The evolution and interpretation of seroprevalence of SARS-CoV-2 antibodies among South African blood donors from the Beta to Omicron variant-driven waves

BACKGROUND AND OBJECTIVES

Confirmed COVID-19 diagnoses underestimate the total number of infections. Blood donors can provide representative seroprevalence estimates, which can be leveraged into reasonable estimates of total infection counts and infection fatality rate (IFR).

MATERIALS AND METHODS

Blood donors who donated after each of three epidemic waves (Beta, Delta and first Omicron waves) were tested for anti-SARS-CoV-2 nucleocapsid antibodies using the Roche Elecsys anti-SARS-CoV-2 total immunoglobulin assay. Roche Elecsys anti-spike antibody testing was done for the post-Omicron sampling. Prevalence of antibodies was estimated by age, sex, race and province and compared to official case reporting. Province and age group-specific IFRs were estimated using external excess mortality estimates.

RESULTS

The nationally weighted anti-nucleocapsid seroprevalence estimates after the Beta, Delta and Omicron waves were 47% (46.2%-48.6%), 71% (68.8%-73.5%) and 87% (85.5%-88.4%), respectively. There was no variation by age and sex, but there were statistically and epidemiologically significant differences by province (except at the latest time point) and race. There was a 13-fold higher seroprevalence than confirmed case counts at the first time point. Age-dependent IFR roughly doubled for every 10 years of age increase over 6 decades from 0.014% in children to 6.793% in octogenarians.

CONCLUSION

Discrepancies were found between seroprevalence and confirmed case counts. High seroprevalence rates found among Black African donors can be ascribed to historical inequities. Our IFR estimates were useful in refining previous large disagreements about the severity of the epidemic in South Africa. Blood donor-based serosurveys provided a valuable and efficient way to provide near real-time monitoring of the ongoing SARS-CoV-2 outbreak.

SARS-CoV-2 antibody dynamics in blood donors and COVID-19 epidemiology in eight Brazilian state capitals: A serial cross-sectional study

Background

The COVID-19 situation in Brazil is complex due to large differences in the shape and size of regional epidemics. Understanding these patterns is crucial to understand future outbreaks of SARS-CoV-2 or other respiratory pathogens in the country.

Methods

We tested 97,950 blood donation samples for IgG antibodies from March 2020 to March 2021 in 8 of Brazil's most populous cities. Residential postal codes were used to obtain representative samples. Weekly age- and sex-specific seroprevalence were estimated by correcting the crude seroprevalence by test sensitivity, specificity, and antibody waning.

Results

The inferred attack rate of SARS-CoV-2 in December 2020, before the Gamma variant of concern (VOC) was dominant, ranged from 19.3% (95% credible interval [CrI] 17.5-21.2%) in Curitiba to 75.0% (95% CrI 70.8-80.3%) in Manaus. Seroprevalence was consistently smaller in women and donors older than 55 years. The age-specific infection fatality rate (IFR) differed between cities and consistently increased with age. The infection hospitalisation rate increased significantly during the Gamma-dominated second wave in Manaus, suggesting increased morbidity of the Gamma VOC compared to previous variants circulating in Manaus. The higher disease penetrance associated with the health system's collapse increased the overall IFR by a minimum factor of 2.91 (95% CrI 2.43-3.53).

Conclusions

These results highlight the utility of blood donor serosurveillance to track epidemic maturity and demonstrate demographic and spatial heterogeneity in SARS-CoV-2 spread.

Funding

This work was supported by Itaú Unibanco 'Todos pela Saude' program; FAPESP (grants 18/14389-0, 2019/21585-0); Wellcome Trust and Royal Society Sir Henry Dale Fellowship 204311/Z/16/Z; the Gates Foundation (INV- 034540 and INV-034652); REDS-IV-P (grant HHSN268201100007I); the UK Medical Research Council (MR/S0195/1, MR/V038109/1); CAPES; CNPq (304714/2018-6); Fundação Faculdade de Medicina; Programa Inova Fiocruz-CE/Funcap - Edital 01/2020 Number: FIO-0167-00065.01.00/20 SPU N°06531047/2020; JBS - Fazer o bem faz bem.

Extending the susceptible-exposed-infected-removed (SEIR) model to handle the false negative rate and symptom-based administration of COVID-19 diagnostic tests: SEIR-fansy

False negative rates of severe acute respiratory coronavirus 2 diagnostic tests, together with selection bias due to prioritized testing can result in inaccurate modeling of COVID-19 transmission dynamics based on reported "case" counts. We propose an extension of the widely used Susceptible-Exposed-Infected-Removed (SEIR) model that accounts for misclassification error and selection bias, and derive an analytic expression for the basic reproduction number R 0 as a function of false negative rates of the diagnostic tests and selection probabilities for getting tested. Analyzing data from the first two waves of the pandemic in India, we show that correcting for misclassification and selection leads to more accurate prediction in a test sample. We provide estimates of undetected infections and deaths between April 1, 2020 and August 31, 2021. At the end of the first wave in India, the estimated under-reporting factor for cases was at 11.1 (95% CI: 10.7,11.5) and for deaths at 3.58 (95% CI: 3.5,3.66) as of February 1, 2021, while they change to 19.2 (95% CI: 17.9, 19.9) and 4.55 (95% CI: 4.32, 4.68) as of July 1, 2021. Equivalently, 9.0% (95% CI: 8.7%, 9.3%) and 5.2% (95% CI: 5.0%, 5.6%) of total estimated infections were reported on these two dates, while 27.9% (95% CI: 27.3%, 28.6%) and 22% (95% CI: 21.4%, 23.1%) of estimated total deaths were reported. Extensive simulation studies demonstrate the effect of misclassification and selection on estimation of R 0 and prediction of future infections. A R-package SEIRfansy is developed for broader dissemination.

The risk of COVID-19 death is much greater and age dependent with type I IFN autoantibodies

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection fatality rate (IFR) doubles with every 5 y of age from childhood onward. Circulating autoantibodies neutralizing IFN-α, IFN-ω, and/or IFN-β are found in ∼20% of deceased patients across age groups, and in ∼1% of individuals aged <70 y and in >4% of those >70 y old in the general population. With a sample of 1,261 unvaccinated deceased patients and 34,159 individuals of the general population sampled before the pandemic, we estimated both IFR and relative risk of death (RRD) across age groups for individuals carrying autoantibodies neutralizing type I IFNs, relative to noncarriers. The RRD associated with any combination of autoantibodies was higher in subjects under 70 y old. For autoantibodies neutralizing IFN-α2 or IFN-ω, the RRDs were 17.0 (95% CI: 11.7 to 24.7) and 5.8 (4.5 to 7.4) for individuals <70 y and ≥70 y old, respectively, whereas, for autoantibodies neutralizing both molecules, the RRDs were 188.3 (44.8 to 774.4) and 7.2 (5.0 to 10.3), respectively. In contrast, IFRs increased with age, ranging from 0.17% (0.12 to 0.31) for individuals <40 y old to 26.7% (20.3 to 35.2) for those ≥80 y old for autoantibodies neutralizing IFN-α2 or IFN-ω, and from 0.84% (0.31 to 8.28) to 40.5% (27.82 to 61.20) for autoantibodies neutralizing both. Autoantibodies against type I IFNs increase IFRs, and are associated with high RRDs, especially when neutralizing both IFN-α2 and IFN-ω. Remarkably, IFRs increase with age, whereas RRDs decrease with age. Autoimmunity to type I IFNs is a strong and common predictor of COVID-19 death.

SARS-CoV-2 infection fatality rate after the first epidemic wave in Mexico

Background

Estimates of SARS-CoV-2 infection fatality rates (IFRs) in developing countries remain poorly characterized. Mexico has one of the highest reported COVID-19 case-fatality rates worldwide, although available estimates do not consider serologic assessment of prior exposure nor all SARS-CoV-2-related deaths. We aimed to estimate sex- and age-specific IFRs for SARS-CoV-2 in Mexico.

Methods

The total number of people in Mexico with evidence of prior SARS-CoV-2 infection was derived from National Survey of Health and Nutrition-COVID-19 (ENSANUT 2020 Covid-19)—a nationally representative serosurvey conducted from August to November 2020. COVID-19 mortality data matched to ENSANUT’s dates were retrieved from the death-certificate registry, which captures the majority of COVID-19 deaths in Mexico, and from the national surveillance system, which covers the subset of COVID-19 deaths that were identified by the health system and were confirmed through a positive polymerase chain reaction test. We analysed differences in IFRs by urbanization and region.

Results

The national SARS-CoV-2 IFR was 0.47% (95% CI 0.44, 0.50) using death certificates and 0.30% (95% CI 0.28, 0.33) using surveillance-based deaths. The IFR increased with age, being close to zero at age <30 years, but increasing to 1% at ages 50–59 years in men and 60–69 years in women, and being the highest at ≥80 years for men (5.88%) and women (6.23%). Across Mexico's nine regions, Mexico City (0.99%) had the highest and the Peninsula (0.26%) the lowest certificate-based IFRs. Metropolitan areas had higher certificate-based IFR (0.63%) than rural areas (0.17%).

Conclusion

After the first wave of the COVID-19 pandemic, the overall IFR in Mexico was comparable with those of European countries. The IFR in Mexico increased with age and was higher in men than in women. The variations in IFRs across regions and places of residence within the country suggest that structural factors related to population characteristics, pandemic containment and healthcare capabilities could have influenced lethality at the local level.

[Infection fatality rate (IFR) during the course of COVID-19 pandemic in Upper Silesia Metropolitan Area (Poland) in 2020]

BACKGROUND

During the course of COVID-19 pandemic, a wide range of scientific projects was implemented worldwide, including studies focusing on infection fatality rate (IFR). The value of IFR depends on the number of COVID-19 deaths in a population in a given period and the number of infected people in this population, usually provided by seropepidemiological studies (anti-SARS-CoV-2 IgG in the case of COVID-19). The objective of our study was to estimate IFR in the course of COVID-19 pandemic in 2020, in the general population of Upper Silesia Metropolitan Area (USMA).

MATERIAL AND METHODS

The seroepidemiological study was conducted in October-November 2020. Among randomly selected inhabitants of Katowice, Gliwice, and Sosnowiec (N = 1167), the presence of SARS-CoV-2 virus infection was assessed based on a positive IgG test result performed with the ELISA method. Data on deaths due to COVID-19 were obtained from the Registry Offices of each city. The infection fatality rate was calculated using the formula IFR (%) = [number of deaths/number of infected] × 100.

RESULTS

Results of our study showed the prevalence of infection at 11.4% (95% CI: 9.5-13.2). In three examined towns, in the period January-November 2020, there was a total of 516 COVID-19 deaths. The resulting crude IFR was 0.65% (95% CI: 0.56-0.78). The IgG test had 88% sensitivity and 99% specificity and these figures were used to adjust IFR. The adjusted IFR value was similar to the crude value: IFR = 0.62% (95% CI: 0.53-0.74).

CONCLUSIONS

The value of IFR estimated for the USMA population was similar to average values obtained in other countries and can be used as the background for monitoring the course and impact of COVID-19 pandemic in the Upper Silesian Industrial Area. Med Pr. 2021;72(6):671-6.

The Second Wave of COVID-19 in South and Southeast Asia and the Effects of Vaccination

Background: By February 2021, the overall impact of coronavirus disease 2019 (COVID-19) in South and Southeast Asia was relatively mild. Surprisingly, in early April 2021, the second wave significantly impacted the population and garnered widespread international attention. Methods: This study focused on the nine countries with the highest cumulative deaths from the disease as of August 17, 2021. We look at COVID-19 transmission dynamics in South and Southeast Asia using the reported death data, which fits a mathematical model with a time-varying transmission rate. Results: We estimated the transmission rate, infection fatality rate (IFR), infection attack rate (IAR), and the effects of vaccination in the nine countries in South and Southeast Asia. Our study suggested that the IAR is still low in most countries, and increased vaccination is required to prevent future waves. Conclusion: Implementing non-pharmacological interventions (NPIs) could have helped South and Southeast Asia keep COVID-19 under control in 2020, as demonstrated in our estimated low-transmission rate. We believe that the emergence of the new Delta variant, social unrest, and migrant workers could have triggered the second wave of COVID-19.

Estimation of SARS-CoV-2 infection fatality rate by age and comorbidity status using antibody screening of blood donors during the COVID-19 epidemic in Denmark

Background

Studies presenting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection fatality rate (IFR) for healthy individuals are warranted. We estimate IFR by age and comorbidity status using data from a large serosurvey among Danish blood donors and nationwide data on coronavirus disease 2019 (COVID-19) mortality.

Methods

Danish blood donors aged 17–69 years donating blood October 2020–February 2021 were tested with a commercial SARS-CoV-2 total antibody assay. IFR was estimated for weeks 11 to 42, 2020 and week 43, 2020 to week 6, 2021, representing the first 2 waves of COVID-19 epidemic in Denmark.

Results

In total, 84944 blood donors were tested for antibodies. The seroprevalence was 2% in October 2020 and 7% in February 2021. Among 3898039 Danish residents aged 17–69 years, 249 deaths were recorded. The IFR was low for people <51 years without comorbidity during the 2 waves (combined IFR=3.36 per 100000 infections). The IFR was below 3‰ for people aged 61–69 years without comorbidity. IFR increased with age and comorbidity but declined from the first to second wave.

Conclusions

In this nationwide study, the IFR was very low among people <51 years without comorbidity.

Trends in seroprevalence of SARS-CoV-2 and infection fatality rate in the Norwegian population through the first year of the COVID-19 pandemic

Background

Infection with the novel coronavirus SARS‐CoV‐2 induces antibodies that can be used as a proxy for COVID‐19. We present a repeated nationwide cross‐sectional study assessing the seroprevalence of SARS‐CoV‐2, the infection fatality rate (IFR), and infection hospitalization rate (IHR) during the first year of the pandemic in Norway.

Methods

Residual serum samples were solicited in April/May 2020 (Round 1), in July/August 2020 (Round 2) and in January 2021 (Round 3). Antibodies against SARS‐CoV‐2 were measured using a flow cytometer‐based assay. Aggregate data on confirmed cases, COVID‐19‐associated deaths and hospitalizations were obtained from the Emergency preparedness registry for COVID‐19 (Beredt C19), and the seroprevalence estimates were used to estimate IFR and IHR.

Results

Antibodies against SARS‐CoV‐2 were measured in 4840 samples. The estimated seroprevalence increased from 0.8% (95% credible interval [CrI] 0.4%–1.3%) after the first wave of the pandemic (Rounds 1 and 2 combined) to 3.2% (95% CrI 2.3%–4.2%) (Round 3). The IFR and IHR were higher in the first wave than in the second wave and increased with age. The IFR was 0.2% (95% CrI 0.1%–0.3%), and IHR was 0.9% (95% CrI 0.6%–1.5%) for the second wave.

Conclusions

The seroprevalence estimates show a cumulative increase of SARS‐CoV‐2 infections over time in the Norwegian population and suggest some under‐recording of confirmed cases. The IFR and IHR were low, corresponding to the relatively low number of COVID‐19‐associated deaths and hospitalizations in Norway. Most of the Norwegian population was still susceptible to SARS‐CoV‐2 infection after the first year of the pandemic.

COVID-19 lockdown - Blessing or disaster?

Despite the dubious results of the nationwide lockdown in India state after state continue to clamp lockdowns indiscriminately and claim that it is a panacea for all their ills. Unverified claims abound and myths are perpetuated without any basis. It is time to take a close look at the hard data and come to logical conclusions regarding the utility of prolonged open-ended lockdowns. Unfortunately, the evidence does not support the use of prolonged lockdowns as a useful strategy to combat the COVID-19 pandemic.

High Infection Fatality Rate Among Elderly and Risk Factors Associated With Infection Fatality Rate and Asymptomatic Infections of COVID-19 Cases in Hong Kong

Background: Since the emergence in December 2019, the COVID-19 pandemic has become one of the greatest global public health threats in history. However, asymptomatic infections have increased the challenges of providing accurate estimates for the infection fatality rate (IFR) of COVID-19. Methods: We calculated the asymptomatic case ratios based on the reported COVID-19 cases in Hong Kong where intensive testing has been conducted in close contacts and high-risk populations. We estimated the IFR using both symptomatic and asymptomatic cases as denominator. The boosted regression tree (BRT) and multivariable logistic regression models were used to identify relative contribution and effect size of the risk factors associated with the asymptomatic cases and IFRs. Results: The ratio of the asymptomatic patients in Hong Kong was higher than many other regions over the world. Imported cases had a higher asymptomatic proportion than local cases. Older age and male were associated with a higher IFR than younger age and females. Conclusion: Policymakers should consider the potential risk factors for the asymptomatic infections and IFRs by the Hong Kong surveillance data to mitigate the diseases and reduce the case mortality of COVID-19.

COVID-19 antibody seroprevalence in Santa Clara County, California

BACKGROUND

Measuring the seroprevalence of antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is central to understanding infection risk and fatality rates. We studied Coronavirus Disease 2019 (COVID-19)-antibody seroprevalence in a community sample drawn from Santa Clara County.

METHODS

On 3 and 4 April 2020, we tested 3328 county residents for immunoglobulin G (IgG) and immunoglobulin M (IgM) antibodies to SARS-CoV-2 using a rapid lateral-flow assay (Premier Biotech). Participants were recruited using advertisements that were targeted to reach county residents that matched the county population by gender, race/ethnicity and zip code of residence. We estimate weights to match our sample to the county by zip, age, sex and race/ethnicity. We report the weighted and unweighted prevalence of antibodies to SARS-CoV-2. We adjust for test-performance characteristics by combining data from 18 independent test-kit assessments: 14 for specificity and 4 for sensitivity.

RESULTS

The raw prevalence of antibodies in our sample was 1.5% [exact binomial 95% confidence interval (CI) 1.1-2.0%]. Test-performance specificity in our data was 99.5% (95% CI 99.2-99.7%) and sensitivity was 82.8% (95% CI 76.0-88.4%). The unweighted prevalence adjusted for test-performance characteristics was 1.2% (95% CI 0.7-1.8%). After weighting for population demographics, the prevalence was 2.8% (95% CI 1.3-4.2%), using bootstrap to estimate confidence bounds. These prevalence point estimates imply that 53 000 [95% CI 26 000 to 82 000 using weighted prevalence; 23 000 (95% CI 14 000-35 000) using unweighted prevalence] people were infected in Santa Clara County by late March-many more than the ∼1200 confirmed cases at the time.

CONCLUSION

The estimated prevalence of SARS-CoV-2 antibodies in Santa Clara County implies that COVID-19 was likely more widespread than indicated by the number of cases in late March, 2020. At the time, low-burden contexts such as Santa Clara County were far from herd-immunity thresholds.

COVID-19 antibody seroprevalence in Santa Clara County, California

BACKGROUND

Measuring the seroprevalence of antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is central to understanding infection risk and fatality rates. We studied Coronavirus Disease 2019 (COVID-19)-antibody seroprevalence in a community sample drawn from Santa Clara County.

METHODS

On 3 and 4 April 2020, we tested 3328 county residents for immunoglobulin G (IgG) and immunoglobulin M (IgM) antibodies to SARS-CoV-2 using a rapid lateral-flow assay (Premier Biotech). Participants were recruited using advertisements that were targeted to reach county residents that matched the county population by gender, race/ethnicity and zip code of residence. We estimate weights to match our sample to the county by zip, age, sex and race/ethnicity. We report the weighted and unweighted prevalence of antibodies to SARS-CoV-2. We adjust for test-performance characteristics by combining data from 18 independent test-kit assessments: 14 for specificity and 4 for sensitivity.

RESULTS

The raw prevalence of antibodies in our sample was 1.5% [exact binomial 95% confidence interval (CI) 1.1-2.0%]. Test-performance specificity in our data was 99.5% (95% CI 99.2-99.7%) and sensitivity was 82.8% (95% CI 76.0-88.4%). The unweighted prevalence adjusted for test-performance characteristics was 1.2% (95% CI 0.7-1.8%). After weighting for population demographics, the prevalence was 2.8% (95% CI 1.3-4.2%), using bootstrap to estimate confidence bounds. These prevalence point estimates imply that 53 000 [95% CI 26 000 to 82 000 using weighted prevalence; 23 000 (95% CI 14 000-35 000) using unweighted prevalence] people were infected in Santa Clara County by late March-many more than the ∼1200 confirmed cases at the time.

CONCLUSION

The estimated prevalence of SARS-CoV-2 antibodies in Santa Clara County implies that COVID-19 was likely more widespread than indicated by the number of cases in late March, 2020. At the time, low-burden contexts such as Santa Clara County were far from herd-immunity thresholds.

COVID-19 antibody seroprevalence in Santa Clara County, California

BACKGROUND

Measuring the seroprevalence of antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is central to understanding infection risk and fatality rates. We studied Coronavirus Disease 2019 (COVID-19)-antibody seroprevalence in a community sample drawn from Santa Clara County.

METHODS

On 3 and 4 April 2020, we tested 3328 county residents for immunoglobulin G (IgG) and immunoglobulin M (IgM) antibodies to SARS-CoV-2 using a rapid lateral-flow assay (Premier Biotech). Participants were recruited using advertisements that were targeted to reach county residents that matched the county population by gender, race/ethnicity and zip code of residence. We estimate weights to match our sample to the county by zip, age, sex and race/ethnicity. We report the weighted and unweighted prevalence of antibodies to SARS-CoV-2. We adjust for test-performance characteristics by combining data from 18 independent test-kit assessments: 14 for specificity and 4 for sensitivity.

RESULTS

The raw prevalence of antibodies in our sample was 1.5% [exact binomial 95% confidence interval (CI) 1.1-2.0%]. Test-performance specificity in our data was 99.5% (95% CI 99.2-99.7%) and sensitivity was 82.8% (95% CI 76.0-88.4%). The unweighted prevalence adjusted for test-performance characteristics was 1.2% (95% CI 0.7-1.8%). After weighting for population demographics, the prevalence was 2.8% (95% CI 1.3-4.2%), using bootstrap to estimate confidence bounds. These prevalence point estimates imply that 53 000 [95% CI 26 000 to 82 000 using weighted prevalence; 23 000 (95% CI 14 000-35 000) using unweighted prevalence] people were infected in Santa Clara County by late March-many more than the ∼1200 confirmed cases at the time.

CONCLUSION

The estimated prevalence of SARS-CoV-2 antibodies in Santa Clara County implies that COVID-19 was likely more widespread than indicated by the number of cases in late March, 2020. At the time, low-burden contexts such as Santa Clara County were far from herd-immunity thresholds.

Reconciling estimates of global spread and infection fatality rates of COVID-19: An overview of systematic evaluations

BACKGROUND

Estimates of community spread and infection fatality rate (IFR) of COVID-19 have varied across studies. Efforts to synthesize the evidence reach seemingly discrepant conclusions.

METHODS

Systematic evaluations of seroprevalence studies that had no restrictions based on country and which estimated either total number of people infected and/or aggregate IFRs were identified. Information was extracted and compared on eligibility criteria, searches, amount of evidence included, corrections/adjustments of seroprevalence and death counts, quantitative syntheses and handling of heterogeneity, main estimates and global representativeness.

RESULTS

Six systematic evaluations were eligible. Each combined data from 10 to 338 studies (9-50 countries), because of different eligibility criteria. Two evaluations had some overt flaws in data, violations of stated eligibility criteria and biased eligibility criteria (eg excluding studies with few deaths) that consistently inflated IFR estimates. Perusal of quantitative synthesis methods also exhibited several challenges and biases. Global representativeness was low with 78%-100% of the evidence coming from Europe or the Americas; the two most problematic evaluations considered only one study from other continents. Allowing for these caveats, four evaluations largely agreed in their main final estimates for global spread of the pandemic and the other two evaluations would also agree after correcting overt flaws and biases.

CONCLUSIONS

All systematic evaluations of seroprevalence data converge that SARS-CoV-2 infection is widely spread globally. Acknowledging residual uncertainties, the available evidence suggests average global IFR of ~0.15% and ~1.5-2.0 billion infections by February 2021 with substantial differences in IFR and in infection spread across continents, countries and locations.

Geographic and demographic heterogeneity of SARS-CoV-2 diagnostic testing in Illinois, USA, March to December 2020

Background

Availability of SARS-CoV-2 testing in the United States (U.S.) has fluctuated through the course of the COVID-19 pandemic, including in the U.S. state of Illinois. Despite substantial ramp-up in test volume, access to SARS-CoV-2 testing remains limited, heterogeneous, and insufficient to control spread.

Methods

We compared SARS-CoV-2 testing rates across geographic regions, over time, and by demographic characteristics (i.e., age and racial/ethnic groups) in Illinois during March through December 2020. We compared age-matched case fatality ratios and infection fatality ratios through time to estimate the fraction of SARS-CoV-2 infections that have been detected through diagnostic testing.

Results

By the end of 2020, initial geographic differences in testing rates had closed substantially. Case fatality ratios were higher in non-Hispanic Black and Hispanic/Latino populations in Illinois relative to non-Hispanic White populations, suggesting that tests were insufficient to accurately capture the true burden of COVID-19 disease in the minority populations during the initial epidemic wave. While testing disparities decreased during 2020, Hispanic/Latino populations consistently remained the least tested at 1.87 tests per 1000 population per day compared with 2.58 and 2.87 for non-Hispanic Black and non-Hispanic White populations, respectively, at the end of 2020. Despite a large expansion in testing since the beginning of the first wave of the epidemic, we estimated that over half (50-80%) of all SARS-CoV-2 infections were not detected by diagnostic testing and continued to evade surveillance.

Conclusions

Systematic methods for identifying relatively under-tested geographic regions and demographic groups may enable policymakers to regularly monitor and evaluate the shifting landscape of diagnostic testing, allowing officials to prioritize allocation of testing resources to reduce disparities in COVID-19 burden and eventually reduce SARS-CoV-2 transmission.

Estimating Unreported COVID-19 Cases with a Time-Varying SIR Regression Model

BACKGROUND

Potential unreported infection might impair and mislead policymaking for COVID-19, and the contemporary spread of COVID-19 varies in different counties of the United States. It is necessary to estimate the cases that might be underestimated based on county-level data, to take better countermeasures against COVID-19. We suggested taking time-varying Susceptible-Infected-Recovered (SIR) models with unreported infection rates (UIR) to estimate factual COVID-19 cases in the United States.

METHODS

Both the SIR model integrated with unreported infection rates (SIRu) of fixed-time effect and SIRu with time-varying parameters (tvSIRu) were applied to estimate and compare the values of transmission rate (TR), UIR, and infection fatality rate (IFR) based on US county-level COVID-19 data.

RESULTS

Based on the US county-level COVID-19 data from 22 January (T1) to 20 August (T212) in 2020, SIRu was first tested and verified by Ordinary Least Squares (OLS) regression. Further regression of SIRu at the county-level showed that the average values of TR, UIR, and IFR were 0.034%, 19.5%, and 0.51% respectively. The ranges of TR, UIR, and IFR for all states ranged from 0.007-0.157 (mean = 0.048), 7.31-185.6 (mean = 38.89), and 0.04-2.22% (mean = 0.22%). Among the time-varying TR equations, the power function showed better fitness, which indicated a decline in TR decreasing from 227.58 (T1) to 0.022 (T212). The general equation of tvSIRu showed that both the UIR and IFR were gradually increasing, wherein, the estimated value of UIR was 9.1 (95%CI 5.7-14.0) and IFR was 0.70% (95%CI 0.52-0.95%) at T212.

INTERPRETATION

Despite the declining trend in TR and IFR, the UIR of COVID-19 in the United States is still on the rise, which, it was assumed would decrease with sufficient tests or improved countersues. The US medical system might be largely affected by severe cases amidst a rapid spread of COVID-19.

Jobs, Housing, and Mask Wearing: Cross-Sectional Study of Risk Factors for COVID-19

BACKGROUND

Many studies have focused on the characteristics of symptomatic patients with COVID-19 and clinical risk factors. This study reports the prevalence of COVID-19 in an asymptomatic population of a hospital service area (HSA) and identifies factors that affect exposure to the virus.

OBJECTIVE

The aim of this study is to measure the prevalence of COVID-19 in an HSA, identify factors that may increase or decrease the risk of infection, and analyze factors that increase the number of daily contacts.

METHODS

This study surveyed 1694 patients between April 30 and May 13, 2020, about their work and living situations, income, behavior, sociodemographic characteristics, and prepandemic health characteristics. This data was linked to testing data for 454 of these patients, including polymerase chain reaction test results and two different serologic assays. Positivity rate was used to calculate approximate prevalence, hospitalization rate, and infection fatality rate (IFR). Survey data was used to analyze risk factors, including the number of contacts reported by study participants. The data was also used to identify factors increasing the number of daily contacts, such as mask wearing and living environment.

RESULTS

We found a positivity rate of 2.2%, a hospitalization rate of 1.2%, and an adjusted IFR of 0.55%. A higher number of daily contacts with adults and older adults increases the probability of becoming infected. Occupation, living in an apartment versus a house, and wearing a face mask outside work increased the number of daily contacts.

CONCLUSIONS

Studying prevalence in an asymptomatic population revealed estimates of unreported COVID-19 cases. Occupational, living situation, and behavioral data about COVID-19-protective behaviors such as wearing a mask may aid in the identification of nonclinical factors affecting the number of daily contacts, which may increase SARS-CoV-2 exposure.

Global perspective of COVID-19 epidemiology for a full-cycle pandemic

As of October 2020, there are >1 million documented deaths with COVID-19. Excess deaths can be caused by both COVID-19 and the measures taken. COVID-19 shows extremely strong risk stratification across age, socioeconomic factors, and clinical factors. Calculation of years-of-life-lost from COVID-19 is methodologically challenging and can yield misleading over-estimates. Many early deaths may have been due to suboptimal management, malfunctional health systems, hydroxychloroquine, sending COVID-19 patients to nursing homes, and nosocomial infections; such deaths are partially avoidable moving forward. About 10% of the global population may be infected by October 2020. Global infection fatality rate is 0.15-0.20% (0.03-0.04% in those <70 years), with large variability across locations with different age-structure, institutionalization rates, socioeconomic inequalities, population-level clinical risk profile, public health measures, and health care. There is debate on whether at least 60% of the global population must be infected for herd immunity, or, conversely, mixing heterogeneity and pre-existing cross-immunity may allow substantially lower thresholds. Simulations are presented with a total of 1.58-8.76 million COVID-19 deaths over 5-years (1/2020-12/2024) globally (0.5-2.9% of total global deaths). The most favorable figures in that range would be feasible if high risk groups can be preferentially protected with lower infection rates than the remaining population. Death toll may also be further affected by potential availability of effective vaccines and treatments, optimal management and measures taken, COVID-19 interplay with influenza and other health problems, reinfection potential, and any chronic COVID-19 consequences. Targeted, precise management of the pandemic and avoiding past mistakes would help minimize mortality.

SARS-CoV-2 infection fatality rate among elderly retired Danish blood donors - A cross-sectional study

BACKGROUND

Despite the vast majority of individuals succumbing to infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are elderly, infection fatality rate (IFR) estimates for the age group 70 years older are still scarce. To this end we assessed SARS-CoV-2 seroprevalence among retired blood donors and combined it with national COVID-19 survey data to provide reliable population-based IFR estimates for this age group.

METHODS

We identified 60,926 retired blood donors age 70 years or older in the rosters of three region-wide Danish blood banks and invited them to fill in a questionnaire on COVID-19 related symptoms and behaviours. Among 24,861 (40.8%) responders, we invited a random sample of 3,200 individuals for blood testing. Overall, 1,201 (37.5%) individuals were tested for SARS-CoV-2 antibodies (Wantai) and compared to 1,110 active blood donors age 17-69 years. Seroprevalence 95% confidence intervals (CI) were adjusted for assay sensitivity and specificity.

RESULTS

Among retired (age 70 years or older) and active (age 17-69 years) blood donors, adjusted seroprevalences were 1.4% (95% CI: 0.3%-2.5%) and 2.5% (95% CI: 1.3%-3.8%), respectively. Using available population data on COVID-19 related fatalities, IFRs for patients age 70 years or older and for 17-69 years were estimated at 5.4% (95% CI: 2.7%-6.4%) and 0.083% (95% CI: 0.054%-0.18%), respectively. Only 52.4% of SARS-CoV-2 seropositive retired blood donors reported having been sick since the start of the pandemic.

CONCLUSION

COVID-19 IFR in the age group above 69 years is estimated to be 65 times as high as the IFR for people age 18-69 years.

EXTENDING THE SUSCEPTIBLE-EXPOSED-INFECTED-REMOVED(SEIR) MODEL TO HANDLE THE HIGH FALSE NEGATIVE RATE AND SYMPTOM-BASED ADMINISTRATION OF COVID-19 DIAGNOSTIC TESTS: SEIR-fansy

The false negative rate of the diagnostic RT-PCR test for SARS-CoV-2 has been reported to be substantially high. Due to limited availability of testing, only a non-random subset of the population can get tested. Hence, the reported test counts are subject to a large degree of selection bias. We consider an extension of the Susceptible-Exposed-Infected-Removed (SEIR) model under both selection bias and misclassification. We derive closed form expression for the basic reproduction number under such data anomalies using the next generation matrix method. We conduct extensive simulation studies to quantify the effect of misclassification and selection on the resultant estimation and prediction of future case counts. Finally we apply the methods to reported case-death-recovery count data from India, a nation with more than 5 million cases reported over the last seven months. We show that correcting for misclassification and selection can lead to more accurate prediction of case-counts (and death counts) using the observed data as a beta tester. The model also provides an estimate of undetected infections and thus an under-reporting factor. For India, the estimated under-reporting factor for cases is around 21 and for deaths is around 6. We develop an R-package (SEIRfansy) for broader dissemination of the methods.

Estimating actual COVID-19 case numbers using cumulative death count-A method of measuring effectiveness of lockdown of non-essential activities: a South African case study

Introduction

Estimating the number of SARS-CoV-2 infected individuals at any specific time point is always a challenge due to asymptomatic cases, the incubation period and testing delays. Here we use an empirical analysis of cumulative death count, transmission-to-death time lag, and infection fatality rate (IFR) to evaluate and estimate the actual cases at a specific time point as a strategy of tracking the spread of COVID-19.

Methods

This method mainly uses death count, as COVID-19 related deaths are arguably more reliably reported than infection case numbers. Using an IFR estimate of 0.66%, we back-calculate the number of cases that would result in the cumulative number of deaths at a given time point in South Africa between 27 February and 14 April. We added the mean incubation period (6.4 days) and the onset-to-death time lag (17.8 days) to identify the estimated time lag between transmission and death (25 days, rounded up). We use the statistical programming language R to analyze the data and produce plots.

Results

We estimate 28,182 cases as of 14 April, compared with 3,465 reported cases. Weekly growth rate of actual cases dropped immediately after lockdown implementation and has remained steady, measuring at 51.2% as of 14 April. The timing of drop in growth rate suggests that South Africa’s infection prevention strategy may have been effective at reducing viral transmission.

Conclusion

Estimating the actual number of cases at a specific time point can support evidence-based policies to reduce and prevent the spread of COVID-19. Non-reported, asymptomatic, hard to reach and, mild cases are possible sources of outbreaks that could emerge after lockdown. Therefore, close monitoring, optimized screening strategy and prompt response to COVID-19 could help in stopping the spread of the virus.

COVID-19 Infection Fatality Rate Associated with Incidence-A Population-Level Analysis of 19 Spanish Autonomous Communities

Previous studies have found large variations in the COVID-19 infection fatality rate (IFR). This study hypothesized that IFR would be influenced by COVID-19 epidemic intensity. We tested the association between epidemic intensity and IFR using serological results from a recent large SARS-CoV-2 serosurvey (N = 60,983) in 19 Spanish regions. The infection fatality rate for Spain as a whole was 1.15% and varied between 0.13% and 3.25% in the regions (median 1.07%, IQR 0.69-1.32%). The IFR by region was positively associated with SARS-CoV-2 seroprevalence (rho = 0.54; p = 0.0162), cases/100,000 (rho = 0.75; p = 0.002), hospitalizations/100,000 (rho = 0.78; p = 0.0001), mortality/100,000 (rho = 0.77; p = 0.0001) and case fatality rate (rho = 0.49; p = 0.0327). These results suggest that the SARS-CoV-2 IFR is not fixed. The Spanish regions with more rapid and extensive spread of SARS-CoV-2 had higher IFRs. These findings are compatible with the theory that slowing the spread of COVID-19 down reduces the IFR and case fatality rate via preventing hospitals from being overrun, and thus allowing better and lifesaving care.

Public Health Lessons Learned From Biases in Coronavirus Mortality Overestimation

In testimony before US Congress on March 11, 2020, members of the House Oversight and Reform Committee were informed that estimated mortality for the novel coronavirus was 10-times higher than for seasonal influenza. Additional evidence, however, suggests the validity of this estimation could benefit from vetting for biases and miscalculations. The main objective of this article is to critically appraise the coronavirus mortality estimation presented to Congress. Informational texts from the World Health Organization and the Centers for Disease Control and Prevention are compared with coronavirus mortality calculations in Congressional testimony. Results of this critical appraisal reveal information bias and selection bias in coronavirus mortality overestimation, most likely caused by misclassifying an influenza infection fatality rate as a case fatality rate. Public health lessons learned for future infectious disease pandemics include: safeguarding against research biases that may underestimate or overestimate an associated risk of disease and mortality; reassessing the ethics of fear-based public health campaigns; and providing full public disclosure of adverse effects from severe mitigation measures to contain viral transmission.