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SARS-CoV-2 continues to widely circulate in populations globally. Underdetection is acknowledged and is problematic when attempting to capture the true prevalence. Seroprevalence studies, where blood samples from a population sample are tested for SARS-CoV-2 antibodies that react to the SARS-CoV-2 virus, are a common method for estimating the propo...
Contexts in source publication
Context 1
... magnitude of how far the estimated seroprevalence will be from the true prevalence depends not only on the test, but also on the true prevalence in the population. Table 1 shows the results obtained using the same test as before on a sample of 1000 people. If the true prevalence is 2%, a study like this gives an estimated prevalence of 7.5%, which is almost four times higher-an overestimation of this magnitude can lead us to very misleading conclusions. ...Context 2
... magnitude of how far the estimated seroprevalence will be from the true prevalence depends not only on the test, but also on the true prevalence in the population. Table 1 shows the results obtained using the same test as before on a sample of 1000 people. If the true prevalence is 2%, a study like this gives an estimated prevalence of 7.5%, which is almost four times higher-an overestimation of this magnitude can lead us to very misleading conclusions. ...Similar publications
Uncontrolled high transmission is driving the continuous evolution of SARS-CoV-2, leading to the nonstop emergence of the new variants with varying sensitivity to the neutralizing antibodies and vaccines. We have analysed of 8,82,740 SARS-CoV-2 genome sequences, collected and sequenced during late December 2019 to 25 March 2021 from all across the...
Citations
... It was estimated that non-random sampling is associated with a higher participation rate compared to random sampling [22]. However, we have acknowledged the higher risk of response bias in applying the non-random sampling method, as agreement to participate in a study could be influenced by several factors that in turn are related to the likelihood of previous exposure: socioeconomic status, trust in government or the medical profession, and many others [23]. Nevertheless, results from other studies suggest, that under the most penalizing scenarios of selection of participants, the population prevalence estimate changes minimally compared to non-random sampling [24]. ...
Objectives
Despite positive trends in SARS-CoV-2 epidemiology, seroprevalence surveys remain an important tool for estimating the magnitude of the COVID-19 pandemic. This study aimed to investigate the prevalence of IgG antibodies against SARS-CoV-2 nucleocapsid (N) and spike (S) proteins in a sample of the Lithuanian population (N = 517) and evaluate how the pattern of seropositivity correlates with the levels of SARS-CoV-2 infection and vaccination.
Methods
Study participants (aged 18–88 years) filled in the questionnaire self-reporting their demographic-social variables, health status, and SARS-CoV-2-related status. The anti-S and anti-N IgG levels were estimated using a microarray ELISA test.
Results
After several pandemic waves and vaccination campaign, the seroprevalence of SARS-CoV-2-specific IgG in the analyzed sample was 97.87 % by March–May 2023. We determined the 96.91 % prevalence of anti-S and 58.03 % prevalence of anti-N IgG. The majority of study participants (71.18 %) had hybrid immunity induced by vaccination and SARS-CoV-2 infection. 20.3 % of study participants were anti-N IgG positive without reporting any previous symptoms or a positive SARS-CoV-2 test. A decline of anti-N IgG positivity within 9 months after infection was observed.
Conclusions
This study demonstrates high total seroprevalence in March–May 2023 in all age groups indicating a widely established humoral immunity against SARS-CoV-2 in Lithuania.
... As such, there remains a significant difference between the observed (reported) number of cases and the 'true' number of cases. A population consists of infected and uninfected people, infected people may be symptomatic or not, and their probability of being detected depends heavily on symptoms that some approaches to case ascertainment such as sero-prevalence surveys [5] or modelling based on Infection Fatality Ratios [6] refer only to the broader population of symptomatic infections. Evidence from numerous sources cite the difficulties in identification of asymptomatic cases, delayed symptoms, rapidly evolving variants and strains, efficacy of contract tracing, inchoate health systems, inadequacies in serological testing, amongst others [7][8][9][10][11][12]. ...
Objective
We re-examined the reported number of COVID-19 cases in Australia and across its states during the first wave of the pandemic. We provided estimates of the total number of cases, adjusted for under-reporting.
Methods
Publicly available data sourced from Australian governments at federal, state and territory levels included records on cumulative confirmed COVID-19 cases and cumulative deaths occurring in Australia and across its states on a daily basis. Lower bound and upper bound estimates of the total number of COVID-19 cases in Australia and across its states, that included the undetected cases that have not been recorded, were estimated.
Results
On January 25, 2020, Australia recorded its first 4 cases of COVID-19 and the first death occurred on March 3, 2020. On April 1, 2020, 4864 cases had been reported with 21 deaths. Our estimation showed that on April 1, 2020, the minimum and maximum number of COVID-19 cases in Australia were in fact 10,160 (95 % CI: 9781–10,538) and 21,748 (95 % CI: 21,607–22,014) respectively. We estimated that the total number of cases were at least twice and at most four times the observed cases recorded. These differences were also found at the state level where in New South Wales there was a minimum and maximum of 207 and 447 cases in total for every 100 reported cases, while in Victoria these figures were much lower at 157 and 265 respectively for every 100 reported cases.
Conclusion
Case ascertainment during the pandemic is known to have been underestimated due to difficulties in testing and contact tracing, amongst others. Capture-recapture methods provided a measure of the gap between the official number of cases recorded and the actual number during the first wave of the pandemic.
... Coronaviruses pose a massive threat to the health and lives of people worldwide. However, the large number of asymptomatic individuals shown by COVID-19 symptomatology makes differentiating between COVID-19 positive and negative individuals a challenge [2]. At the same time, the severity of SARS-CoV-2 pneumonia puts considerable pressure on intensive care resources (ICU) in hospitals without adequate resources [3]. ...
Coronaviruses have affected the lives of people around the world. Increasingly, studies have indicated that the virus is mutating and becoming more contagious. Hence, the pressing priority is to swiftly and accurately predict patient outcomes. In addition, physicians and patients increasingly need interpretability when building machine models in healthcare. We propose an interpretable machine framework(KISM) that can diagnose and prognose patients based on blood test datasets. First, we use k -nearest neighbors, isolated forests, and SMOTE to pre-process the original blood test datasets. Seven machine learning tools Support Vector Machine, Extra Tree, Random Forest, Gradient Boosting Decision Tree, eXtreme Gradient Boosting, Logistic Regression, and ensemble learning were then used to diagnose and predict COVID-19. In addition, we used SHAP and scikit-learn post-hoc interpretability to report feature importance, allowing healthcare professionals and artificial intelligence models to interact to suggest biomarkers that some doctors may have missed. The 10-fold cross-validation of two public datasets shows that the performance of KISM is better than that of the current state-of-the-art methods. In the diagnostic COVID-19 task, an AUC value of 0.9869 and an accuracy of 0.9787 were obtained, and ultimately Leukocytes, platelets, and Proteina C reativa mg/dL were found to be the most indicative biomarkers for the diagnosis of COVID-19. An AUC value of 0.9949 and an accuracy of 0.9677 were obtained in the prognostic COVID-19 task and Age, LYMPH, and WBC were found to be the most indicative biomarkers for identifying the severity of the patient.
... This post-test probability can be computed according to Bayes' rule using estimates of the disease's frequency ("prior probability") and of the test's sensitivity and specificity ("likelihood"), but complexity arises when modeling biological and epidemiological features of the pair disease-test and estimating uncertainty (confidence intervals) to correctly reflect the amount of information at hand. In that sense, serosurveys that took place after the first wave of SARS-CoV-2 pandemic to quantify its extent were facilitated by convenient kinetic properties of biological response in an immunologically naive population [1]. Indeed, as symptoms can follow infection by a week (on average) and hospitalization follows symptoms by another week (if so), anti-spike immunoglobulin G (IgG) rises within a month after the infection and remain stable during several months (contrary to other immunoglobulins or to vaccine response) [2][3][4][5][6][7]. ...
COVID-19 serological tests with a "positive", "intermediate" or "negative" result according to predefined thresholds cannot be directly interpreted as a probability of having been infected with SARS-CoV-2. Based on 81,797 continuous anti-spike tests collected in France after the first wave, a Bayesian mixture model was developed to provide a tailored infection probability for each participant. Depending on the serological value and the context (age and administrative region), a negative or a positive test could correspond to a probability of infection as high as 61.9% or as low as 68.0%, respectively. In infected individuals, the model estimated a proportion of "non-responders" of 14.5% (95% CI, 11.2-18.1%), corresponding to a sub-group of persons who exhibited a weaker serological response to SARS-CoV-2. This model allows for an individual interpretation of serological results as a probability of infection, depending on the context and without any notion of threshold.
... with further information, such as confirmed cases, deaths and infectious disease models, to better understand the disease [31]. ...
Objective:
The aim of this work was to estimate the conditioned probability for the diagnosis of SARS-CoV-2 infection with reverse transcription polymerase chain reaction (RT-PCR), viral antigen rapid diagnostic tests (Ag-RDT), and antibody detection tests depending on the prevalence in the specific healthcare settings in Spain in 2020, and on the pre-test probability (PTP) according to the clinical situation, age and unknown or close contacts of the patient.
Methods:
Performance parameters of tests were obtained from literature. Prevalence data and PTP were obtained from Spanish sources and a survey, respectively. The post-test probability is the positive predictive value (PPV) when test is positive. For negative result, we also calculated the probability of having the infection (false negatives).
Results:
For both RT-PCR and viral Ag-RDT, the lowest PPV values were for the population screenings. This strategy proved to be useful in ruling out infection but generates a high number of false positives. At individual level, both tools provided high PPV (≥ 97%) when the PTP values are over 35%. In seroprevalence studies, though the specificity of IgG alone tests is high, under low seroprevalence, false positives cannot be avoided. Total antibodies tests are useful for diagnosis of COVID-19 in those doubtful cases with RT-PCR or Ag-RDT tests being repeatedly negative.
Conclusions:
The interpretating of results depends not only on the accuracy of the test, but also on the prevalence of the infection in different settings, and the PTP associated to the patient before performing the test.
... The extent and nature of the spread of SARS-CoV-2 in populations is often not fully understood as a significant proportion Extended author information available on the last page of the article of cases, particularly in Africa, are asymptomatic, and testing is limited in many low-to-middle-income countries [3][4][5]. Finding the true extent of SARS-CoV-2 spread is critical to provide actionable data about the epidemiological progress of the disease for researchers and policymakers [6]. ...
Introduction:
The true nature of the population spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in populations is often not fully known as most cases, particularly in Africa, are asymptomatic. Finding the true magnitude of SARS-CoV-2 spread is crucial to provide actionable data about the epidemiological progress of the disease for researchers and policymakers. This study developed and optimized an antibody enzyme-linked immunosorbent assay (ELISA) using recombinant nucleocapsid antigen expressed in-house using a simple bacterial expression system.
Methods:
Nucleocapsid protein from SARS-CoV-2 was expressed and purified from Escherichia coli. Plasma samples used for the assay development were obtained from Ghanaian SARS-CoV-2 seropositive individuals during the pandemic, while seronegative controls were plasma samples collected from blood donors before the coronavirus disease 2019 (COVID-19) pandemic. Another set of seronegative controls was collected during the COVID-19 pandemic. Antibody detection and levels within the samples were validated using commercial kits and Luminex. Analyses were performed using GraphPad Prism, and the sensitivity, specificity and background cut-off were calculated.
Results and discussion:
This low-cost ELISA (£0.96/test) assay has a high prediction of 98.9%, and sensitivity and specificity of 97% and 99%, respectively. The assay was subsequently used to screen plasma from SARS-CoV-2 RT-PCR-positive Ghanaians. The assay showed no significant difference in nucleocapsid antibody levels between symptomatic and asymptomatic, with an increase of the levels over time. This is in line with our previous publication.
Conclusion:
This study developed a low-cost and transferable assay that enables highly sensitive and specific detection of human anti-SARS-CoV-2 IgG antibodies. This assay can be modified to include additional antigens and used for continuous monitoring of sero-exposure to SARS-CoV-2 in West Africa.
... giving so-called point prevalence [13]. Seroprevalence, a related concept, looks at the proportion of individuals in the population have antibodies for a specific disease, for example, SARS-CoV-2 [14]. Sensitivity, denoted Se, sometimes also called the true positive fraction (TPF), is the probability of having a positive test result, given that the subject has the disease, P r(Y = 1|D = 1) [2]. ...
Background: The COVID-19 has led to many studies of seroprevalence. A number of methods exist in the statistical literature to correctly estimate disease prevalence in the presence of diagnostic testmisclassification, but these methods seem to be less known and notroutinely used in the public health literature. We aimed to show how widespread the problem is in recent publications, and to quantify the magnitude of bias introduced when correct methods are not used.
Methods: We examined a sample of recent literature to determine how often public health researcher did not account for test performance in estimates of seroprevalence. Using straightforward calculations, we estimated the amount of bias introduced when reporting the proportion of positive test results instead of using sensitivity and specificity to estimate disease prevalence.
Results: Of the seroprevalence studies sampled, 80% failed to account for sensitivity and specificity. Expected bias is often more than is desired in practice, ranging from 1% to 10%.
Conclusions: Researchers conducting studies of prevalence should correctly account for test sensitivity and specificity in their statistical analysis.
... First, quantifying the total number of infections is usually done using serosurveys. Accurate IFR estimates require relatively unbiased serosurveys that sample the full population at risk of infection [13]. Second, deaths due to COVID-19 occur within a wide time period after initial infection, (on average, 20.2 days (95% CI 8.0-50.0 ...
The ongoing COVID-19 pandemic has killed at least 1.1 million people in the United States and over 6.7 million globally. Accurately estimating the age-specific infection fatality rate (IFR) of SARS-CoV-2 for different populations is crucial for assessing and understanding the impact of COVID-19 and for appropriately allocating vaccines and treatments to at-risk groups. We estimated age-specific IFRs of wild-type SARS-CoV-2 using published seroprevalence, case, and death data from New York City (NYC) from March to May 2020, using a Bayesian framework that accounted for delays between key epidemiological events. IFRs increased 3-4-fold with every 20 years of age, from 0.06% in individuals between 18-45 years old to 4.7% in individuals over 75. We then compared IFRs in NYC to several city- and country-wide estimates including England, Switzerland (Geneva), Sweden (Stockholm), Belgium, Mexico, and Brazil, as well as a global estimate. IFRs in NYC were higher for individuals younger than 65 years old than most other populations, but similar for older individuals. IFRs for age groups less than 65 decreased with income and increased with income inequality measured using the Gini index. These results demonstrate that the age-specific fatality of COVID-19 differs among developed countries and raises questions about factors underlying these differences, including underlying health conditions and healthcare access.
... Furthermore, they can be used to study the risk factors for SARS-CoV-2 infection. 3 Estimating the seroprevalence among healthcare providers gives information on how the disease spreads in high-risk settings with many patient contacts, both symptomatic and asymptomatic. [4][5][6] On the one hand, it identifies the burden of infections in healthcare providers, and on the other hand STRENGTHS AND LIMITATIONS OF THIS STUDY ⇒ Phase III validation in a relevant target population, primary healthcare providers. ...
Objectives
To validate a rapid serological test (RST) for SARS-CoV-2 antibodies used in seroprevalence studies in healthcare providers, including primary healthcare providers (PHCPs) in Belgium.
Design
A phase III validation study of the RST (OrientGene) within a prospective cohort study.
Setting
Primary care in Belgium.
Participants
Any general practitioner (GP) working in primary care in Belgium and any other PHCP from the same GP practice who physically manages patients were eligible in the seroprevalence study. For the validation study, all participants who tested positive (376) on the RST at the first testing timepoint (T1) and a random sample of those who tested negative (790) and unclear (24) were included.
Intervention
At T2, 4 weeks later, PHCPs performed the RST with fingerprick blood (index test) immediately after providing a serum sample to be analysed for the presence of SARS-CoV-2 immunoglobulin G antibodies using a two-out-of-three assay (reference test).
Primary and secondary outcome measures
The RST accuracy was estimated using inverse probability weighting to correct for missing reference test data, and considering unclear RST results as negative for the sensitivity and positive for the specificity. Using these conservative estimates, the true seroprevalence was estimated both for T2 and RST-based prevalence values found in a cohort study with PHCPs in Belgium.
Results
1073 paired tests (403 positive on the reference test) were included. A sensitivity of 73% (a specificity of 92%) was found considering unclear RST results as negative (positive). For an RST-based prevalence at T1 (13.9), T2 (24.9) and T7 (70.21), the true prevalence was estimated to be 9.1%, 25.9% and 95.7%, respectively.
Conclusion
The RST sensitivity (73%) and specificity (92%) make an RST-based seroprevalence below (above) 23% overestimate (underestimate) the true seroprevalence.
Trial registration number
NCT04779424 .
... There could be many reasons to explain the differences between estimated and actual reported numbers of COVID-19 related infections and deaths. These include variable limitations in the capacity for testing and monitoring COVID-19 infections in HCWs across countries, under or no reporting of COVID-19 deaths occurring outside of hospitals or other healthcare settings, reluctance in some countries to disclose deaths from COVID-19 in healthcare workers, lack of testing of mild and asymptomatic cases, and false negative or false positive tests due to limitations in the specific test and testing conditions (9,(11)(12)(13). As a result, many untested HCWs deaths may have been excluded from total mortality figures. ...
... Important to note that the wide range predictions of future infection scenarios obtained from infectious disease models early in the pandemic were likely due to the under-detection and delayed reporting of RT-PCR confirmed COVID-19 cases (11,14). We found that there was a marked shift in the focus of articles from the analysis of PCR positivity rate amongst HCWs, to the seropositivity overtime, highlighting the potential importance of seropositivity in estimating untested/unreported infections. ...
Objectives: The COVID-19 pandemic has greatly impacted health and care workers (HCW) globally, whom are considered at greater risk of infection and death. This study aims to document emerging evidence on disease prevalence, clinical outcomes, and vaccination rates of HCWs.
Methods: Three databases were surveyed resulting on 108 final articles between July–December 2020 (period 1) and January–June 2021 (period 2).
Results: Amongst the overall 980,000 HCWs identified, in period 1, the estimates were 6.1% (95% CI, 4.1–8.8) for the PCR positivity rate. Regarding outcomes, the hospitalization prevalence was 1.6% (95% CI, 0.7–3.9), and mortality rate of 0.3% (95% CI, 0.1–0.8). In period 2, the PCR positivity rate was 8.1% (95% CI, 4.6–13.8). Analysis of outcomes revealed a hospitalization rate of 0.7% (95% CI 0.3–1.8), and average mortality rate of 0.3% (95% CI 0.1–0.9). Our analysis indicated a HCW vaccination rate of 59.0% (95% CI, 39.4–76.1).
Conclusion: Studies from the latter half of 2020 to the first half of 2021 showed a slight increasing trend in PCR positivity among HCW, along with improved clinical outcomes in the 1-year period of exposure. These results correlate well with the improving uptake of COVID-19 vaccination globally.
