Background: Novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), is now sweeping across the world. A substantial proportion of infections only lead to mild symptoms or are asymptomatic, but the proportion and infectivity of asymptomatic infections remains unknown. In this paper, we proposed a model to estimate the proportion and infectivity of asymptomatic cases, using COVID-19 in Henan Province, China, as an example.Methods: We extended the conventional susceptible-exposed-infectious-recovered model by including asymptomatic, unconfirmed symptomatic, and quarantined cases. Based on this model, we used daily reported COVID-19 cases from January 21 to February 26, 2020, in Henan Province to estimate the proportion and infectivity of asymptomatic cases, as well as the change of effective reproductive number, Rt.Results: The proportion of asymptomatic cases among COVID-19 infected individuals was 42% and the infectivity was 10% that of symptomatic ones. The basic reproductive number R0 = 2.73, and Rt dropped below 1 on January 31 under a series of measures.Conclusion: The spread of the COVID-19 epidemic was rapid in the early stage, with a large number of asymptomatic infected individuals having relatively low infectivity. However, it was quickly brought under control with national measures.

Meta-analysis dataset for the following article: A Rapid Review and Meta-Analysis of the Asymptomatic Proportion of PCR-Confirmed SARS-CoV-2 Infections in Community Settings.These CSV format data enable the calculation of a pooled asymptomatic proportion of PCR-confirmed COVID-19 cases based on available studies (n=21) with methodologically-appropriate design to detect and identify truly asymptomatic cases. The asymptomatic proportion is given as the number of asymptomatic PCR-confirmed infections over the total number of PCR-confirmed infections.The file also includes the completed PRISMA checklist for this review.

First reported in Wuhan, China, in December 2019, now more than 846,200 confirmed cases of COVID-19 are spread across 187 countries worldwide. The US and several countries in Europe such as Italy, Spain, and Belgium have continued to see a decrease in daily cases. Russia, Brazil, and Latin American countries are seeing increasing trends. India has also seen an increase in the number of new cases reported despite strict distancing measures taken early on.
Special populations analysis covered in the report include the following:
COVID-19 in children may result in systemic multisystem syndrome with severe outcomes.
Childhood routine vaccination rates drop during pandemic.
COVID-19’s impact in pregnant women unclear, though most cases are asymptomatic.
The COVID-19 pandemic could cause an increase in the prevalence of post-traumatic stress disorder (PTSD).
Complications of opioid addiction will be challenging for the management of disease during the COVID-19 pandemic. Read More

Background The proportion of asymptomatic carriers and transmission risk factors of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among household and non-household contacts remains unclear. In Singapore, extensive contact tracing by the Ministry of Health for every diagnosed COVID-19 case, and legally enforced quarantine and intensive health surveillance of close contacts provided a rare opportunity to determine asymptomatic attack rates and SARS-CoV-2 transmission risk factors among community close contacts of patients with COVID-19. Methods This retrospective cohort study involved all close contacts of confirmed COVID-19 cases in Singapore, identified between Jan 23 and April 3, 2020. Household contacts were defined as individuals who shared a residence with the index COVID-19 case. Non-household close contacts were defined as those who had contact for at least 30 min within 2 m of the index case. All patients with COVID-19 in Singapore received inpatient treatment, with access restricted to health-care staff. All close contacts were quarantined for 14 days with thrice-daily symptom monitoring via telephone. Symptomatic contacts underwent PCR testing for SARS-CoV-2. Secondary clinical attack rates were derived from the prevalence of PCR-confirmed SARS-CoV-2 among close contacts. Consenting contacts underwent serology testing and detailed exposure risk assessment. Bayesian modelling was used to estimate the prevalence of missed diagnoses and asymptomatic SARS-CoV-2-positive cases. Univariable and multivariable logistic regression models were used to determine SARS-CoV-2 transmission risk factors. Findings Between Jan 23 and April 3, 2020, 7770 close contacts (1863 household contacts, 2319 work contacts, and 3588 social contacts) linked to 1114 PCR-confirmed index cases were identified. Symptom-based PCR testing detected 188 COVID-19 cases, and 7582 close contacts completed quarantine without a positive SARS-CoV-2 PCR test. Among 7518 (96·8%) of the 7770 close contacts with complete data, the secondary clinical attack rate was 5·9% (95% CI 4·9-7·1) for 1779 household contacts, 1·3% (0·9-1·9) for 2231 work contacts, and 1·3% (1·0-1·7) for 3508 social contacts. Bayesian analysis of serology and symptom data obtained from 1150 close contacts (524 household contacts, 207 work contacts, and 419 social contacts) estimated that a symptom-based PCR-testing strategy missed 62% (95% credible interval 55-69) of COVID-19 diagnoses, and 36% (27-45) of individuals with SARS-CoV-2 infection were asymptomatic. Sharing a bedroom (multivariable odds ratio [OR] 5·38 [95% CI 1·82-15·84]; p=0·0023) and being spoken to by an index case for 30 min or longer (7·86 [3·86-16·02]; p<0·0001) were associated with SARS-CoV-2 transmission among household contacts. Among non-household contacts, exposure to more than one case (multivariable OR 3·92 [95% CI 2·07-7·40], p<0·0001), being spoken to by an index case for 30 min or longer (2·67 [1·21-5·88]; p=0·015), and sharing a vehicle with an index case (3·07 [1·55-6·08]; p=0·0013) were associated with SARS-CoV-2 transmission. Among both household and non-household contacts, indirect contact, meal sharing, and lavatory co-usage were not independently associated with SARS-CoV-2 transmission. Interpretation Targeted community measures should include physical distancing and minimising verbal interactions. Testing of all household contacts, including asymptomatic individuals, is warranted. Funding Ministry of Health of Singapore, National Research Foundation of Singapore, and National Natural Science Foundation of China.

In the United States between February 12 and March 16, 2020, the percentage of COVID-19 patients hospitalized with the disease increased with age. Findings estimated that up to 70 percent of adults aged 85 years and older were hospitalized.

Who is at higher risk from COVID-19? The same study also found that coronavirus patients aged 85 and older were at the highest risk of death. There are other risk factors besides age that can lead to serious illness. People with pre-existing medical conditions, such as diabetes, heart disease, and lung disease, can develop more severe symptoms. In the U.S. between January and May 2020, case fatality rates among confirmed COVID-19 patients were higher for those with underlying health conditions.

How long should you self-isolate? As of August 24, 2020, more than 16 million people worldwide had recovered from COVID-19 disease, which includes patients in health care settings and those isolating at home. The criteria for discharging patients from isolation varies by country, but asymptomatic carriers of the virus can generally be released ten days after their positive case was confirmed. For patients showing signs of the illness, they must isolate for at least ten days after symptom onset and also remain in isolation for a short period after the symptoms have disappeared.

The counties of Trousdale and Lake – both in Tennessee – had the highest COVID-19 infection rates in the United States as of June 9, 2020. Dakota, Nobles, and Lincoln also ranked among the U.S. counties with the highest number of coronavirus cases per 100,000 people.

Coronavirus hits the East Coast In the United States, the novel coronavirus had infected around 5.4 million people and had caused nearly 170,000 deaths by mid-August 2020. The densely populated states of New York and New Jersey were at the epicenter of the outbreak in the country. New York City, which is composed of five counties, was one of the most severely impacted regions. However, the true level of transmission is likely to be much higher because many people will be asymptomatic or suffer only mild symptoms that are not diagnosed.

All states are in crisis The first coronavirus case in the U.S. was confirmed in the state of Washington in mid-January 2020. At the time, it was unclear how the virus was spreading; we now know that close contact with an infected person and breathing in their respiratory droplets is the primary mode of transmission. It is no surprise that the four states with the most coronavirus cases are those with the highest populations: New York, Texas, Florida, and California. However, Louisiana was the state with the highest COVID-19 infection rate per 100,000 people as of August 24, 2020.

Executive summary: Studying the spread and epidemiological characteristics of COVID-19 virus specially in household settings are needed to prepare our self-better in preventing and controlling this epidemic. In this study we proposed a conceptual framework of four level of determinates and tried to understand the transmission dynamics of COVID-19 among household contacts along with clinical, epidemiological and virologic characteristics of the infection.

Aims & Objectives:

the proportion of asymptomatic cases and symptomatic cases;

the incubation period of COVID-19 and the duration of infectiousness and of detectable shedding;

the serial interval of COVID-19 infection;

clinical risk factors for COVID-19, and the clinical course and severity of disease;

high-risk population subgroups;

the secondary infection rate and secondary clinical attack rate of COVID-19 infection among household contacts; and

the associations of various factors across four dimensions interaction associated with risk of transmission

Methodology: This was a case-ascertained study where all susceptible contacts of a laboratory confirmed COVID-19 case were studied prospective for four weeks after their enrolment. It was done in New Delhi, during the end of first wave as well as whole second wave from December 2020 to July 2021. The study team collected the key information by questionnaire along with blood and oro-nasal swab during the household visits. Follow-up was done on day 7, 14 and 28 for observing the disease characteristic and symptomatology along with confirmation by serum and oro-nasal swab testing. Daily characteristics of the infection were noted by the participants on symptoms diary.

Results: We enrolled 99 households, each having one laboratory-confirmed COVID-19 index case along with their 318 susceptible contacts. By the end of the follow-up, secondary infection rate was seen at 55.5%, while seroconversion in 46.6%. Hospitalization and case fatality rate was 3.83% and 1.7% respectively. Among epidemiological characteristics we observed serial interval of 8.0 ± 6.7 days, generation time 3.8 ± 6.4, while secondary attack rate was 54.9%. The predictors of secondary infection among individual contact level were being female (OR:2.13, 95% CI:1.27 - 3.57), age of the household contact (1.01;1.00 - 1.03), symptoms at baseline (3.39; 1.61- 7.12) and during follow-up (3.18; 1.64 - 6.19), while only symptoms during follow-up (3.81: 1.43 - 10.14) and being RT-PCR positive (8.32; 3.22 -21.54) was significantly and independently associated with seroconversion among household contacts. Among index case-level age of the primary case (1.03; 1.01 -1.04) and any symptoms during follow-up (6.29; 1.83-21.63) significantly and independently associated with secondary infection while any symptoms during follow-up was associated with seroconversion among household contacts. Among household-level characteristics having more rooms (4.44; 2.16 - 9.13) independently associated with secondary infection, while more rooms (3.98; 1.23 -12.90) along with overcrowding (0.37; 0.16 - 0.82) associated with seroconversion. Among contact pattern only taking care of the index case (2.02;1.21- 3.38) was significantly and independently associated with secondary infection, while none was associated with seroconversion.

Conclusion: A high secondary cases and secondary attack rate was seen in our study. This highlights the need to adopts strict measure and advocate COVID appropriate behaviours in order to break the transmission chain at household level. The targeted approach at household contacts with higher risk would be efficient in limiting the development of infection among susceptible contacts.

As of December 22, 2022, Austria had performed the most COVID-19 tests per one million population among the countries most severely impacted by the pandemic. The U.S. has conducted over 1.1 billion COVID-19 tests in total.

Testing is the key to controlling virus The World Health Organization sent a clear message to all countries in March 2020: test, test, and test. The more tests that are conducted, the easier it becomes to track the spread of the virus and reduce transmission. Many countries followed the advice, identifying a greater number of cases at an earlier stage, isolating infected individuals, and limiting the spread of the disease to others. As cases numbers have decreased in some regions so have restrictions, however many countries still require negative test results before entering the country.

What is an antibody test? Countries around the world made widespread testing a key part of their plans to exit lockdown. However, the global demand for antibody test kits has been huge. The kits are used to identify antibodies in a person’s blood sample. The presence of antibodies means the individual has been exposed to the SARS-CoV-2 virus and developed antibodies to help fight it. Antibody tests are important in detecting infections in people who are asymptomatic, i.e., showing few or no symptoms. Asymptomatic carriers may have unwittingly contributed to the rapid spread of the disease.

Asymptomatic carriers contribute to the spread of Coronavirus Disease 2019 (COVID-19), but their clinical characteristics, viral kinetics, and antibody responses remain unclear. A total of 56 COVID-19 patients without symptoms at admission and 19 age-matched symptomatic patients were enrolled. RNA of SARS-CoV-2 was tested using transcriptase quantitative PCR, and the total antibodies (Ab), IgG, IgA, and IgM against the SARS-CoV-2 were tested using Chemiluminescence Microparticle Immuno Assay. Among 56 patients without symptoms at admission, 33 cases displayed symptoms and 23 remained asymptomatic throughout the follow-up period. 43.8% of the asymptomatic carriers were children and none of the asymptomatic cases had recognizable changes in C-reactive protein or interleukin-6, except one 64-year-old patient. The initial threshold cycle value of nasopharyngeal SARS-CoV-2 in asymptomatic carriers was similar to that in pre-symptomatic and symptomatic patients, but the positive viral nucleic acid detection period of asymptomatic carriers (9.63 days) was shorter than pre-symptomatic patients (13.6 days). There were no obvious differences in the seropositive conversion rate of total Ab, IgG, and IgA among the three groups, though the rates of IgM varied largely. The average peak IgG and IgM COI of asymptomatic cases was 3.5 and 0.8, respectively, which is also lower than those in symptomatic patients with peaked IgG and IgM COI of 4.5 and 2.4 (p < 0.05). Young COVID-19 patients seem to be asymptomatic cases with early clearance of SARS-CoV-2 and low levels of IgM generation but high total Ab, IgG, and IgA. Our findings provide empirical information for viral clearance and antibody kinetics of asymptomatic COVID-19 patients.

As of November 14, 2022, the United States had recorded almost 98 million cases of COVID-19. The country had also reported a total number of over one million deaths from the disease.

COVID-19 testing remains important The cumulative number of coronavirus cases worldwide reached almost 633 million towards the beginning of November 2022. Demand for test kits has at times exceeded production levels, but many countries continue to test citizens to more effectively control rises in cases. The U.S. has performed the most tests worldwide, followed by India and the United Kingdom.

The silent spread of the coronavirus Widespread testing will also help to detect people who might be asymptomatic – showing few or no symptoms of the illness. These carriers are unwittingly transmitting the virus to others, and the threat of silent transmission is one reason why mass lockdowns have been imposed around the world. However, as asymptomatic carriers produce no symptoms, they may have developed some natural immunity to the illness. Viruses are not as easily spread in communities with high rates of immunity, which helps to protect more vulnerable groups of people. When an infection rate is less than one, a community has achieved herd immunity.

Around 282 thousand new cases of COVID-19 were reported in the United States during the week ending November 11, 2022. Between January 20, 2020 and November 11, 2022 there had been around 96.8 million confirmed cases of COVID-19 with over one million deaths in the U.S. as reported by the World Health Organization.

How did the coronavirus outbreak start? Pneumonia cases with an unknown cause were first reported in the Hubei province of China at the end of December 2019. Patients described symptoms including a fever and difficulty breathing, and early reports suggested no evidence of human-to-human transmission. We now know that a novel coronavirus named SARS-CoV-2 is causing the disease COVID-19. The virus has been characterized as a pandemic and continues to spread from person to person – there have been around 642 million cases worldwide as of November 17, 2022.

The importance of isolation and quarantine In an effort to contain the early spread of the virus, China tightened travel restrictions and enforced isolation measures in the hardest-hit areas. The World Health Organization endorsed this strategy, and countries around the world implemented similar quarantine measures. Staying at home can limit the spread of the virus, and this applies to individuals who are only showing mild symptoms or none at all. Asymptomatic carriers of the virus – those that are experiencing no symptoms – may transmit the virus to people who are at a higher risk of getting very sick.

Every day, schools, child care centres and licensed home child care agencies report to the Ministry of Education on children, students and staff that have positive cases of COVID-19. If there is a discrepancy between numbers reported here and those reported publicly by a Public Health Unit, please consider the number reported by the Public Health Unit to be the most up-to-date. Schools and school boards report when a school is closed to the Ministry of Education. Data is current as of 2:00 pm the previous day. This dataset is subject to change. Data is only updated on weekdays excluding provincial holidays Effective June 15, 2022, board and school staff will not be expected to report student/staff absences and closures in the Absence Reporting Tool. The ministry will no longer report absence rates or school/child care closures on Ontario.ca for the remainder of the school year. Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak. ##Summary of school closures This is a summary of school closures in Ontario. Data includes: * Number of schools closed * Total number of schools * Percentage of schools closed ##School Absenteeism This report provides a summary of schools and school boards that have reported staff and student absences. Data includes: * School board * School * City or Town * Percentage of staff and students who are absent ##Summary of cases in schools This report provides a summary of COVID-19 activity in publicly-funded Ontario schools. Data includes: * School-related cases (total) * School-related student cases * School-related staff cases * Current number of schools with a reported case * Current number of schools closed Note: In some instances the type of cases are not identified due to privacy considerations. ##Schools with active COVID-19 cases This report lists schools and school boards that have active cases of COVID-19. Data includes : * School Board * School * Municipality * Confirmed Student Cases * Confirmed Staff Cases * Total Confirmed Cases ##Cases in school board partners This report lists confirmed active cases of COVID-19 for other school board partners (e.g. bus drivers, authorized health professionals etc.) and will group boards if there is a case that overlaps. Data includes : * School Board(s) * School Municipality * Confirmed cases – other school board partners ##Summary of targeted testing conducted in schools This data includes all tests that have been reported to the Ministry of Education since February 1, 2021. School boards and other testing partners will report to the Ministry every Wednesday based on data from the previous seven days. Data includes : * School boards or regions * Number of schools invited to participate in the last seven days * Total number of tests conducted in the last seven days * Cumulative number of tests conducted * Number of new cases identified in the last seven days * Cumulative number of cases identified ##Summary of asymptomatic testing at conducted in pharmacies: This is a summary of COVID-19 rapid antigen testing conducted at participating pharmacies in Ontario since March 27, 2021. * Total number of tests conducted in the last seven days * Cumulative number of tests conducted * Number of new cases identified in the last seven days * Cumulative number of cases identified

SARS-CoV-2 infection causes diverse outcomes ranging from asymptomatic infection to respiratory distress and death. A major unresolved question is whether prior immunity to endemic, human common cold coronaviruses (hCCCoVs) impacts susceptibility to SARS-CoV-2 infection or immunity following infection and vaccination. Therefore, we analyzed samples from the same individuals before and after SARS-CoV-2 infection or vaccination. We found hCCCoV antibody levels increase after SARS-CoV-2 exposure, demonstrating cross-reactivity. However, a case-control study indicates that baseline hCCCoV antibody levels are not associated with protection against SARS-CoV-2 infection. Rather, higher magnitudes of pre-existing betacoronavirus antibodies correlate with more SARS-CoV-2 antibodies following infection, an indicator of greater disease severity. Additionally, immunization with hCCCoV spike proteins before SARS-CoV-2 immunization impedes the generation of SARS-CoV-2-neutralizing antibodies in mice. Together, these data suggest that pre-existing hCCCoV antibodies hinder SARS-CoV-2 antibody-based immunity following infection and provide insight on how pre-existing coronavirus immunity impacts SARS-CoV-2 infection, which is critical considering emerging variants.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the ongoing coronavirus disease 2019 (COVID-19) pandemic. Understanding the influence of mutations in the SARS-CoV-2 gene on clinical outcomes is critical for treatment and prevention. Here, we analyzed all high-coverage complete SARS-CoV-2 sequences from GISAID database from January 1, 2020, to January 1, 2021, to mine the mutation hotspots associated with clinical outcome and developed a model to predict the clinical outcome in different epidemic strains. Exploring the cause of mutation based on RNA-dependent RNA polymerase (RdRp) and RNA-editing enzyme, mutation was more likely to occur in severe and mild cases than in asymptomatic cases, especially A > G, C > T, and G > A mutations. The mutations associated with asymptomatic outcome were mainly in open reading frame 1ab (ORF1ab) and N genes; especially R6997P and V30L mutations occurred together and were correlated with asymptomatic outcome with high prevalence. D614G, Q57H, and S194L mutations were correlated with mild and severe outcome with high prevalence. Interestingly, the single-nucleotide variant (SNV) frequency was higher with high percentage of nt14408 mutation in RdRp in severe cases. The expression of ADAR and APOBEC was associated with clinical outcome. The model has shown that the asymptomatic percentage has increased over time, while there is high symptomatic percentage in Alpha, Beta, and Gamma. These findings suggest that mutation in the SARS-CoV-2 genome may have a direct association with clinical outcomes and pandemic. Our result and model are helpful to predict the prevalence of epidemic strains and to further study the mechanism of mutation causing severe disease.

Analysis of ‘Nursing Homes with Residents Positive for COVID-19, April - June 2020’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/8e9967f5-6ac9-44f7-8687-02fd436c3316 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Nursing homes with residents positive for COVID-19 from 4/22/2020 to 6/19/2020. Starting in July 2020, this dataset will no longer be updated and will be replaced by the CMS COVID-19 Nursing Home Dataset, available at the following link: https://data.ct.gov/Health-and-Human-Services/CMS-COVID-19-Nursing-Home-Dataset/w8wc-65i5.

Methods: 1) Laboratory-confirmed case counts are based upon data reported via the FLIS web portal. Nursing homes were asked to provide cumulative totals of residents with laboratory confirmed covid. This includes residents currently in-house, in the hospital, or who are deceased. Residents were excluded if they tested positive prior to initial admission to the nursing home. 2) The cumulative number of deaths among nursing home residents is based upon data reported by the Office of the Chief Medical Examiner. For public health surveillance, COVID-19-associated deaths include persons who tested positive for COVID-19 around the time of death (laboratory-confirmed) and persons whose death certificate lists COVID-19 disease as a cause of death or a significant condition contributing to death (probable).

Limitations: 1) As of the week of 5/10/20, Point Prevalence Survey testing is being offered to all asymptomatic nursing home residents to inform infection prevention efforts. Point prevalence surveys will be conducted over a period of several weeks. Some nursing homes had adequate testing resources available to conduct surveys prior to this date. Differences in survey timing will impact the number of positive results that a nursing home reports. 2) Cumulative totals of residents testing positive are being collected rather than individual resident data. Thus we cannot verify the counts, de-duplicate, and/or verify whether there is a record of a positive lab test. This may result in either under- or over-counting. 3) The number of COVID-19 positive residents and the number of confirmed deaths among residents are tabulated from different data sources. Due to the timing of availability of test results for deceased residents, it is not appropriate to calculate the percent of cases who died due to COVID-19 at any particular facility based upon this data. 4) The count of deaths reported for 4/14 are not included in this dataset, as they were not broken out by laboratory-confirmed or probable. They can be viewed in the DPH Report here: https://portal.ct.gov/-/media/Coronavirus/CTDPHCOVID19summary4162020.pdf?la=en

--- Original source retains full ownership of the source dataset ---

COVID-19 Antigen Self-Test Market Outlook

The global market size for COVID-19 antigen self-tests in 2023 was approximately USD 4.5 billion, and it is expected to soar to around USD 12.7 billion by 2032, growing at an impressive CAGR of 12.3% during the forecast period. This significant growth can be attributed to several factors, including the increasing demand for rapid and reliable testing methods, the continued prevalence of COVID-19 variants, and the growing recognition of the importance of regular self-testing in mitigating the spread of the virus.

One of the primary growth factors for the COVID-19 antigen self-test market is the escalating need for quick and accurate testing solutions. As the pandemic continues to evolve, there is an urgent requirement for rapid antigen tests that can deliver results within minutes, enabling individuals to make timely decisions about their health and safety. These tests are particularly valuable in settings where PCR testing is not readily accessible or where time is of the essence, such as before travel or attending large gatherings. The convenience and speed of antigen self-tests have made them a preferred choice for many people worldwide.

Another critical driver of market growth is the ongoing mutation of the SARS-CoV-2 virus, leading to the emergence of new variants. These variants may have different transmission characteristics and potentially escape immune responses, necessitating frequent testing to monitor and control their spread. Antigen self-tests offer a practical solution for detecting infections caused by these variants, allowing individuals to act promptly and reduce the risk of further transmission. This adaptability to evolving viral strains has enhanced the demand for antigen self-tests and contributed to their market expansion.

The increasing awareness and acceptance of self-testing as a proactive measure for managing public health have also played a significant role in the market's growth. Governments and health organizations worldwide have been advocating for regular self-testing to identify asymptomatic cases and prevent outbreaks. The widespread availability and affordability of antigen self-tests have empowered individuals to take responsibility for their health, leading to higher adoption rates. This shift towards self-testing has been instrumental in curbing the spread of COVID-19 and sustaining the demand for antigen self-tests.

Regionally, North America has been a significant market for COVID-19 antigen self-tests, driven by high testing rates and strong healthcare infrastructure. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, primarily due to the large population base, increasing awareness, and government initiatives promoting self-testing. The rapid urbanization and improving healthcare facilities in countries like India and China are anticipated to fuel the demand for antigen self-tests, contributing to the region's market expansion. Europe, Latin America, and the Middle East & Africa are also expected to see substantial growth, driven by similar factors such as government support, rising COVID-19 cases, and improving access to healthcare services.

Household COVID-19 Testing has emerged as a pivotal aspect of the self-test market, particularly as individuals seek to manage their health proactively within the comfort of their homes. The convenience of conducting tests at home without the need for medical supervision has empowered households to take charge of their health decisions. This trend is further supported by the increasing availability of user-friendly test kits that provide quick and reliable results. As households become more aware of the importance of regular testing, the demand for home-based COVID-19 testing solutions is expected to rise significantly. This shift not only aids in early detection and isolation of positive cases but also alleviates the burden on healthcare facilities, allowing them to focus resources on critical cases. As a result, household testing is playing a crucial role in curbing the spread of the virus and maintaining public health safety.

Product Type Analysis

The COVID-19 antigen self-test market can be broadly segmented by product type into rapid antigen test kits and home test kits. Rapid antigen test kits have gained significant traction due to their ability to provide quick results, often within 15 to 30 minutes. These kits are designed for ease of us

Every day, schools, child care centres and licensed home child care agencies report to the Ministry of Education on children, students and staff that have positive cases of COVID-19. If there is a discrepancy between numbers reported here and those reported publicly by a Public Health Unit, please consider the number reported by the Public Health Unit to be the most up-to-date. Schools and school boards report when a school is closed to the Ministry of Education. Data is current as of 2:00 pm the previous day. This dataset is subject to change. Data is only updated on weekdays excluding provincial holidays Effective June 15, 2022, board and school staff will not be expected to report student/staff absences and closures in the Absence Reporting Tool. The ministry will no longer report absence rates or school/child care closures on Ontario.ca for the remainder of the school year. Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak. ##Summary of school closures This is a summary of school closures in Ontario. Data includes: * Number of schools closed * Total number of schools * Percentage of schools closed ##School Absenteeism This report provides a summary of schools and school boards that have reported staff and student absences. Data includes: * School board * School * City or Town * Percentage of staff and students who are absent ##Summary of cases in schools This report provides a summary of COVID-19 activity in publicly-funded Ontario schools. Data includes: * School-related cases (total) * School-related student cases * School-related staff cases * Current number of schools with a reported case * Current number of schools closed Note: In some instances the type of cases are not identified due to privacy considerations. ##Schools with active COVID-19 cases This report lists schools and school boards that have active cases of COVID-19. Data includes : * School Board * School * Municipality * Confirmed Student Cases * Confirmed Staff Cases * Total Confirmed Cases ##Cases in school board partners This report lists confirmed active cases of COVID-19 for other school board partners (e.g. bus drivers, authorized health professionals etc.) and will group boards if there is a case that overlaps. Data includes : * School Board(s) * School Municipality * Confirmed cases – other school board partners ##Summary of targeted testing conducted in schools This data includes all tests that have been reported to the Ministry of Education since February 1, 2021. School boards and other testing partners will report to the Ministry every Wednesday based on data from the previous seven days. Data includes : * School boards or regions * Number of schools invited to participate in the last seven days * Total number of tests conducted in the last seven days * Cumulative number of tests conducted * Number of new cases identified in the last seven days * Cumulative number of cases identified ##Summary of asymptomatic testing at conducted in pharmacies: This is a summary of COVID-19 rapid antigen testing conducted at participating pharmacies in Ontario since March 27, 2021. * Total number of tests conducted in the last seven days * Cumulative number of tests conducted * Number of new cases identified in the last seven days * Cumulative number of cases identified

The coronavirus disease 2019 (COVID-19) pandemic has led to unprecedented global challenges. A zero-COVID strategy is needed to end the crisis, but there is a lack of biological evidence. In the present study, we collected available data on SARS, MERS, and COVID-19 to perform a comprehensive comparative analysis and visualization. The study results revealed that the fatality rate of COVID-19 is low, whereas its death toll is high compared to SARS and MERS. Moreover, COVID-19 had a higher asymptomatic rate. In particular, COVID-19 exhibited unique asymptomatic transmissibility. Further, we developed a foolproof operating software in Python language to simulate COVID-19 spread in Wuhan, showing that the cumulative cases of existing asymptomatic spread would be over 100 times higher than that of only symptomatic spread. This confirmed the essential role of asymptomatic transmissibility in the uncontrolled global spread of COVID-19, which enables the necessity of implementing the zero-COVID policy. In conclusion, we revealed the triggering role of the asymptomatic transmissibility of COVID-19 in this unprecedented global crisis, which offers support to the zero-COVID strategy against the recurring COVID-19 spread.

SARS-CoV2 a new emerging Corona Virus Disease in humans, which called for containment measures by many countries. The current paper aims to discuss the impact of two different sampling methodologies when executing a drive through COVID-19 survey on the quality of estimated disease burden measures. Secondary data analysis of a pilot cross-sectional survey targeting Qatar's primary health care registered population was done. Two groups with different sampling methods were compared for estimating COVID-19 point prevalence using molecular testing for nasopharyngeal swabs. The first group is a stratified random sample non-proportional to size (N = 260). A total of 16 population strata based on age group, gender, and nationality were sampled. The second group is the Open invitation group (N = 841). The results showed that the two groups were obviously and significantly different in age and nationality. Besides, reporting of COVID-19 symptoms was more frequent in the open invitation group (28.2%) than the random sample (16.2%). The open invitation group overestimated the symptomatic COVID-19 prevalence rate by more than four times, while it overestimated the asymptomatic COVID-19 cases by a small margin. The overall prevalence rate of active COVID-19 cases in the open invitation sample (13.3%) was almost double that of the random sample (6.9%). Furthermore, using population sampling weights reduced the prevalence rate to 0.8%. The lesson learned here is that it is wise to consider the magnitude of bias introduced in a surveillance system when relying on convenient sampling approaches in response to time constraints.