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The SARS-CoV-2 B.1.1.7 variant of concern (VOC) is increasing in prevalence across Europe. Accurate estimation of disease severity associated with this VOC is critical for pandemic planning. We found increased risk of death for VOC compared with non-VOC cases in England (hazard ratio: 1.67; 95% confidence interval: 1.34-2.09; p < 0.0001). Absolute risk of death by 28 days increased with age and comorbidities. This VOC has potential to spread faster with higher mortality than the pandemic to date.

IntroductionCoronavirus disease 2019 (COVID-19) has caused more than 690 million deaths worldwide. Different results concerning the death rates of the Delta and Omicron variants have been recorded. We aimed to assess the secular trend of case fatality rate (CFR), identify risk factors associated with mortality following COVID-19 diagnosis, and investigate the risks of mortality and hospitalization during Delta and Omicron waves in the United States.MethodsThis study assessed 2,857,925 individuals diagnosed with COVID-19 in the United States from January 2020, to June 2022. The inclusion criterion was the presence of COVID-19 diagnostic codes in electronic medical record or a positive laboratory test of the SARS-CoV-2. Statistical analysis was bifurcated into two components, longitudinal analysis and comparative analysis. To assess the discrepancies in hospitalization and mortality rates for COVID-19, we identified the prevailing periods for the Delta and Omicron variants.ResultsLongitudinal analysis demonstrated four sharp surges in the number of deaths and CFR. The CFR was persistently higher in males and older age. The CFR of Black and White remained higher than Asians since January 2022. In comparative analysis, the adjusted hazard ratios for all-cause mortality and hospitalization were higher in Delta wave compared to the Omicron wave. Risk of all-cause mortality was found to be greater 14–30 days after a COVID-19 diagnosis, while the likelihood of hospitalization was higher in the first 14 days following a COVID-19 diagnosis in Delta wave compared with Omicron wave. Kaplan–Meier analysis revealed the cumulative probability of mortality was approximately 2-fold on day 30 in Delta than in Omicron cases (log-rank p

Objectives: To determine the case-fatality rate (CFR) of coronavirus disease 2019 (Covid-19) and its associated determinants in order to understand the true magnitude of the problem during the ongoing conflict in Yemen. Methods: The CFR among the confirmed Covid-19 cases in Yemen was calculated. The data was retrieved from national Covid-19 surveillance between April 10, when the first COVID-19 case reported, and May 31, 2020. Results: A total of 419 confirmed Covid-19 cases were reported. There were 14.1% and 5.7% of cases who required intensive care and mechanical ventilators, respectively. Out of the total cases, 95 deaths were reported, giving a CFR of 22.6% which is much higher compared to other countries. CFR was significantly higher among elderly people compared to young adults and varied between governorates. Mortality was associated with pre-existing hypertension (OR: 2.30; 95% CI: 1.58, 3.54) and diabetes (OR: 1.68; 95% CI: 1.08, 2.61). Conclusions: The elderly and those with comorbidities, in particular hypertension and diabetes, have higher risk for poor outcomes and therefore should receive more attention in the clinical setting. Preventive measures should also be prioritized to protect those groups in order to reduce the severe cases and deaths-associated COVID-19 in armed-conflict. [ABSTRACT FROM AUTHOR]

Background: The first case of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified in Wuhan, China, in late 2019, the epicenter of the COVID-19 outbreak. In India on January 30, 2020 and in Andhra Pradesh on March 12, 2020 first case was reported. The case fatality rate (CFR) among diagnosed cases is generally about 2%-3% but varies by country. Aim: To estimate the CFR in Andhra Pradesh during the pandemic in different phases. Methods: All the secondary data were collected either from the covid19india website or github website or Indian government websites or Andhra Pradesh State government website or media quoting the government announcements January 30, 2020 to August 8, 2020. The CFR was estimated as per the Methods for Estimating the Case Fatality Ratio for a Novel, Emerging Infectious Disease. Results: In the pre-lockdown phase, the CFR of Andhra Pradesh was 0.00%, Uttarandhra, Central Andhra & Rayalaseema was Nil, and South Andhra it was ranging from Nil-0.00%, In lockdown phase, the CFR Andhra Pradesh was ranging from 1.34 to 42.31%, Uttarandhra - Nil -5.56%, Central Andhra - Nil-100.00%, South Andhra - 0.00-66.67%, and Rayalaseema - Nil-100.00%. In post-lockdown phase, the CFR Andhra Pradesh was ranging from 0.99 to 2.50%, Uttarandhra - 0.59 to 4.55%, Central Andhra - 0.82 to 14.33%, South Andhra - 1.05 to 2.30%, and Rayalaseema - 0.37-8.07%. Conclusions: The CFR of confirmed cases of COVID-19 in Andhra Pradesh and its districts was a downward trend except Prakasam district. [ABSTRACT FROM AUTHOR]

This paper uses the Value of Statistical Life (VSL) literature to weigh the costs and benefits of non-pharmaceutical interventions of the U.S. COVID-19 stay-at-home orders that affected 92 percent of the U.S. workforce at their peak in April 2020. We calculate the prevaccine COVID-19 infection fatality rate to have been 0.85 percent. We find that the stay-at-home orders saved most likely about 71,000 lives and led to a net benefit to the United States of 1.7 percent of GDP after accounting for lives saved and drops in workforce participation. Through October 31, 2021, the VSL of U.S. lives lost to COVID-19 was over $8.4 trillion. [ABSTRACT FROM AUTHOR]

Background: The first case of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified in Wuhan, China, in late 2019, the epicenter of the COVID-19 outbreak. In India on January 30, 2020 and in Andhra Pradesh on March 12, 2020 first case was reported. The case fatality rate (CFR) among diagnosed cases is generally about 2%-3% but varies by country. Aim: To estimate the CFR in Andhra Pradesh during the pandemic in different phases. Methods: All the secondary data were collected either from the covid19india website or github website or Indian government websites or Andhra Pradesh State government website or media quoting the government announcements January 30, 2020 to August 8, 2020. The CFR was estimated as per the Methods for Estimating the Case Fatality Ratio for a Novel, Emerging Infectious Disease. Results: In the pre-lockdown phase, the CFR of Andhra Pradesh was 0.00%, Uttarandhra, Central Andhra & Rayalaseema was Nil, and South Andhra it was ranging from Nil-0.00%, In lockdown phase, the CFR Andhra Pradesh was ranging from 1.34 to 42.31%, Uttarandhra - Nil -5.56%, Central Andhra - Nil-100.00%, South Andhra - 0.00-66.67%, and Rayalaseema - Nil-100.00%. In post-lockdown phase, the CFR Andhra Pradesh was ranging from 0.99 to 2.50%, Uttarandhra - 0.59 to 4.55%, Central Andhra - 0.82 to 14.33%, South Andhra - 1.05 to 2.30%, and Rayalaseema - 0.37-8.07%. Conclusions: The CFR of confirmed cases of COVID-19 in Andhra Pradesh and its districts was a downward trend except Prakasam district. [ABSTRACT FROM AUTHOR]

Introduction: SARS-CoV-2 primarily affects a person's respiratory system and leads to the spread of pathogenicity in the person and therefore this study evaluated the Case Fatality Rate (CFR) and Basic Reproductive Rate (R-naught) of COVID-19. Method: This cross-sectional descriptive study was performed on all people with COVID-19 from the first date of admission of positive PCR patients with SARS-CoV-2 until September 21, 2020, in the area of Markazi Province in Khomein, Iran. Information on the records of definitively discharged and deceased patients due to COVID-19 (having positive PCR) was extracted from 20/2/2020 to 21/9/2020, and Case Fatality Rate (CFR) and Basic Reproductive Rate (R-naught) of COVID-19 were evaluated. The sample size was 691 people. Results: The CFR of the COVID-19 in the current study is 6.65%, and the rate of disease transmission among the sensitive population of Khomein city at the beginning of the study (March) averaged BASIC REPRODUCTIVE RATE=1.75, and in the last month (September) of the study This value was reduced to BASIC REPRODUCTIVE RATE=1.306. Conclusion: According to our study on Case Fatality Rate (CFR) and Basic Reproductive Rate (R-naught) of COVID-19, showed that the prevalence of the disease was high in March, which due to lack of proper observance of health protocols, we saw this problem and in the months when people have more problems. Hygiene was reduced, and the rate of transmission and mortality was reduced. [ABSTRACT FROM AUTHOR]

The paper presents a critical analysis of the European spread of the SARS-CoV-2 virus that causes the COVID-19 disease across 48 European countries and territories, including the Monaco and Andorra principalities and Vatican City. Simple cross-sectional regressions, using country populations, are able to predict quite accurately both the total number of cases and deaths, which cast doubt on measures aimed at controlling the disease via lockdowns. This throws into sharp contrast the relative effectiveness of the attempts to risk manage the spread of the virus by 'flattening the curve' of the speed of transmission, and the efficacy of lockdowns in terms of the spread of the disease and death rates. The algorithmic techniques, results and analysis presented in the paper should prove useful to the medical and health professions, science advisers and risk management and decision making of healthcare by state, regional and national governments in all countries in Europe. [ABSTRACT FROM AUTHOR]

This paper presents a novel analysis of the global spread of the SARS-CoV-2 virus that causes the COVID-19 disease using the R package "nCov2019", with an emphasis on the global spread and forecasts of the disease, and the rate of transmission in individual countries at two different points in time, namely, March and September 2020. This throws in sharp relief the relative effectiveness of the attempts to risk manage the spread of the virus by "flattening the curve" (aka planking the curve) of the speed of transmission, and the efficacy of lockdowns in terms of the spread of the disease and death rates. Simple cross-sectional regressions are able to predict quite well both the total number of cases and deaths, which cast doubt on the above measures. The algorithmic techniques, results and analysis presented in the paper should prove useful to the medical and health professions, science advisers and risk management and decision making of healthcare by state, regional and national governments in all countries. [ABSTRACT FROM AUTHOR]

Patients with coronavirus disease 2019 (COVID-19) who have underlying malignancy have a higher mortality rate compared with those without cancer, although the magnitude of such excess risk is not clearly defined. We performed a systematic review and pooled analysis to provide precise estimates of the mortality rate among patients with both cancer and COVID-19. A systematic literature search involving peer-reviewed publications, preprints and conference proceedings up to July 16, 2020, was performed. The primary end-point was the case fatality rate (CFR), defined as the rate of death among patients with cancer and COVID-19. The CFR was assessed with a random effects model, which was used to derive a pooled CFR and its 95% confidence interval (CI). Fifty-two studies, involving a total of 18,650 patients with both COVID-19 and cancer, were selected for the pooled analysis. A total of 4243 deaths were recorded in this population. The probability of death was 25.6% (95% CI: 22.0%–29.5%; I2 = 48.9%) in this patient population. Patients with cancer who develop COVID-19 have high probability of mortality. Appropriate and aggressive preventive measures must be taken to reduce the risk of COVID-19 in patients with cancer and to optimally manage those who do contract the infection. • Patients with both cancer and coronavirus disease 2019 (COVID-19) have a higher mortality than reported in the overall COVID-19-positive population. • We aim to provide precise mortality estimates among patients with cancer and COVID-19. • Pooled case mortality rate was 25.6% (95% CI: 22.0%–29.5%; I2 = 48.9%). • Aggressive measures must be taken to reduce risk of infection in patients with cancer. [ABSTRACT FROM AUTHOR]

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Since December 2019, when the first case of coronavirus disease (COVID-19) was identified in the city of Wuhan in the Hubei Province of China, the epidemic has generated tens of thousands of cases throughout China. As of February 28, 2020, the cumulative number of reported deaths in China was 2,858. We estimated the time-delay adjusted risk for death from COVID-19 in Wuhan, as well as for China excluding Wuhan, to assess the severity of the epidemic in the country. Our estimates of the risk for death in Wuhan reached values as high as 12% in the epicenter of the epidemic and ≈1% in other, more mildly affected areas. The elevated death risk estimates are probably associated with a breakdown of the healthcare system, indicating that enhanced public health interventions, including social distancing and movement restrictions, should be implemented to bring the COVID-19 epidemic under control. [ABSTRACT FROM AUTHOR]

[Display omitted] • Malnutrition, pre-existing childhood morbidities are risk factors of COVID-19 among under-five children. • Poor household environmental conditions aggravate susceptibility to COVID-19 among under-five children. • Identified the states and union territories which have in the high-risk and low-risk zone of COVID-19 in India. The novel Coronavirus disease 2019 (2019-nCoV) outbreak, caused by severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2), has become the worst serious global risk to humanity in the last century and linked with various risk factors. To find out the risk zone associated with Coronavirus disease among children under-five age using malnourished status, pre-existing morbidity conditions, poor household environmental conditions, and also with case fatality rate (CFR) and active case rate (ACR) of COVID-19 in India. Data was collected from the 4th round of the National Family Health Survey (NFHS)-4, 2015–16, and CFR and ACR of COVID-19 related data collected from the Ministry of Health and Family Welfare (MoHFW) on 18th May 2020. Mean, standard deviation, and Z-score statistical methods have been employed to identify the risk factors zone and Hot Spot analysis (Getis-Ord Gi) has been done. The states and union territories (UTs) which have a high composite vulnerability score (CVS) of COVID-19 among under-five children are in Meghalaya (CVS = 1), Uttar Pradesh (CVS = 0.93), Jharkhand (CVS = 0.86), Bihar (CVS = 0.74), Madhya Pradesh (CVS = 0.74), and Odisha (CVS = 0.55). The states and UTs which have low composite vulnerability score of COVID-19 among under-five children are in Sikkim (CVS = -0.90), Daman & Diu (CVS = -0.76) Lakshadweep (CVS = -0.74), Kerala (CVS = -0.72), Chandigarh (CVS = -0.71). The COVID-19 high-risk zones (hot spot: 99% Confidence interval [CI]) were observed in Madhya Pradesh, Uttar Pradesh, Jharkhand, Bihar, and Meghalaya states of India, which are spatially high clustered and the low-risk zones (cold spot: 95% CI) were observed in Kerala, Mizoram states of India. Well-built public health measures, including rapidly searching in high focus areas and testing of COVID-19, should be performed in vulnerable regions of COVID-19. [ABSTRACT FROM AUTHOR]