The outbreak of the novel coronavirus in Wuhan, China, saw infection cases spread throughout the Asia-Pacific region. By April 13, 2024, India had faced over 45 million coronavirus cases. South Korea followed behind India as having had the second highest number of coronavirus cases in the Asia-Pacific region, with about 34.6 million cases. At the same time, Japan had almost 34 million cases. At the beginning of the outbreak, people in South Korea had been optimistic and predicted that the number of cases would start to stabilize. What is SARS CoV 2?Novel coronavirus, officially known as SARS CoV 2, is a disease which causes respiratory problems which can lead to difficulty breathing and pneumonia. The illness is similar to that of SARS which spread throughout China in 2003. After the outbreak of the coronavirus, various businesses and shops closed to prevent further spread of the disease. Impacts from flight cancellations and travel plans were felt across the Asia-Pacific region. Many people expressed feelings of anxiety as to how the virus would progress. Impact throughout Asia-PacificThe Coronavirus and its variants have affected the Asia-Pacific region in various ways. Out of all Asia-Pacific countries, India was highly affected by the pandemic and experienced more than 50 thousand deaths. However, the country also saw the highest number of recoveries within the APAC region, followed by South Korea and Japan.

Background: The outbreak of novel coronavirus disease 2019 (COVID-19) started in the city of Wuhan, China, with a period of rapid initial spread. Transmission on a regional and then national scale was promoted by intense travel during the holiday period of the Chinese New Year. We studied the variation in transmission of COVID-19, locally in Wuhan, as well as on a larger spatial scale, among different cities and even among provinces in mainland China.Methods: In addition to reported numbers of new cases, we have been able to assemble detailed contact data for some of the initial clusters of COVID-19. This enabled estimation of the serial interval for clinical cases, as well as reproduction numbers for small and large regions.Findings: We estimated the average serial interval was 4.8 days. For early transmission in Wuhan, any infectious case produced as many as four new cases, transmission outside Wuhan was less intense, with reproduction numbers below two. During the rapid growth phase of the outbreak the region of Wuhan city acted as a hot spot, generating new cases upon contact, while locally, in other provinces, transmission was low.Interpretation: COVID-19 is capable of spreading very rapidly. The sizes of outbreak in provinces of mainland China mainly depended on the numbers of cases imported from Wuhan as the local reproduction numbers were low. The COVID-19 epidemic should be controllable with appropriate interventions (suspension of public transportation, cancellation of mass gatherings, implementation of surveillance and testing, and promotion of personal hygiene and face mask use).

This dashboard created by Operations Dashboard contains the most up-to-date coronavirus COVID-19 cases and latest trend plot. It covers China, the US, Canada, Australia (at province/state level), and the rest of the world (at country level, represented by either the country centroids or their capitals). Data sources are WHO, US CDC, China NHC, ECDC, and DXY. The China data is automatically updating at least once per hour, and non China data is updating manually. This layer is created and maintained by the Center for Systems Science and Engineering (CSSE) at the Johns Hopkins University. This service is supported by Esri Living Atlas team and JHU Data Services.

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.

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

China experienced another widespread Coronavirus disease 2019 (COVID-19) outbreak recently caused by the Omicron variant, which is less severe but far more contagious than the other COVID-19 variants, leading local governments to focus efforts on eliminating the spread of the disease. Previous studies showed that after “recovering” from the virus, some patients could re-test positive for COVID-19 with nucleic acid tests, challenging the control of disease spread. In this study, we aimed to analyze the clinical and laboratory characteristics of re-positive COVID-19 patients in Northeast China. We retrospectively analyzed data from confirmed reverse transcription polymerase chain reaction (RT-PCR) re-positive COVID-19 patients who were admitted to the First Hospital of Jilin University, Jilin Province, China, from March to June 2022. Detailed clinical symptoms, medical history, anti-Corona Virus (CoV) IgG and IgM levels, and CoV nucleic acid cycle threshold (Ct) values during the re-positive period were collected and analyzed. A total of 180 patients were included in this study, including 62 asymptomatic cases and 118 mild cases. The cohort included 113 men and 67 women, with an average age of 45.73 years. The median time between recovery from the virus and re-positivity was 13 days. Our results showed that the proportion of re-positive patients with symptoms was lower, and the nucleic acid test-positive duration was shorter during the re-positive period. Furthermore, in patients with underlying disease, the proportion of patients with symptoms was higher, anti-CoV IgG levels were lower, and the total disease duration was longer. In conclusion, during the re-positive period, the symptoms were milder, and the CoV nucleic acid test-positive course was shorter. The concomitant underlying disease is an important factor associated with clinical symptoms, and the overall course of COVID-19 re-positive patients may be associated with lower anti-CoV IgG levels. Large-scale and multicenter studies are recommended to better understand the pathophysiology of recurrence in patients with COVID-19.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is believed to have originated in Wuhan City, Hubei Province, China, in December 2019. Infection with this highly dangerous human-infecting coronavirus via inhalation of respiratory droplets from SARS-CoV-2 carriers results in coronavirus disease 2019 (COVID-19), which features clinical symptoms such as fever, dry cough, shortness of breath, and life-threatening pneumonia. Several COVID-19 waves arose in Taiwan from January 2020 to March 2021, with the largest outbreak ever having a high case fatality rate (CFR) (5.95%) between May and June 2021. In this study, we identified five 20I (alpha, V1)/B.1.1.7/GR SARS-CoV-2 (KMUH-3 to 7) lineage viruses from COVID-19 patients in this largest COVID-19 outbreak. Sequence placement analysis using the existing SARS-CoV-2 phylogenetic tree revealed that KMUH-3 originated from Japan and that KMUH-4 to KMUH-7 possibly originated via local transmission. Spike mutations M1237I and D614G were identified in KMUH-4 to KMUH-7 as well as in 43 other alpha/B.1.1.7 sequences of 48 alpha/B.1.1.7 sequences deposited in GISAID derived from clinical samples collected in Taiwan between 20 April and July. However, M1237I mutation was not observed in the other 12 alpha/B.1.1.7 sequences collected between 26 December 2020, and 12 April 2021. We conclude that the largest COVID-19 outbreak in Taiwan between May and June 2021 was initially caused by the alpha/B.1.1.7 variant harboring spike D614G + M1237I mutations, which was introduced to Taiwan by China Airlines cargo crew members. To our knowledge, this is the first documented COVID-19 outbreak caused by alpha/B.1.1.7 variant harboring spike M1237I mutation thus far. The largest COVID-19 outbreak in Taiwan resulted in 13,795 cases and 820 deaths, with a high CFR, at 5.95%, accounting for 80.90% of all cases and 96.47% of all deaths during the first 2 years. The high CFR caused by SARS-CoV-2 alpha variants in Taiwan can be attributable to comorbidities and low herd immunity. We also suggest that timely SARS-CoV-2 isolation and/or sequencing are of importance in real-time epidemiological investigations and in epidemic prevention. The impact of G614G + M1237I mutations in the spike gene on the SARS-CoV-2 virus spreading as well as on high CFR remains to be elucidated.

As of December 12, 2022, the Philippines had reported the highest number of SARS-CoV-2 Beta variant cases in the Asia-Pacific region, with 3,239 cases in total. In comparison, a total of six cases of the coronavirus (COVID-19) Beta variant had been reported in China as of December 12, 2022. The SARS-CoV-2 Beta variant (B.1.351) was first detected in South Africa in late 2020.

The first two cases of the new coronavirus (COVID-19) in Italy were recorded between the end of January and the beginning of February 2020. Since then, the number of cases in Italy increased steadily, reaching over 26.9 million as of January 8, 2025. The region mostly hit by the virus in the country was Lombardy, counting almost 4.4 million cases. On January 11, 2022, 220,532 new cases were registered, which represented the biggest daily increase in cases in Italy since the start of the pandemic. The virus originated in Wuhan, a Chinese city populated by millions and located in the province of Hubei. More statistics and facts about the virus in Italy are available here.For a global overview, visit Statista's webpage exclusively dedicated to coronavirus, its development, and its impact.

The ongoing coronavirus disease 2019 (COVID-19) pandemic represents one of the most exigent threats of our lifetime to global public health and economy. As part of the pandemic, from January 10 to March 10, 2020, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) began to spread in Hefei (Anhui Province, China) with a total of 174 confirmed cases of COVID-19. During this period, we were able to gather critical information on the transmission and evolution of pathogens through genomic surveillance. Particularly, the objective of our study was to track putative variants of SARS-CoV-2 circulating in Hefei for the first time and contribute to the global effort toward elucidating the molecular epidemic profile of the virus. Patients who showed symptoms of COVID-19 were routinely tested for SARS-CoV-2 infections via RT-PCR at the First Affiliated Hospital of Anhui Medical University. Whole-genome sequencing was performed on 97 clinical samples collected from 29 confirmed COVID-19 patients. As a result, we identified a local novel single-nucleotide polymorphism site (10,380) harboring a G → T mutation (Gly → Val) in Hefei. Further phylogenetic network analysis with all the sequences of SARS-CoV-2 deposited in GenBank collected in East and Southeast Asia revealed a local subtype of S-type SARS-CoV-2 (a1) harboring a C → T synonymous mutation (Leu) at position 18,060 of ORF1b, likely representing a local SARS-CoV-2 mutation site that is obviously concentrated in Hefei and the Yangtze River Delta region. Moreover, clinical investigation on the inflammatory cytokine profile of the patients suggested that mutations at positions 18,060 (the shared variable site of subtype a1) and 28,253(harboring a C → T synonymous mutation, Phe) were associated with milder immune responses in the patients.

This feature layer contains the most up-to-date COVID-19 cases for the US, Canada. Data sources: WHO, CDC, ECDC, NHC, DXY, 1point3acres, Worldometers.info, BNO, state and national government health departments, and local media reports. The China data is automatically updating at least once per hour, and non China data is updating manually. This layer is created and maintained by the Center for Systems Science and Engineering (CSSE) at the Johns Hopkins University. This feature layer is supported by Esri Living Atlas team and JHU Data Services. This layer is opened to the public and free to share. Contact Johns Hopkins.

IMPORTANT NOTICE:

1. Fields for Active Cases and Recovered Cases are set to 0 in all locations. John Hopkins has not found a reliable source for this information at the county level but will continue to look and carry the fields.

2. Fields for Incident Rate and People Tested are placeholders for when this becomes available at the county level.

3. In some instances, cases have not been assigned a location at the county scale. those are still assigned a state but are listed as unassigned and given a Lat Long of 0,0.

Data Field Descriptions by Alias Name:

• Province/State: (Text) Country Province or State Name (Level 2 Key)
  
• Country/Region: (Text) Country or Region Name (Level 1 Key)
  
• Last Update: (Datetime) Last data update Date/Time in UTC
• Latitude: (Float) Geographic Latitude in Decimal Degrees (WGS1984)
• Longitude: (Float) Geographic Longitude in Decimal Degrees (WGS1984)
• Confirmed: (Long) Best collected count of Confirmed Cases reported by geography
  
• Recovered: (Long) Not Currently in Use, JHU is looking for a source
• Deaths: (Long) Best collected count for Case Deaths reported by geography
  
• Active: (Long) Confirmed - Recovered - Deaths (computed) Not Currently in Use due to lack of Recovered data
  
• County: (Text) US County Name (Level 3 Key)
  
• FIPS: (Text) US State/County Codes
  
• Combined Key: (Text) Comma separated concatenation of Key Field values (L3, L2, L1)
  
• Incident Rate: (Long)
  
• People Tested: (Long) Not Currently in Use Placeholder for additional data
  
• People Hospitalized: (Long) Not Currently in Use Placeholder for additional data

ObjectivesTo understand the epidemic characteristics of various SARS-CoV-2 variants, we mainly focus on analyzing general epidemic profiles, viral mutation, and evolution of COVID-19 outbreaks caused by different SARS-CoV-2 variants of concern (VOCs) in China as of August 2022.MethodsWe systematically sorted out the general epidemic profiles of outbreaks caused by various SARS-CoV-2 VOCs in China, compared the differences of outbreaks caused by Delta and Omicron VOCs, and analyzed the mutational changes of subvariants between the same outbreak and different outbreaks.FindingsBy 15 August 2022, a total of 2, 33, and 124 COVID-19 outbreaks caused by Alpha, Delta, and Omicron VOCs, respectively, were reported in different regions of China. In terms of the number of outbreaks, the extent of affected areas, and the total number of confirmed cases, Omicron VOCs were more widespread than the other variants. The most frequently circulating PANGO lineages in China were B.1.617.2 and AY.122 in Delta VOCs, and BA.2.2.1, BA.2, BA.2.2, and BA.5 for Omicron VOCs. Additional mutations in the genome of the SARS-CoV-2 strain were frequently observed in outbreaks with longer duration and higher numbers of infections.ConclusionThrough the comprehensive analysis of the COVID-19 outbreaks, the influences, and the evolution of the SARS-CoV-2 variants in China, we found differences between outbreaks caused by Delta and Omicron VOCs. The genome of SARS-CoV-2 continued to evolve within the same outbreak and across outbreaks occurring in different locations or at different times. These findings suggest that rapidly containing an Omicron virus outbreak can not only reduce the spread of the virus but also delay the virus’s mutation frequency.

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.

Background: At present, the global sever acute respiratory syndrome coronavirus 2 (SARS-CoV-2) situation is still grim, and the risk of local outbreaks caused by imported viruses is high. Therefore, it is necessary to monitor the genomic variation and genetic evolution characteristics of SARS-CoV-2. The main purpose of this study was to detect the entry of different SARS-CoV-2 variants into Jiangsu Province, China.Methods: First, oropharyngeal swabs were collected from 165 patients (55 locally confirmed cases and 110 imported cases with confirmed and asymptomatic infection) diagnosed with SARS-CoV-2 infection in Jiangsu Province, China between January 2020 and June 2021. Then, whole genome sequencing was used to explore the phylogeny and find potential mutations in genes of the SARS-CoV-2. Last, association analysis among clinical characteristics and SARS-CoV-2 Variant of Concern, pedigree surveillance analysis of SARS-COV-2, and single nucleotide polymorphisms (SNPs) detection in SARS-COV-2 samples was performed.Results: More men were infected with the SARS-CoV-2 when compared with women. The onset of the SARS-CoV-2 showed a trend of younger age. Moreover, the number of asymptomatic infected patients was large, similar to the number of common patients. Patients infected with Alpha (50%) and Beta (90%) variants were predominantly asymptomatic, while patients infected with Delta (17%) variant presented severe clinical features. A total of 935 SNPs were detected in 165 SARS-COV-2 samples. Among which, missense mutation (58%) was the dominant mutation type. About 56% of SNPs changes occurred in the open reading frame 1ab (ORF1ab) gene. Approximately, 20% of SNP changes occurred in spike glycoprotein (S) gene, such as p.Asp501Tyr, p.Pro681His, and p.Pro681Arg. In total, nine SNPs loci in S gene were significantly correlated with the severity of patients. It is worth mentioning that amino acid substitution of p.Asp614Gly was significantly positively correlated with the clinical severity of patients. The amino acid replacements of p.Ser316Thr and p.Lu484Lys were significantly negatively correlated with the course of disease.Conclusion: Sever acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may further undergo a variety of mutations in different hosts, countries, and weather conditions. Detecting the entry of different virus variants of SARS-CoV-2 into Jiangsu Province, China may help to monitor the spread of infection and the diversity of eventual recombination or genomic mutations.

Coronavirus Diagnostics Market Outlook

The global coronavirus diagnostics market size was estimated at USD 20.5 billion in 2023 and is projected to reach USD 34.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 6.1% from 2024 to 2032. The growth of this market is majorly driven by the ongoing demand for accurate and rapid diagnostic solutions to manage the COVID-19 pandemic and potential future waves or outbreaks of coronavirus variants.

The primary growth factor in the coronavirus diagnostics market is the continuous need for efficient and timely diagnosis, which is crucial for managing the spread of COVID-19 and other coronaviruses. As new variants of the virus continue to emerge, the need for robust diagnostic testing remains high to ensure early detection and appropriate treatment. Governments and healthcare organizations worldwide have heavily invested in diagnostic infrastructures, further propelling the market growth. Additionally, the integration of advanced technologies such as AI and machine learning in diagnostic tools has improved accuracy and speed, contributing to market expansion.

Another significant growth factor is the increase in the global aging population, which is more susceptible to severe outcomes from COVID-19 infections. Older adults often have comorbid conditions that necessitate regular and reliable testing to manage their health effectively. The pandemic has highlighted the need for improved healthcare systems and diagnostics, leading to long-term investments in this sector. Moreover, public awareness regarding the importance of early diagnosis and preventive healthcare has surged, encouraging the adoption of regular testing protocols even post-pandemic.

Furthermore, the rising number of diagnostic laboratories and the expansion of point-of-care testing facilities have significantly contributed to the market growth. The convenience and accessibility of point-of-care testing have made it a preferred option for many individuals, reducing the burden on centralized laboratories and speeding up the diagnosis process. Additionally, the development and approval of various types of COVID-19 tests, including molecular, antigen, and serology tests, have provided multiple options for accurate and timely diagnosis, catering to different needs and scenarios.

Regionally, North America and Europe have been at the forefront of the coronavirus diagnostics market due to their advanced healthcare infrastructure and significant investments in healthcare R&D. However, the Asia Pacific region is expected to witness the fastest growth during the forecast period. This growth can be attributed to the large population base, increasing healthcare expenditure, and the rising prevalence of COVID-19 cases in countries like India and China. Government initiatives to enhance healthcare infrastructure and increased awareness about the importance of diagnostics will further drive the market in this region.

Test Type Analysis

The coronavirus diagnostics market is segmented by test type into molecular tests, serology tests, and antigen tests, each playing a critical role in managing the pandemic. Molecular tests, particularly RT-PCR, have been the gold standard for COVID-19 diagnosis due to their high sensitivity and specificity. RT-PCR tests detect the viral RNA, making them effective for early detection even in asymptomatic cases. The demand for molecular tests has surged due to their accuracy, driving advancements in this segment, including the development of rapid PCR tests that reduce the time required for diagnosis.

Serology tests, which detect antibodies produced in response to SARS-CoV-2 infection, are essential for understanding the spread of the virus within a population and identifying individuals who have developed immunity. These tests have been pivotal for epidemiological studies and vaccine efficacy assessments. The development of high-throughput and point-of-care serology tests has expanded their usage, making them a valuable tool in both clinical and research settings. Despite their usefulness, the market for serology tests faces challenges such as the variability in antibody response among individuals and the need for standardized testing protocols.

Antigen tests provide a rapid diagnostic solution by detecting specific proteins from the virus. These tests are less complex and quicker compared to molecular tests, making them suitable for mass screening and use in resource-limited settings. The convenience and speed of antigen tests have led to their widespread adoption in various scenarios

Coronavirus disease 2019 (COVID-19) cases in China has grown rapidly after adjustment of the dynamic zero-COVID-19 strategy. However, how different vaccination states affect symptoms, severity and post COVID conditions was unclear. Here, we used an online questionnaire to investigate the infection status of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among 11,897 participants, with 55.55% positive and 28.42% negative. The common COVID-19 symptoms were fatigue (73.31%), cough (70.02%), fever (65.25%) and overall soreness (58.64%); self-reported asymptomatic infection accounted for 0.7% of participants. The persistent symptoms at 1 month after infection included fatigue (48.7%), drowsiness (34.3%), cough (30.1%), decreased exercise ability (23.1%) and pharyngeal discomfort (19.4%), which was reduced by more than 200% at 2 months. Participants with complications such as chronic obstructive pulmonary disease, respiratory diseases, diabetes, hypertension, etc. have a higher proportion of hospitalization and longer recovery time (p 

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.

COVID-19 is an acute respiratory infectious disease caused by SARS-CoV-2. It was first reported in Wuhan, China in December 2019 and rapidly spread globally in early 2020, triggering a global pandemic. In December 2022, China adjusted the dynamic COVID-zero strategy that lasted for three years. The number of positive cases in China increased rapidly in the short term. Weihai was also affected during this period. We conducted genomic surveillance of SARS-CoV-2 variants in Weihai during this period, hoping to understand the changes in the genomic characteristics of SARS-CoV-2 before and after the adjustment of the epidemic policy. In this study,we collected SARS-CoV-2 positive samples from March 2022 to March 2023 in Weihai and performed SARS-CoV-2 whole genome sequencing on these samples using next-generation sequencing technology. we obtained a total of 704 SARS-CoV-2 whole genome sequences, and selected 581 high-quality sequences for further analysis. The analysis results showed that from March 2022 to November 2022, before the adjustment of epidemic policy, the COVID-19 cases in Weihai were mainly from four local clusters,which were caused by four variants, including BA.2,BA.1.1,P.1.15 and BA.5.2.1. Phylogenetic analysis showed that: In the same cluster,the sequences between each other were highly homologous, and the whole genome sequence were almost identical. After December 2022, the epidemic policy was adjusted, BF.7 and BA.5.2 became the dominant variants in Weihai, consistent with the main domestic strains in China during the same period. Phylodynamic analysis showed that BF.7 and BA.5.2 had a large amount of genetic diversities in December, and the effective population size of BF.7 and BA.5.2 also showed explosive growth in December. In conclusion, we reported the composition and dynamic trend of SARS-CoV-2 variants in Weihai from March 2022 to March 2023. We found that there have been significant changes in the variants and expansion patterns of SARS-CoV-2 before and after the adjustment of epidemic policies. But the dominant variants in Weihai were the same as the SARS-CoV-2 variants circulating globally at the same time and we found no persistently dominant variants or new lineages during this period.

Covid-19 Therapeutic Market Outlook

The global Covid-19 Therapeutic market size was valued at approximately $XX billion in 2023 and is projected to reach around $XX billion by 2032, growing at a CAGR of XX% during the forecast period. The rapid evolution of the Covid-19 pandemic has driven significant advancements and investments in therapeutic solutions, which in turn is propelling the market growth. The continuous rise in the number of Covid-19 cases and the increasing focus on developing effective treatments are primary factors augmenting this market's expansion.

One of the principal growth factors for the Covid-19 Therapeutic market is the unprecedented global effort in research and development. Governments and private sectors have poured substantial funds into discovering, testing, and producing treatments for Covid-19. Initiatives like the Accelerating Covid-19 Therapeutic Interventions and Vaccines (ACTIV) partnership have streamlined processes, fostering faster development and distribution of effective therapeutics. These collaborative efforts are expected to sustain the market's growth momentum in the coming years.

Another critical driver is the variation in Covid-19 variants, which continuously challenge existing treatment protocols. The emergence of new variants has necessitated the ongoing development and adjustment of therapeutics to maintain efficacy. This dynamic landscape ensures ongoing demand for novel therapeutic solutions. Furthermore, the increasing understanding of the virus's pathology has led to the discovery of new drug targets and treatment strategies, enhancing the market's growth prospects.

The role of regulatory bodies has also been pivotal in accelerating market growth. Agencies like the FDA and EMA have implemented expedited review processes for Covid-19 therapeutics, allowing for quicker approval and deployment. Emergency Use Authorizations (EUAs) have made it possible for promising treatments to be used in clinical settings sooner, thus meeting immediate healthcare needs. This regulatory support is a substantial growth driver as it reduces the time-to-market for new therapies.

Regionally, North America holds a significant share of the Covid-19 Therapeutic market, driven by substantial investments in healthcare infrastructure and a strong focus on research and development. Europe follows closely, benefiting from robust healthcare systems and collaborative R&D efforts. Asia Pacific is expected to witness the fastest growth rate due to increasing healthcare expenditure and a rising number of Covid-19 cases, particularly in densely populated countries like India and China. Latin America and the Middle East & Africa are also anticipated to see growth, albeit at a slower pace due to varying healthcare infrastructure and resource availability.

Drug Class Analysis

The Covid-19 Therapeutic market is segmented by drug class into Antiviral, Monoclonal Antibodies, Corticosteroids, Immunomodulators, and Others. The Antiviral segment has been particularly crucial in managing Covid-19, as these drugs directly inhibit viral replication. Remdesivir, an antiviral initially developed for Ebola, received global attention and was among the first drugs granted EUA for Covid-19 treatment. The demand for antivirals remains high as they are a frontline defense against viral propagation.

Monoclonal Antibodies have also gained prominence due to their ability to target specific virus proteins. Treatments like Regeneron's REGEN-COV and Eli Lilly's Bamlanivimab have been game-changers in treating Covid-19, especially for patients at high risk of severe disease. These therapies not only provide immediate benefits but also open avenues for further research into monoclonal antibody applications against evolving variants.

Corticosteroids, particularly Dexamethasone, have been pivotal in reducing inflammation and preventing cytokine storms in severe Covid-19 cases. The inclusion of corticosteroids in treatment protocols has significantly improved patient outcomes. Their widespread availability and low cost also make them an accessible option globally, contributing to the segment's strong market presence.

Immunomodulators like Tocilizumab and Baricitinib have been essential in managing immune response dysregulation in Covid-19 patients. These drugs mitigate severe inflammatory reactions, thus reducing mortality rates. The ongoing research into other immunomodulatory agents continues to expand the therapeutic arsenal available for combating Co