 booster dose was first recommended by CDC for adults 65+ years old and people in certain populations and high risk occupational and institutional settings. For people ages 12-17 years, data are graphed starting the week of December 26, 2021, 2 weeks after the first recommendation for a booster dose for adolescents ages 16-17 years. For people ages 5-11 years, data are included starting the week of June 5, 2022, 2 weeks after the first recommendation for a booster dose for children aged 5-11 years. For people ages 50 years and older, data on second booster doses are graphed starting the week including March 29, 2022, when the recommendation was made for second boosters. Vertical lines represent dates when changes occurred in U.S. policy for COVID-19 vaccination (details provided above). Reporting is by primary series vaccine type rather than additional or booster dose vaccine type. The booster dose vaccine type may be different than the primary series vaccine type. ** Because data on the immune status of cases and associated deaths are unavailable, an additional dose in an immunocompromised person cannot be distinguished from a booster dose. This is a relevant consideration because vaccines can be less effective in this group. Deaths: A COVID-19–associated death occurred in a person with a documented COVID-19 diagnosis who died; health department staff reviewed to make a determination using vital records, public health investigation, or other data sources. Rates of COVID-19 deaths by vaccination status are reported based on when the patient was tested for COVID-19, not the date they died. Deaths usually occur up to 30 days after COVID-19 diagnosis. Participating jurisdictions: Currently, these 31 health departments that regularly link their case surveillance to immunization information system data are included in these incidence rate estimates: Alabama, Arizona, Arkansas, California, Colorado, Connecticut, District of Columbia, Florida, Georgia, Idaho, Indiana, Kansas, Kentucky, Louisiana, Massachusetts, Michigan, Minnesota, Nebraska, New Jersey, New Mexico, New York, New York City (New York), North Carolina, Philadelphia (Pennsylvania), Rhode Island, South Dakota, Tennessee, Texas, Utah, Washington, and West Virginia; 30 jurisdictions also report deaths among vaccinated and unvaccinated people. These jurisdictions represent 72% of the total U.S. population and all ten of the Health and Human Services Regions. Data on cases among people who received additional or booster doses were reported from 31 jurisdictions; 30 jurisdictions also reported data on deaths among people who received one or more additional or booster dose; 28 jurisdictions reported cases among people who received two or more additional or booster doses; and 26 jurisdictions reported deaths among people who received two or more additional or booster doses. This list will be updated as more jurisdictions participate. Incidence rate estimates: Weekly age-specific incidence rates by vaccination status were calculated as the number of cases or deaths divided by the number of people vaccinated with a primary series, overall or with/without a booster dose (cumulative) or unvaccinated (obtained by subtracting the cumulative number of people vaccinated with a primary series and partially vaccinated people from the 2019 U.S. intercensal population estimates) and multiplied by 100,000. Overall incidence rates were age-standardized using the 2000 U.S. Census standard population. To estimate population counts for ages 6 months through 1 year, half of the single-year population counts for ages 0 through 1 year were used. All rates are plotted by positive specimen collection date to reflect when incident infections occurred. For the primary series analysis, age-standardized rates include ages 12 years and older from April 4, 2021 through December 4, 2021, ages 5 years and older from December 5, 2021 through July 30, 2022 and ages 6 months and older from July 31, 2022 onwards. For the booster dose analysis, age-standardized rates include ages 18 years and older from September 19, 2021 through December 25, 2021, ages 12 years and older from December 26, 2021, and ages 5 years and older from June 5, 2022 onwards. Small numbers could contribute to less precision when calculating death rates among some groups. Continuity correction: A continuity correction has been applied to the denominators by capping the percent population coverage at 95%. To do this, we assumed that at least 5% of each age group would always be unvaccinated in each jurisdiction. Adding this correction ensures that there is always a reasonable denominator for the unvaccinated population that would prevent incidence and death rates from growing unrealistically large due to potential overestimates of vaccination coverage. Incidence rate ratios (IRRs): IRRs for the past one month were calculated by dividing the average weekly incidence rates among unvaccinated people by that among people vaccinated with a primary series either overall or with a booster dose. Publications: Scobie HM, Johnson AG, Suthar AB, et al. Monitoring Incidence of COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Status — 13 U.S. Jurisdictions, April 4–July 17, 2021. MMWR Morb Mortal Wkly Rep 2021;70:1284–1290. Johnson AG, Amin AB, Ali AR, et al. COVID-19 Incidence and Death Rates Among Unvaccinated and Fully Vaccinated Adults with and Without Booster Doses During Periods of Delta and Omicron Variant Emergence — 25 U.S. Jurisdictions, April 4–December 25, 2021. MMWR Morb Mortal Wkly Rep 2022;71:132–138

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.

As of March 9, 2023, Indonesia registered 160,941 deaths from the coronavirus. This week, Indonesia is experiencing an increase in cases caused by the highly-contagious Omicron variant.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

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 3, 2023, South Korea has confirmed a total of 32,256,154 cases of coronavirus (COVID-19) within the country, including 35,071 deaths. South Korea's handling of the coronavirus (COVID-19) was initially widely praised, though the government's handling of vaccine distribution has been criticized. After the first wave lasted till April, Seoul and the metropolitan areas were hit hard by a few group infections during the second wave in August 2020. This was followed by a fourth wave, driven by the delta variant and low vaccination rates, leading to rising figures. Though the country has since achieved high vaccination rates, the omicron variant led to record new daily cases in 2022.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

As of June 7, 2022, there were 2,523,915 active coronavirus COVID-19 cases and a total of 3,090 deaths registered in Taiwan. Despite the island's proximity to the mainland China, Taiwan had managed to contain the virus before the outbreak of the Delta variant of COVID-19. The success was due to an effective disease control system developed from the experience in the SARS epidemic. The highly contagious Omicron variant had brought a spike in new infections in Taiwan since March 2022.

Estimated counterfactual averted deaths with 80% prediction interval (PI) for each scenario, as described in Part C of the S1 File.

Peru is the country with the highest mortality rate due to the coronavirus disease (COVID-19) in Latin America. As of November 13, 2023, the country registered over 672 deaths per 100,000 inhabitants. It was followed by Brazil, with around 331.5 fatal cases per 100,000 population. In total, over 1.76 million people have died due to COVID-19 in Latin America and the Caribbean.

Are these figures accurate? Although countries like Brazil already rank among the countries most affected by the coronavirus disease (COVID-19), there is still room to believe that the number of cases and deaths in Latin American countries are underreported. The main reason is the relatively low number of tests performed in the region. For example, Brazil, one of the most impacted countries in the world, has performed approximately 63.7 million tests as of December 22, 2022. This compared with over one billion tests performed in the United States, approximately 909 million tests completed in India, or around 522 million tests carried out in the United Kingdom.

Capacity to deal with the outbreak With the spread of the Omicron variant, the COVID-19 pandemic is putting health systems around the world under serious pressure. The lack of equipment to treat acute cases, for instance, is one of the problems affecting Latin American countries. In 2019, the number of ventilators in hospitals in the most affected countries ranged from 25.23 per 100,000 inhabitants in Brazil to 5.12 per 100,000 people in Peru.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Objectives

This study aimed to evaluate the clinical efficacy of Paxlovid in patients hospitalized with severe/critical COVID-19.

Methods

Data were acquired from patients with severe/critical COVID-19 diagnosed between December 2022 and January 2023 at a medical center in China. Patients were divided into the Paxlovid treatment group and the conventional treatment group. The association between Paxlovid and all-cause mortality of patients during hospitalization was evaluated using the COX regression model and inverse probability weighting method, respectively. The secondary endpoint was the improvement in patients' lung imaging findings 1 week later. The odds ratio (OR) was estimated using logistic regression.

Results

A total of 158 eligible patients were enrolled, including 50 in-hospital deaths (50/98) in the Paxlovid group and 28 (28/60) in the conventional treatment group. The corrected hazard ratio for death was 0.51 (95% CI: 0.28–0.94, p = 0.031) and the inverse probability-weighted hazard ratio was 0.42 (95% CI: 0.24–0.75). The secondary endpoint analysis revealed that Paxlovid was associated with improved lung imaging findings 1 week later (adjusted OR: 0.35, 95% CI: 0.16–0.77).

Conclusion

Treatment with Paxlovid is associated with a significantly reduced risk of death and improved lung imaging findings in patients with severe/critical COVID-19.

Same as Table 1 for DTOT the total number of COVID-19 deaths per thousand inhabitants since the beginning of the pandemic.

To describe the demographic/clinical characteristics, treatment patterns, and mortality among patients hospitalized with COVID-19 during Omicron predominance by immunocompromised and high-risk status. Retrospective observational study of patients hospitalized with COVID-19 between January 1, 2022 and November 30, 2022, using data from the Optum de-identified Clinformatics Data Mart Database. Patient demographic/clinical characteristics, treatments, mortality and costs, were assessed, during the emergence of BA.1 BA.4, BA.5, BA.2.12.1, BA.2.75, BQ.1, XBB Omicron viral subvariants. Overall, 43,123 patients were included, with a mean (standard deviation [SD]) age of 75.5 (12.4) years, 51.8% were female. Immunocompromised patients accounted for 36% of hospitalized patients while only 5.8% received any outpatient COVID-19 treatment within 30 days of hospital admission. The mean (SD) hospital length of stay was 7.9 (7.5) days with 15.5% mortality within 30 days of admission. Mean (SD) hospital costs were $33,975 ($26,392), and 30-day all-cause readmission was 15.1%. Patients with immunocompromised status and those with a higher number of high-risk conditions proceeded to have an elevated proportion of hospital readmissions and mortality within 30 days. Moreover, a higher proportion of mortality was observed during the BA.1 period (20.1%) relative to other variant periods (11.0%). COVID-19 imposed a large healthcare burden, particularly among immunocompromised patients and those with underlying high-risk conditions during Omicron period. Low utilization of outpatient COVID-19 treatments was observed in these high-risk populations eligible for treatment. Continued surveillance and research regarding COVID-19 variants and the impact of outpatient treatment options on high-risk patients is crucial to inform and guide public health action.

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.

BackgroundIn Hungary, the pandemic waves in late 2021 and early 2022 were dominated by the Delta and Omicron SARS-CoV-2 variants, respectively. Booster vaccines were offered with one or two doses for the vulnerable population during these periods.Methods and FindingsThe nationwide HUN-VE 2 study examined the effectiveness of primary immunization, single booster, and double booster vaccination in the prevention of Covid-19 related mortality during the Delta and Omicron waves, compared to an unvaccinated control population without prior SARS-CoV-2 infection during the same study periods. The risk of Covid-19 related death was 55% lower during the Omicron vs. Delta wave in the whole study population (n=9,569,648 and n=9,581,927, respectively; rate ratio [RR]: 0.45, 95% confidence interval [CI]: 0.44–0.48). During the Delta wave, the risk of Covid-19 related death was 74% lower in the primary immunized population (RR: 0.26; 95% CI: 0.25–0.28) and 96% lower in the booster immunized population (RR: 0.04; 95% CI: 0.04–0.05), vs. the unvaccinated control group. During the Omicron wave, the risk of Covid-19 related death was 40% lower in the primary immunized population (RR: 0.60; 95% CI: 0.55–0.65) and 82% lower in the booster immunized population (RR: 0.18; 95% CI: 0.16–0.2) vs. the unvaccinated control group. The double booster immunized population had a 93% lower risk of Covid-19 related death compared to those with only one booster dose (RR: 0.07; 95% CI. 0.01–0.46). The benefit of the second booster was slightly more pronounced in older age groups.ConclusionsThe HUN-VE 2 study demonstrated the significantly lower risk of Covid-19 related mortality associated with the Omicron vs. Delta variant and confirmed the benefit of single and double booster vaccination against Covid-19 related death. Furthermore, the results showed the additional benefit of a second booster dose in terms of SARS-CoV-2 infection and Covid-19 related mortality.

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: U.S. Centers for Disease Control and Prevention (CDC)For more information, visit the Johns Hopkins Coronavirus Resource Center.Trends represent the day-to-day rate of new cases with a focus on the most recent 10 to 14 days. Includes Puerto Rico, Guam, Northern Marianas, and U.S. Virgin Islands. Daily new case counts are volatile for many reasons and sometimes the trends reflect that volatility. Thus, we decided to include longer-term summaries here. County Trends as of 9 Mar 20230 (-0) in Emergent1135 (+51) in Spreading1664 (-63) in Epidemic230 (+10) in Controlled110 (+2) in End StageNotes: Many states now only report once per week, and FL only once every two weeks. On 3/7/2022 we adjusted the formula for active cases to reflect the Omicron Variant which is documented to cause lower rates of serious and severe illness. To produce these trends we analyze daily updates 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.For more information about COVID-19 trends, see our country level trends story map and the full methodology.Data Source: Johns Hopkins University CSSE US Cases by County dashboard and USAFacts for Utah County level Data.Feature layer generated from running the Join Features solution that is the basis for daily updates for the U.S. County COVID-19 Tends Story Map.

Background Rural U.S. communities are at risk from COVID-19 due to advanced age and limited access to acute care. Recognizing this, the Vashon Medical Reserve Corps (VMRC) in King County, Washington, implemented an all-volunteer, community-based COVID-19 response program. This program integrated public engagement, SARS-CoV-2 testing, contact tracing, vaccination, and material community support, and was associated with the lowest cumulative COVID-19 case rate in King County. This study aimed to investigate the contributions of demographics, geography and public health interventions to Vashon’s low COVID-19 rates. Methods This observational cross-sectional study compares cumulative COVID-19 rates and success of public health interventions from February 2020 through November 2021 for Vashon Island with King County (including metropolitan Seattle) and Whidbey Island, located ~50 km north of Vashon. To evaluate the role of demography, we developed multiple linear regression models of COVID-19 rates using metrics of age, race/ethnicity, wealth and educational attainment across 77 King County zip codes. To investigate the role of remote geography we expanded the regression models to include North, Central and South Whidbey, similarly remote island communities with varying demographic features. To evaluate the effectiveness of VMRC’s community-based public health measures, we directly compared Vashon’s success of vaccination and contact tracing with that of King County and South Whidbey, the Whidbey community most similar to Vashon. Results Vashon’s cumulative COVID-19 case rate was 29% that of King County overall (22.2 vs 76.8 cases/K). A multiple linear regression model based on King County demographics found educational attainment to be a major correlate of COVID-19 rates, and Vashon’s cumulative case rate was just 38% of predicted (p<.05), so demographics alone do not explain Vashon’s low COVID-19 case rate. Inclusion of Whidbey communities in the model identified a major effect of remote geography (-49 cases/K, p<.001), such that observed COVID-19 rates for all remote communities fell within the model’s 95% prediction interval. VMRC’s vaccination effort was highly effective, reaching a vaccination rate of 1500 doses/K four months before South Whidbey and King County and maintaining a cumulative vaccination rate 200 doses/K higher throughout the latter half of 2021 (p<.001). Including vaccination rates in the model reduced the effect of remote geography to -41 cases/K (p<.001). VMRC case investigation was also highly effective, interviewing 96% of referred cases in an average of 1.7 days compared with 69% in 3.7 days for Washington Department of Health investigating South Whidbey cases and 80% in 3.4 days for Public Health–Seattle & King County (both p<0.001). VMRC’s public health interventions were associated with a 30% lower case rate (p<0.001) and 55% lower hospitalization rate (p=0.056) than South Whidbey. Conclusion While the overall magnitude of the pre-Omicron COVID-19 pandemic in rural and urban U.S. communities was similar, we show that island communities in the Puget Sound region were substantially protected from COVID-19 by their geography. We further show that a volunteer community-based COVID-19 response program was highly effective in the Vashon community, augmenting the protective effect of geography. We suggest that Medical Reserve Corps should be an important element of future pandemic planning. Methods The study period extended from the pandemic onset in February 2020 through November 2021. Daily COVID-19 cases, hospitalizations, deaths and test numbers for King County as a whole and by zip code were downloaded from the King County COVID-19 dashboard (Feb 22, 2022 update). Population data for King County and Vashon are from the April 2020 US Census. Zip code level population data are the average of two zip code tabulation area estimates from the WA Office of Financial Management and Cubit (a commercial data vendor providing access to US Census information). The Asset Limited, Income Constrained, and Employed (ALICE) metric, a measure of the working poor, was obtained from United Way.

Omicron-1 COVID-19 is less invasive in the general population than previous viral variants. However, clinical course and outcome of hospitalised patients with SARS-CoV-2 pneumonia during the shift of the predominance from Delta to Omicron variants are not fully explored. During January 2022 consecutively hospitalised patients with SARS-CoV-2 pneumonia were analysed. SARS-CoV-2 variants were identified by a 2-step pre-screening protocol and randomly confirmed by whole genome sequencing analysis. Clinical, laboratory and treatment data split by type of variant were analysed along with logistic regression of factors associated to mortality. 150 patients [mean age (SD) 67.2(15.8) years, male 54%] were analysed. Compared to Delta (n = 46), Omicron-1 patients (n = 104) were older [mean age (SD): 69.5(15.4) vs 61.9(15.8) years, p = 0.007], with more comorbidities (89.4% vs 65.2%, p = 0.001), less obesity (BMI >30Kg/m2 in 24% vs 43.5%, p = 0.034) but higher vaccination rates for COVID-19 (52.9% vs 8.7%, p 

Introduction: This study aimed to evaluate the characteristics and prognostic factors for coronavirus disease 2019 (COVID-19) patients on maintenance hemodialysis (HD). Methods: All admitted HD patients who were infected with severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) from December 1, 2022 to January 31, 2023 were included. Patients with pneumonia were further classified into the mild, moderate, severe and critical illness. Clinical symptoms, laboratory results, radiologic findings, treatment and clinical outcomes were collected. Independent risk factors for progression to critical disease, and in-hospital mortality were determined by the multivariate regression analysis. The receiver operating characteristic (ROC) analysis with the area under the curve (AUC) was used to evaluate the predictive performance of developing critical status and in-hospital mortality. Results: A total of 182 COVID-19 patients with HD were included, with an average age of the 61.55 years. Out of the total, 84 (46.1%) patients did not have pneumonia and 98 (53.8%) patients had pneumonia. Among patients with pneumonia, 48 (49.0%) had moderate illness, 26 (26.5%) severe illness, and 24 (24.5%) critical illness, respectively. Elder age [HR (95% CI): 1.07 (1.01-1.13), P<0.01], increased levels of lactate dehydrogenase (LDH) [1.01 (1.003-1.01), P<0.01] and C-reactive protein (CRP) [1.01 (1.00-1.01), P=0.04] were risk factors for developing critical illness. Elder age [1.11 (1.03-1.19), P=0.01], increased procalcitonin (PCT) [1.07 (1.02-1.12), P=0.01], and LDH level [1.004 (1-1.01), P=0.03] were factors associated with increased risk of in-hospital mortality. Conclusions: Age, CRP, PCT and LDH can be used to predict negative clinical outcomes for HD patients with COVID-19 pneumonia.

Background Infections with B.1.1.529 (Omicron) variants of SARS-CoV-2 became predominant worldwide since late 2021, replacing the previously dominant B.1.617.2 variant (Delta). While those variants are highly transmissible and can evade vaccine protection, population studies suggested that outcomes from infection with Omicron variants are better compared with Delta. Data regarding prognosis of maintenance hemodialysis (MHD) patients infected with Omicron vs. Delta variants, however, is scarce. Methods This retrospective cohort study includes all patients with end-stage kidney disease treated with MHD in Meir Medical Center, Kfar-Saba, Israel that were diagnosed with SARS-CoV-2 infection between June 2021 and May 2022. Results Twenty-six subjects were diagnosed with the Delta variant and 71 with Omicron. Despite comparable age between groups and higher mean vaccine doses prior to the infection among Omicron group (p<0.001), SARS-CoV-2 infection severity was significantly worse among MHD infected with the Delta variant: 50% developed severe or critical COVID-19 vs. 5% in the Omicron group (p<0.001). Over half of MHD infected with Omicron (57%) were asymptomatic during their illness. 30-day mortality rate for the whole cohort was 5.2%. It was significantly higher among MHD in the Delta group than in the Omicron group (5/26, 19.2% vs. 0/71, p<0.001), as was 90-day mortality rate (5/26, 19.2% vs. 3/71, 4.2%, p=0.02). Conclusions Infection with the SARS-CoV-2 Delta variant was associated with worse outcomes compared with Omicron, among subjects on MHD. However, despite mild disease among vaccinated MHD patients, infection with Omicron variant was still associated with significant 90-day mortality rate.