Studying the Spread of COVID-19 and Its Impact on E-learning: From a Deep Learning Perspective

COVID-19 is a respiratory infectious disease caused by a recently discovered Coronavirus. Since December 2019 and as of October 8, 2020, about 36.6 million (36,625,199) confirmed cases of COVID-19 have been registered globally by the WHO, with more than 1 million (1,063,780) deaths. This paper investigates statistically the spread of COVID-19 disease, which became a killer pandemic in Saudi Arabia. We demonstrate that the low apparent Case Fatality Ratio (CFR) (i.e., mortality rate) observed in Saudi Arabia, as compared with other countries, is strongly proportional to the number of infection cases. To present an effective statistical analysis of the end of COVID-19 pandemic, the researchers used the present evaluation of the Infection Fatality Ratio (IFR) of the COVID-19 reported until September 2020, depending on the reported CFR obtained from the Ministry of Health. The proposed analysis shows more realistic evaluations of the actual range of the deceased as well as more precise factors of how rapidly the infection spreads. The study demonstrates the more powerful elements causing the seriousness of the COVID-19 in Saudi Arabia. Finally, the researchers use the mortality number collected through the last seven months to predict both the overall number of infections and the period in which the infection will end in the Kingdom of Saudi Arabia. The researchers presented the effect of the spread of the COVID-19 pandemic in the E-learning sector in the KKU and BUE universities and the period in which the infection will end. Deep learning (DL) is a potentially powerful artificial intelligence (AI) tool in the fight against the COVID-19 pandemic. This paper also addressed this issue and answered the question: can deep learning technology be used to early screen COVID-19 patients from their computed tomography (CT) images and what is the accuracy of this diagnostic tool. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

All-cause and cause-specific mortality in Kilifi Health and Demographic Surveillance System, in Kenya in the period of the COVID-19 pandemic

Introduction/ Background: COVID-19 impact on all-cause mortality in tropical Africa remains unknown. In Kenya, there were 3,000 COVID-19-attributabledeaths by May 2021. We used the Kilifi Health and Demographic Surveillance System (KHDSS) to monitor mortality among 300,000 residents in rural Kenya during the pandemic and investigated excess mortality. Methods: Using a negative binomial model, accounting for seasonality and trend, we fitted mortality data from 2010-2019 and predicted mortality in April 2020-May 2021. Excess mortality was calculated as [(observedexpected mortality)/expected mortality]-1. We examined the impact of the pandemic on 8 leading causes of death using Verbal Autopsy (VA). Finally, we calculated the anticipated number of COVID-19 deaths in KHDSS, in 10 age strata, as the product of the number of KHDSS residents, KHDSS seroprevalence of SARS-CoV-2 (see impact) and infection fatality ratios (IFR) from a meta-analysis of 28 populations, largely in Europe and America. Results: We observed 1424 deaths between April 2020-May 2021. Based on 2010-19 mortality, we predicted 1510 deaths (excess mortality -5.7%, 95% CI -9.8%, 1.9%). Mortality was significantly lower among children <5 years old (-26.2% 95% CI -33.5, -14.9%). By VA, there were fewer deaths attributable to acute respiratory infections in 2020, compared to 2010-19, in all age groups. External IFRs predicted 327 (95% CI 265-403) COVID-19-attributable deaths, which would represent an excess mortality of 22%. Impact: The impact of COVID-19 on all-cause mortality cannot be assessed without simultaneous evidence of COVID-19 transmission in the same population. A random sample survey of 850 KHDSS residents during December 2020-May 2021 has already reported seroprevalence of anti-SARS-CoV-2 IgG as 12% in children and 26% in adults, suggesting widespread transmission. Conclusion: The lack of mortality impact in Kilifi could be explained either by a compensatory reduction in all non-COVID-19 causes of death or by a substantially lower age-specific risk of death among individuals infected with SARS-CoV-2 in Kenya compared to Europe or America.

Phase Dynamics of the Covid-19 Pandemic. A Systematic Analysis of 213 Countries and Territories. Report 2.

The study was carried out to evaluate the dynamics of monthly numbers of cases, deaths, tests and case fatality ratio worldwide during three phases of the COVID-19 pandemic. Material and methods: Twenty-three sets of databases, dated the 22nd of each month from January 2020 to November 2021, for 213 countries were collected from the Worldometer website. The number of cases, deaths, tests, case fatality ratio, infection fatality ratio, etc. were counted for various periods of time for each of the 213 countries, then the results related to different periods of time were compared. The analysis of main epidemiological parameters resulted in division of three phases of the global pandemic evolution. The first phase (23.01.20-22.07.20), the second phase (23.07.20-22.01.21) and the third phase (23.01.21-22.07.21) were different in terms of the number of tests performed, new cases and mortality due to COVID-19. By the end of second phase, the worldwide statistics indicated end of the pandemic, but the third phase was characterized by sudden rise in number of new cases and deaths. The most dramatic evolution of epidemic curve occurred in the countries where physicians had successfully confronted COVID-19 during the first two phases of the pandemic. Despite the decrease in the overall numbers deaths during the latest months analyzed, additional study is necessary to identify causes of new cases and deaths during the third phase of the pandemic. It can be suggested that preventive and therapeutic protocols should be changed from the 'standard' to 'personalized' types.

The phase dynamics of the COVID-19 pandemic: a systematic analysis of 213 Countries and Territories.

The study was carried out to evaluate the dynamics of monthly numbers of cases, deaths, tests and case fatality ratio worldwide during three phases of the COVID-19 pandemic. Material and methods: Twenty-three sets of databases, dated the 22nd of each month from January 2020 to November 2021, for 213 countries were collected from the Worldometer website. The number of cases, deaths, tests, case fatality ratio, infection fatality ratio, etc. were counted for various periods of time for each of the 213 countries, then the results related to different periods of time were compared. The analysis of main epidemiological parameters resulted in division of three phases of the global pandemic evolution. The first phase (23.01.20-22.07.20), the second phase (23.07.20-22.01.21) and the third phase (23.01.21-22.07.21) were different in terms of the number of tests performed, new cases and mortality due to COVID-19. By the end of second phase, the worldwide statistics indicated imminent end of the pandemic, but the third phase was characterized by sudden rise in the number of new cases and deaths that could not be explained rationally. The most dramatic evolution of epidemic curve occurred in the countries where physicians had successfully confronted COVID-19 during the first two phases of the pandemic. Despite the decrease in the overall numbers deaths during the latest months analyzed, additional study is necessary to identify the cause of increasing in the number of new cases and deaths during the third phase of the pandemic. Presumably, there are several causes of negative evolution of the current pandemic, including over-reliance on polymerase chain reaction tests, application of non-specialized premises for quarantine and treatment, non-professional management, following therapeutic protocols applied in countries with high number of deaths, ignoring preventive treatment, and decreasing in mass and individual immunity. It can be suggested that the use of drugs modulating T-cell immunity is necessary, and preventive and therapeutic protocols should be changed from the 'standard' to 'personalized' types.

Time Trend in SARS-CoV-2 Seropositivity, Surveillance Detection- and Infection Fatality Ratio until Spring 2021 in the Tirschenreuth County-Results from a Population-Based Longitudinal Study in Germany.

Herein, we provide results from a prospective population-based longitudinal follow-up (FU) SARS-CoV-2 serosurveillance study in Tirschenreuth, the county which was hit hardest in Germany in spring 2020 and early 2021. Of 4203 individuals aged 14 years or older enrolled at baseline (BL, June 2020), 3546 participated at FU1 (November 2020) and 3391 at FU2 (April 2021). Key metrics comprising standardized seroprevalence, surveillance detection ratio (SDR), infection fatality ratio (IFR) and success of the vaccination campaign were derived using the Roche N- and S-Elecsys anti-SARS-CoV-2 test together with a self-administered questionnaire. N-seropositivity at BL was 9.2% (1st wave). While we observed a low new seropositivity between BL and FU1 (0.9%), the combined 2nd and 3rd wave accounted for 6.1% new N-seropositives between FU1 and FU2 (ever seropositives at FU2: 15.4%). The SDR decreased from 5.4 (BL) to 1.1 (FU2) highlighting the success of massively increased testing in the population. The IFR based on a combination of serology and registration data resulted in 3.3% between November 2020 and April 2021 compared to 2.3% until June 2020. Although IFRs were consistently higher at FU2 compared to BL across age-groups, highest among individuals aged 70+ (18.3% versus 10.7%, respectively), observed differences were within statistical uncertainty bounds. While municipalities with senior care homes showed a higher IFR at BL (3.0% with senior care home vs. 0.7% w/o), this effect diminished at FU2 (3.4% vs. 2.9%). In April 2021 (FU2), vaccination rate in the elderly was high (>77.4%, age-group 80+).

Reassessing Reported Deaths and Estimated Infection Attack Rate during the First 6 Months of the COVID-19 Epidemic, Delhi, India.

India reported >10 million coronavirus disease (COVID-19) cases and 149,000 deaths in 2020. To reassess reported deaths and estimate incidence rates during the first 6 months of the epidemic, we used a severe acute respiratory syndrome coronavirus 2 transmission model fit to data from 3 serosurveys in Delhi and time-series documentation of reported deaths. We estimated 48.7% (95% credible interval 22.1%-76.8%) cumulative infection in the population through the end of September 2020. Using an age-adjusted overall infection fatality ratio based on age-specific estimates from mostly high-income countries, we estimated that just 15.0% (95% credible interval 9.3%-34.0%) of COVID-19 deaths had been reported, indicating either substantial underreporting or lower age-specific infection-fatality ratios in India than in high-income countries. Despite the estimated high attack rate, additional epidemic waves occurred in late 2020 and April-May 2021. Future dynamics will depend on the duration of natural and vaccine-induced immunity and their effectiveness against new variants.

COVID-19 mortality, excess mortality, deaths per million and infection fatality ratio, Belgium, 9 March 2020 to 28 June 2020.

BackgroundCOVID-19 mortality, excess mortality, deaths per million population (DPM), infection fatality ratio (IFR) and case fatality ratio (CFR) are reported and compared for many countries globally. These measures may appear objective, however, they should be interpreted with caution.AimWe examined reported COVID-19-related mortality in Belgium from 9 March 2020 to 28 June 2020, placing it against the background of excess mortality and compared the DPM and IFR between countries and within subgroups.MethodsThe relation between COVID-19-related mortality and excess mortality was evaluated by comparing COVID-19 mortality and the difference between observed and weekly average predictions of all-cause mortality. DPM were evaluated using demographic data of the Belgian population. The number of infections was estimated by a stochastic compartmental model. The IFR was estimated using a delay distribution between infection and death.ResultsIn the study period, 9,621 COVID-19-related deaths were reported, which is close to the excess mortality estimated using weekly averages (8,985 deaths). This translates to 837 DPM and an IFR of 1.5% in the general population. Both DPM and IFR increase with age and are substantially larger in the nursing home population.DiscussionDuring the first pandemic wave, Belgium had no discrepancy between COVID-19-related mortality and excess mortality. In light of this close agreement, it is useful to consider the DPM and IFR, which are both age, sex, and nursing home population-dependent. Comparison of COVID-19 mortality between countries should rather be based on excess mortality than on COVID-19-related mortality.

Epidemiology of COVID-19

On December 12, 2019, a number of cases emerged caused by an unidentified pneumonia disease outbreak in a local seafood market in Wuhan City, Hubei Province of China. Samples from five patients tested positive for coronaviruses, where 87.1% sequences were identical to the SARS-related coronaviruses. On January 8, 2020, the CSG of the ICTV named this virus SARS-CoV-2, the virus that causes COVID-19. The USA had its first COVID-19 case on January 21, 2020, in Washington State. The WHO declared COVID-19 a pandemic on March 11, 2020. Several biological and epidemiological characteristics of COVID-19 are presented: percent of asymptomatic infections, infection fatality ratio, case fatality rate, reproduction number, incubation period, latent period, and serial interval. Data are presented on the demographic overrepresentation of Blacks and Hispanics on COVID-19 deaths in 50 States and DC, as well as in 14 States with the largest Black and Hispanic populations, along with the top 5 States of residence of the Black population. Data also are offered on Blacks’ disproportional burden of COVID-19 deaths in selected counties in Florida and Georgia. It is worthy to note that, at least about 7 months into the pandemic, the USA had no strategic preparedness and response plan and persistently breached field epidemiology principles, prompting three prominent public health journals, Scientific American, The Lancet, and the New England Journal of Medicine, to deliver sweeping criticisms on the Trump Administration’s mishandling of COVID-19. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

SARS-CoV-2 Seroprevalence in a Rural and Urban Household Cohort during First and Second Waves of Infections, South Africa, July 2020-March 2021.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections may be underestimated because of limited access to testing. We measured SARS-CoV-2 seroprevalence in South Africa every 2 months during July 2020-March 2021 in randomly selected household cohorts in 2 communities. We compared seroprevalence to reported laboratory-confirmed infections, hospitalizations, and deaths to calculate infection-case, infection-hospitalization, and infection-fatality ratios in 2 waves of infection. Post-second wave seroprevalence ranged from 18% in the rural community children <5 years of age, to 59% in urban community adults 35-59 years of age. The second wave saw a shift in age distribution of case-patients in the urban community (from persons 35-59 years of age to persons at the extremes of age), higher attack rates in the rural community, and a higher infection-fatality ratio in the urban community. Approximately 95% of SARS-CoV-2 infections were not reported to national surveillance.

Infection fatality ratio and case fatality ratio of COVID-19.

The infection fatality ratio (IFR) is the risk of death per infection and is one of the most important epidemiological parameters. Enormous efforts have been undertaken to estimate the IFR for COVID-19. This study examined the pros and cons of several approaches. It is found that the frequently used approaches using serological survey results as the denominator and the number of confirmed deaths as the numerator underestimated the true IFR. The most typical examples are South Africa and Peru (before official correction), where the confirmed deaths are one-third of the excess deaths. We argue that the RT-PCR-based case fatality ratio (CFR) is a reliable indicator of the lethality of COVID-19 in locations where testing is extensive. An accurate IFR is crucial for policymaking and public-risk perception.

Age- & sex-specific infection fatality ratios for COVID-19 estimated from two serially conducted community-based serosurveys, Chennai, India, 2020.

Background & objectives: Infection fatality ratio (IFR) is considered a more robust and reliable indicator than case fatality ratio for severity of SARS-CoV-2 infection. Age- and sex-stratified IFRs are crucial to guide public health response. Infections estimated through representative community-based serosurveys would gauge more accurate IFRs than through modelling studies. We describe age- and sex-stratified IFR for COVID-19 estimated through serosurveys conducted in Chennai, India. Methods: Two community-based serosurveys were conducted among individuals aged ≥10 yr during July and October 2020 in 51 of the 200 wards spread across 15 zones of Chennai. Total number of SARS-CoV-2 infections were estimated by multiplying the total population of the city aged ≥10 yr with the weighted seroprevalence and IFR was calculated by dividing the number of deaths with the estimated number of infections. Results: IFR was 17.3 [95% confidence interval (CI): 14.1-21.6] and 16.6 (95% CI: 13.8-20.2) deaths/10,000 infections during July and October 2020, respectively. Individuals aged 10-19 years had the lowest IFR [first serosurvey (R1): 0.2/10,000, 95% CI: 0.2-0.3 and second serosurvey (R2): 0.2/10,000, 95% CI: 0.1-0.2], and it increased with age and was highest among individuals aged above 60 yr (R1: 140.0/10,000, 95% CI: 107.0-183.8 and R2: 111.2/10,000, 95% CI: 89.2-142.0). Interpretation & conclusions: Our findings suggested that the IFR increased with age and was high among the elderly. Therefore, elderly population need to be prioritized for public health interventions including vaccination, frequent testing in long-term care facilities and old age homes, close clinical monitoring of the infected and promoting strict adherence to non-pharmaceutical interventions.

Estimates and Determinants of SARS-Cov-2 Seroprevalence and Infection Fatality Ratio Using Latent Class Analysis: The Population-Based Tirschenreuth Study in the Hardest-Hit German County in Spring 2020.

SARS-CoV-2 infection fatality ratios (IFR) remain controversially discussed with implications for political measures. The German county of Tirschenreuth suffered a severe SARS-CoV-2 outbreak in spring 2020, with particularly high case fatality ratio (CFR). To estimate seroprevalence, underreported infections, and IFR for the Tirschenreuth population aged ≥14 years in June/July 2020, we conducted a population-based study including home visits for the elderly, and analyzed 4203 participants for SARS-CoV-2 antibodies via three antibody tests. Latent class analysis yielded 8.6% standardized county-wide seroprevalence, a factor of underreported infections of 5.0, and 2.5% overall IFR. Seroprevalence was two-fold higher among medical workers and one third among current smokers with similar proportions of registered infections. While seroprevalence did not show an age-trend, the factor of underreported infections was 12.2 in the young versus 1.7 for ≥85-year-old. Age-specific IFRs were <0.5% below 60 years of age, 1.0% for age 60-69, and 13.2% for age 70+. Senior care homes accounted for 45% of COVID-19-related deaths, reflected by an IFR of 7.5% among individuals aged 70+ and an overall IFR of 1.4% when excluding senior care home residents from our computation. Our data underscore senior care home infections as key determinant of IFR additionally to age, insufficient targeted testing in the young, and the need for further investigations on behavioral or molecular causes of the fewer infections among current smokers.

Excess deaths associated with the Iranian COVID-19 epidemic: A province-level analysis.

OBJECTIVE: There has been no province-level data on the number of coronavirus disease 2019 (COVID-19)-related deaths in Iran since the start of the pandemic. This study was performed to estimate the number of COVID-19 deaths and population-level exposure per province using seasonal all-cause mortality data. METHODS: Time-series data were collected from the National Organization for Civil Registration on the seasonal all-cause mortality from spring 2015 to summer 2020 (from March 21, 2015 to September 21, 2020), in accordance with the Solar Hijri (SH) calendar, to estimate the expected number of seasonal deaths for each province using a piecewise linear regression model. A population-weighted infection fatality ratio was then applied to estimate the level of exposure per province during this period. RESULTS: From the start of winter to the end of summer (from December 22, 2019 to September 21, 2020), there were a total of 58 900 (95% confidence interval 46 900-69 500) excess deaths across all 31 provinces, with 27% (95% confidence interval 20-34%) estimated nationwide exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In particular, Qom and Golestan were among the hardest-hit provinces, with nearly 57% exposure, while another 27 provinces showed significant levels of excess mortality in at least one season with >20% population-level exposure to the virus. Unexpectedly high levels of excess mortality were also detected during fall 2019 (from September 23 to December 21, 2019) across 18 provinces, unrelated and prior to the start of the COVID-19 pandemic. CONCLUSIONS: This study quantified the pattern of spread of COVID-19 across the country and identified areas with the largest epidemic growth requiring the most immediate interventions.

Prevalence and Risk Factors of Infection in the Representative COVID-19 Cohort Munich.

Given the large number of mild or asymptomatic SARS-CoV-2 cases, only population-based studies can provide reliable estimates of the magnitude of the pandemic. We therefore aimed to assess the sero-prevalence of SARS-CoV-2 in the Munich general population after the first wave of the pandemic. For this purpose, we drew a representative sample of 2994 private households and invited household members 14 years and older to complete questionnaires and to provide blood samples. SARS-CoV-2 seropositivity was defined as Roche N pan-Ig ≥ 0.4218. We adjusted the prevalence for the sampling design, sensitivity, and specificity. We investigated risk factors for SARS-CoV-2 seropositivity and geospatial transmission patterns by generalized linear mixed models and permutation tests. Seropositivity for SARS-CoV-2-specific antibodies was 1.82% (95% confidence interval (CI) 1.28-2.37%) as compared to 0.46% PCR-positive cases officially registered in Munich. Loss of the sense of smell or taste was associated with seropositivity (odds ratio (OR) 47.4; 95% CI 7.2-307.0) and infections clustered within households. By this first population-based study on SARS-CoV-2 prevalence in a large German municipality not affected by a superspreading event, we could show that at least one in four cases in private households was reported and known to the health authorities. These results will help authorities to estimate the true burden of disease in the population and to take evidence-based decisions on public health measures.

Geographic Variation in Racial Disparities in Health and Coronavirus Disease-2019 (COVID-19) Mortality.

OBJECTIVE: To evaluate the race-stratified state-level prevalence of health determinants and the racial disparities in coronavirus disease 2019 (COVID-19) cumulative incidence and mortality in the United States. PATIENTS AND METHODS: The age-adjusted race-stratified prevalence of comorbidities (hypertension, diabetes, dyslipidemia, and obesity), preexisting medical conditions (pulmonary disease, heart disease, stroke, kidney disease, and malignant neoplasm), poor health behaviors (smoking, alcohol abuse, and physical inactivity), and adverse socioeconomic factors (education, household income, and health insurance) was computed in 435,139 American adult participants from the 2017 Behavioral Risk Factor Surveillance System survey. Correlation was assessed between health determinants and the race-stratified COVID-19 crude mortality rate and infection-fatality ratio computed from respective state public health departments in 47 states. RESULTS: Blacks had a higher prevalence of comorbidities (63.3%; 95% CI, 62.4% to 64.2% vs 55.1%; 95% CI, 54.7% to 55.5%) and adverse socioeconomic factors (47.0%; 95% CI, 46.0% to 47.9% vs 30.9%; 95% CI, 30.6% to 31.3%) than did whites. The prevalence of preexisting medical conditions was similar in blacks (30.4%; 95% CI, 28.8% to 32.1%) and whites (30.8%; 95% CI, 30.2% to 31.4%). The prevalence of poor health behaviors was higher in whites (57.2%; 95% CI, 56.3% to 58.0%) than in blacks (50.2%; 95% CI,46.2% to 54.2%). Comorbidities and adverse socioeconomic factors were highest in the southern region, and poor health behaviors were highest in the western region. The cumulative incidence rate (per 100,000 persons) was 3-fold higher in blacks (1546.4) than in whites (540.4). The crude mortality rate (per 100,000 persons) was 2-fold higher in blacks (83.2) than in whites (33.2). However, the infection-fatality ratio (per 100 cases) was similar in whites (6.2) and blacks (5.4). Within racial groups, the geographic distribution of health determinants did not correlate with the state-level COVID-19 mortality and infection-fatality ratio (P>.05 for all). CONCLUSION: Racial disparities in COVID-19 are largely driven by the higher cumulative incidence of infection in blacks. There is a discordance between the geographic dispersion of COVID-19 mortality and the regional distribution of health determinants.

SARS-CoV-2 Infections and COVID-19 Fatality: Estimation of Infection Fatality Ratio and Current Prevalence.

COVID-19 is one of the most important problems for public health, according to the number of deaths associated to this pathology reported so far. However, from the epidemiological point of view, the dimension of the problem is still unknown, since the number of actual cases of SARS-CoV-2 infected people is underestimated, due to limited testing. This paper aims at estimating the actual Infection Fatality Ratio (number of deaths with respect to the number of infected people) and the actual current prevalence (number of infected people with respect to the entire population), both in a specific population and all over the world. With this aim, this paper proposes a method to estimate Infection Fatality Ratio of a still ongoing infection, based on a daily estimation, and on the relationship between this estimation and the number of tests performed per death. The method has been applied using data about COVID-19 from Italy. Results show a fatality ratio of about 0.9%, which is lower than previous findings. The number of actual infected people in Italy is also estimated, and results show that (i) infection started at the end of January 2020; (ii) a maximum number of about 100,000 new cases in one day was reached at the beginning of March 2020; (iii) the estimated cumulative number of infections at the beginning of October 2020 is about 4.2 million cases in Italy (more than 120 million worldwide, if a generalization is conjectured as reasonable). Therefore, the prevalence at the beginning of October 2020 is estimated at about 6.9% in Italy (1.6% worldwide, if a generalization is conjectured).

Assessing the age specificity of infection fatality rates for COVID-19: systematic review, meta-analysis, and public policy implications.

Determine age-specific infection fatality rates for COVID-19 to inform public health policies and communications that help protect vulnerable age groups. Studies of COVID-19 prevalence were collected by conducting an online search of published articles, preprints, and government reports that were publicly disseminated prior to 18 September 2020. The systematic review encompassed 113 studies, of which 27 studies (covering 34 geographical locations) satisfied the inclusion criteria and were included in the meta-analysis. Age-specific IFRs were computed using the prevalence data in conjunction with reported fatalities 4 weeks after the midpoint date of the study, reflecting typical lags in fatalities and reporting. Meta-regression procedures in Stata were used to analyze the infection fatality rate (IFR) by age. Our analysis finds a exponential relationship between age and IFR for COVID-19. The estimated age-specific IFR is very low for children and younger adults (e.g., 0.002% at age 10 and 0.01% at age 25) but increases progressively to 0.4% at age 55, 1.4% at age 65, 4.6% at age 75, and 15% at age 85. Moreover, our results indicate that about 90% of the variation in population IFR across geographical locations reflects differences in the age composition of the population and the extent to which relatively vulnerable age groups were exposed to the virus. These results indicate that COVID-19 is hazardous not only for the elderly but also for middle-aged adults, for whom the infection fatality rate is two orders of magnitude greater than the annualized risk of a fatal automobile accident and far more dangerous than seasonal influenza. Moreover, the overall IFR for COVID-19 should not be viewed as a fixed parameter but as intrinsically linked to the age-specific pattern of infections. Consequently, public health measures to mitigate infections in older adults could substantially decrease total deaths.

SARS-CoV-2 Infections in the World: An Estimation of the Infected Population and a Measure of How Higher Detection Rates Save Lives.

This paper provides an estimation of the accumulated detection rates and the accumulated number of infected individuals by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Worldwide, on July 20, it has been estimated above 160 million individuals infected by SARS-CoV-2. Moreover, it is found that only about 1 out of 11 infected individuals are detected. In an information context in which population-based seroepidemiological studies are not frequently available, this study shows a parsimonious alternative to provide estimates of the number of SARS-CoV-2 infected individuals. By comparing our estimates with those provided by the population-based seroepidemiological ENE-COVID study in Spain, we confirm the utility of our approach. Then, using a cross-country regression, we investigated if differences in detection rates are associated with differences in the cumulative number of deaths. The hypothesis investigated in this study is that higher levels of detection of SARS-CoV-2 infections can reduce the risk exposure of the susceptible population with a relatively higher risk of death. Our results show that, on average, detecting 5 instead of 35 percent of the infections is associated with multiplying the number of deaths by a factor of about 6. Using this result, we estimated that 120 days after the pandemic outbreak, if the US would have tested with the same intensity as South Korea, about 85,000 out of their 126,000 reported deaths could have been avoided.

Age-specific SARS-CoV-2 infection fatality ratio and associated risk factors, Italy, February to April 2020.

We analysed 5,484 close contacts of coronavirus disease (COVID-19) cases in Italy, all tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Infection fatality ratio was 0.43% (95% confidence interval (CI): 0.21-0.79) for individuals younger than 70 years and 10.5% (95% CI: 8.0-13.6) for older individuals. Risk of death after infection was 62% lower (95% CI: 31-80) in clusters identified after 16 March 2020 and 1.8-fold higher for males (95% CI: 1.03-3.16).