SARS-CoV-2 seroprevalence among children in Latvia: A cross-sectional study

Coronavirus disease 2019 (COVID-19) is a major global health concern. In contrast to adults, the course of the disease has been observed to be mild or even asymptomatic in children. It is therefore both clinically and epidemiologically important to measure the seroprevalence in children and adolescents to discern the overall morbidity of the disease and to compare these findings with similar data collected globally. We conducted a cross-sectional study between March and July of 2022 at the Children Clinical University Hospital in Riga, Latvia, to evaluate the seroprevalence of antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Participants aged 0 to 18 years were enrolled during hospitalization for reasons other than COVID-19. The levels of SARS-CoV-2 spike protein and nucleocapsid antibodies were measured in blood samples. The possibility of transplacental antibody transport was evaluated by directly interviewing the mothers of participants aged 18 months and younger. Various demographic and epidemiological risk factors and their association with seroprevalence were analyzed. Positive SARS-CoV-2 nucleocapsid antibodies were designated the main criterion for seropositivity. Of 200 enrolled children, 173 were found to be seropositive, resulting in an overall seroprevalence of 86.5%. The highest seroprevalence was detected in children and adolescents aged 12 to 18 years. With the progression of the COVID-19 pandemic, the seroprevalence in children has increased significantly. We found that almost 1-third of seropositive children in our study population were unaware of being previously infected with SARS-CoV-2 due to an asymptomatic course of the disease. Our study findings pertaining to high seropositivity among children and adolescents might be beneficial for public authorities to adapt epidemiological strategies and prevention measures. The high seroprevalence rate reported here and in many other populations around the world suggests that COVID-19 will likely become one of the many seasonal viral infections.     

Hospital-Wide SARS-CoV-2 seroprevalence in health care workers in a Spanish teaching hospital

IntroductionHospital-wide SARS-CoV-2 seroprevalence is rarely explored and can identify areas of unexpected risk. We determined the seroprevalence against SARS-CoV-2 in all health care workers (HCW) at a hospital.MethodsCross-sectional study (14-27/04/2020). We determined SARS-CoV-2 IgG by ELISA in all HCW including external workers of a teaching hospital in Madrid. They were classified by professional category, working area, and risk for SARS-CoV-2 exposure.ResultsAmong 2919 HCW, 2590 (88,7%) were evaluated. The mean age was 43.8 years (SD 11.1), and 73.9% were females. Globally, 818 (31.6%) workers were IgG positive with no differences for age, sex or previous diseases. Of these, 48.5% did not report previous symptoms. Seropositivity was more frequent in high- (33.1%) and medium- (33.8%) than in low-risk areas (25.8%, p = 0.007), but not for hospitalization areas attending COVID-19 and non-COVID-19 patients (35.5 vs 38.3% p > 0.05). HWC with a previous SARS-CoV2 PCR-positive test were IgG seropositive in 90.8%. By multivariate logistic regression analysis seropositivity was significantly associated with being physicians (OR 2.37, CI95% 1.61–3.49), nurses (OR 1.67, CI95% 1.14–2.46), nurse assistants (OR 1.84, CI95% 1.24–2.73), HCW working at COVID-19 hospitalization areas (OR 1.71, CI95% 1.22–2.40), non-COVID-19 hospitalization areas (OR 1.88, CI95% 1.30–2.73), and at the Emergency Room (OR 1.51, CI95% 1.01–2.27).ConclusionsSeroprevalence uncovered a high rate of infection previously unnoticed among HCW. Patients not suspected of having COVID-19 as well as asymptomatic HCW may be a relevant source for nosocomial SARS-CoV-2 transmission.     

SARS-CoV-2 variants: A continuing threat to global health

Coronavirus disease 2019 (COVID-19) is an acute respiratory illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 pandemic ha...     

The seroprevalence of SARS-CoV-2 infection in Malaysia: 7 August to 11 October 2020

Background

From the beginning of the COVID-19 pandemic until mid-October 2020, Malaysia recorded ~15,000 confirmed cases. But there could be undiagnosed cases due mainly to asymptomatic infections. Seroprevalence studies can better quantify underlying infection from SARS-CoV-2 by identifying humoral antibodies against the virus. This study was the first to determine the prevalence of SARS-CoV-2 infection in  Malaysia's general population, as well as the proportion of asymptomatic and undiagnosed infections.

Methods

This cross-sectional seroprevalence study with a two-stage stratified random cluster sampling design included 5,131 representative community dwellers in Malaysia aged ≥1 year. Data collection lasted from 7 August to 11 October 2020 involving venous blood sampling and interviews for history of COVID-19 symptoms and diagnosis. Previous SARS-CoV-2 infection was defined as screened positive using the Wantai SARS-CoV-2 Total Antibody enzyme-linked immunosorbent assay and confirmed positive using the GenScript SARS-CoV-2 surrogate Virus Neutralization Test. We performed a complex sampling design analysis, calculating sample weights considering probabilities of selection, non-response rate and post-stratification weight.

Results

The overall weighted prevalence of SARS-CoV-2 infection was 0.49% (95%CI 0.28–0.85) (N = 150,857). Among the estimated population with past infection, around 84.1% (95%CI 58.84–95.12) (N = 126 826) were asymptomatic, and 90.1% (95%CI 67.06–97.58) (N = 135 866) were undiagnosed.

Conclusions

Our study revealed a low pre-variant and pre-vaccination seroprevalence of SARS-CoV-2 infection in Malaysia up to mid-October 2020, with a considerable proportion of asymptomatic and undiagnosed cases. This led to subsequent adoption of SARS-CoV-2 antigen rapid test kits to increase case detection rate and to reduce time to results and infection control measures.     

Estimating seroprevalence of SARS-CoV-2 in Ohio: A Bayesian multilevel poststratification approach with multiple diagnostic tests

Globally, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected more than 59 million people and killed more than 1.39 million. Designing and monitoring interventions to slow and stop the spread of the virus require knowledge of how many people have been and are currently infected, where they live, and how they interact. The first step is an accurate assessment of the population prevalence of past infections. There are very few population-representative prevalence studies of SARS-CoV-2 infections, and only two states in the United States—Indiana and Connecticut—have reported probability-based sample surveys that characterize statewide prevalence of SARS-CoV-2. One of the difficulties is the fact that tests to detect and characterize SARS-CoV-2 coronavirus antibodies are new, are not well characterized, and generally function poorly. During July 2020, a survey representing all adults in the state of Ohio in the United States collected serum samples and information on protective behavior related to SARS-CoV-2 and coronavirus disease 2019 (COVID-19). Several features of the survey make it difficult to estimate past prevalence: 1) a low response rate; 2) a very low number of positive cases; and 3) the fact that multiple poor-quality serological tests were used to detect SARS-CoV-2 antibodies. We describe a Bayesian approach for analyzing the biomarker data that simultaneously addresses these challenges and characterizes the potential effect of selective response. The model does not require survey sample weights; accounts for multiple imperfect antibody test results; and characterizes uncertainty related to the sample survey and the multiple imperfect, potentially correlated tests.

Slowing or stopping the spread of a new virus for which a vaccine does not exist starts with two key pieces of information. One is what fraction of the population has been infected and is thereby potentially less susceptible or even immune to future infection; two is what fraction of the population is currently infected and potentially infectious to others. Together with a basic understanding of the infection process, this information roughly characterizes the potential for the epidemic to grow. However, in the early months of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there was great uncertainty about the number of people who had been infected due to challenges with adequate testing and asymptomatic and mild cases. Due to this uncertainty, debate occurred over whether the past infection prevalence was far higher than recorded cases suggested, even potentially approaching herd immunity thresholds, believed to be 70 to 85%. A population-based estimate of past infection prevalence was of critical importance to public health officials and policy makers who have the responsibility to manage the epidemic, make policy, and protect the public.As of this writing (late November 2020), the global SARS-CoV-2 pandemic has infected more than 59 million people, and coronavirus disease 2019 has killed more than 1.39 million (1). Basic epidemiological information to describe the pandemic is scarce because the virus is new and the pandemic exploded rapidly. In its place is a wide variety of indicators based on convenient, mostly nonrepresentative, or indirectly related data—counts of all-cause deaths (e.g., refs. 2–4), facility-based testing results for symptomatic patients, nonrepresentative samples, and in many situations, results from inadequately characterized tests that perform poorly (5). Franceschi et al. (6) identify 37 SARS-CoV-2 prevalence studies from 19 countries. Most present results are from nonrepresentative, otherwise special, or very small study populations. Just 14 represent large-enough populations to be of policy interest—national or state level—and use a probability-based sample from a credible sampling frame to produce results that could represent the population of interest—Asia: one (7); Europe: seven (8–14); North America: two (15, 16); and South America: four (17–20). The two in North America are from the state of Indiana (15) and the state of Connecticut (16) in the United States.Conducting population-representative biomarker surveys is difficult—particularly in the United States. Good sampling frames exist in a variety of forms (tax rolls, telephone numbers, etc.), but recruiting willing respondents is exceptionally difficult and likely affected by selection relative to the outcome of interest. Both of the studies in the United States had low response rates for the full interview with valid test results for SARS-CoV-2—Indiana: 23.4% and Connecticut: 7.8%. Further complicating analysis, there were few positive tests among those who did respond—Indiana: 47/3,658=1.3% for the PCR test of current infection and 38/3,658=1.0% for antibody test of ever infected and Connecticut: 23/567=4.1% for the antibody test of ever infected. Both studies described concern that the nonresponding participants were likely to be at higher risk of infection with SARS-CoV-2. Finally, like all SARS-CoV-2 immunology investigations to date, both studies struggled with poor-quality antibody tests whose unfavorable performance characteristics were not well understood; ref. 7 has an overview of these issues.Statistical analysis of data like these is difficult. First, the low response rate requires extensive recalibration of the sampling weights, and in the worst case, there may be sampling units with no respondents at all. Second, the very small number of positive cases pushes the asymptotic (large-sample) assumptions of frequentist methods to their limits and can break them. Third, the imperfect and poorly characterized antibody tests potentially add a lot of uncertainty that must be reflected in the results, particularly in low-prevalence settings (21). Fourth, when results from multiple tests with different performance characteristics are combined, the joint result must be accurately described and its uncertainty propagated to the final estimate of prevalence—importantly, including the possibility that results from individual tests are correlated. Finally, if there is selection on the outcome, then the effect of this must be understood. In our review of the literature, we did not find an existing method that addresses all of these challenges in a unified way.Here, we describe an analytical approach developed to produce estimates of past infection with SARS-CoV-2 using data from a probability-based household survey representing adults in the state of Ohio in the United States. Like the SARS-CoV-2 prevalence studies in Indiana and Connecticut, the Ohio survey had a low response rate, few positive cases, and the possibility of selective response. Additionally, the Ohio survey used multiple imperfect antibody tests for the same antibodies, resulting in the need to quantify uncertainty in the joint result and account for possible dependence among results.To overcome these challenges, we weave together two well-established modeling frameworks into a single coherent approach. We utilize the literature on modeling multiple imperfect diagnostic tests through the use of a Bayesian latent class model (e.g., refs. 22 and 23). This enables us to combine information across tests to infer the true latent infection status of a participant while incorporating uncertainty about the characteristics of the tests. We use the latent infection status to generate model-based estimates of the population prevalence using multilevel regression and poststratification (21, 24). These approaches are integrated into a single Bayesian model that allows for the full propagation of uncertainty, exact inferences, and the ability to specify informative priors using external information. By doing so, we produce estimates that reflect all available information and uncertainty.     

Ongoing global and regional adaptive evolution of SARS-CoV-2

Understanding the trends in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) evolution is paramount to control the COVID-19 pandemic. We analyzed more than 300,000 high-quality genome sequences of SARS-CoV-2 variants available as of January 2021. The results show that the ongoing evolution of SARS-CoV-2 during the pandemic is characterized primarily by purifying selection, but a small set of sites appear to evolve under positive selection. The receptor-binding domain of the spike protein and the region of the nucleocapsid protein associated with nuclear localization signals (NLS) are enriched with positively selected amino acid replacements. These replacements form a strongly connected network of apparent epistatic interactions and are signatures of major partitions in the SARS-CoV-2 phylogeny. Virus diversity within each geographic region has been steadily growing for the entirety of the pandemic, but analysis of the phylogenetic distances between pairs of regions reveals four distinct periods based on global partitioning of the tree and the emergence of key mutations. The initial period of rapid diversification into region-specific phylogenies that ended in February 2020 was followed by a major extinction event and global homogenization concomitant with the spread of D614G in the spike protein, ending in March 2020. The NLS-associated variants across multiple partitions rose to global prominence in March to July, during a period of stasis in terms of interregional diversity. Finally, beginning in July 2020, multiple mutations, some of which have since been demonstrated to enable antibody evasion, began to emerge associated with ongoing regional diversification, which might be indicative of speciation.

High mutation rates of RNA viruses enable adaptation to hosts at a staggering pace (1–4). Nevertheless, robust sequence conservation indicates that purifying selection is the principal force shaping the evolution of virus populations, with positive selection affecting only relatively small subsets of sites directly involved in virus−host coevolution (5–8). The fate of a novel zoonotic virus is, in part, determined by the race between public health intervention and virus diversification. Even intermittent periods of positive selection can result in lasting immune evasion, leading to oscillations in the size of the susceptible population and, ultimately, a regular pattern of repeating epidemics, as has been amply demonstrated for influenza (9–11).During the current coronavirus pandemic (COVID-19), understanding the degree and dynamics of the diversification of severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) and identification of sites subject to positive selection are essential for establishing practicable, proportionate public health responses, from guidelines on isolation and quarantine to vaccination (12). To investigate the evolution of SARS-CoV-2, we collected all available SARS-Cov-2 genomes as of January 8, 2021, and constructed a global phylogenetic tree using a “divide and conquer” approach. Patterns of repeated mutations fixed along the tree were analyzed in order to identify the sites subject to positive selection. These sites form a network of potential epistatic interactions. Analysis of the putative adaptive mutations provides for the identification of signatures of evolutionary partitions of SARS-CoV-2. The dynamics of these partitions over the course of the pandemic reveals alternating periods of globalization and regional diversification.     

Infection-induced seroconversion and seroprevalence of SARS-CoV-2 among a cohort of children and youth in Montreal,Canada

The EnCORE study is a prospective serology study of SARS-CoV-2 in a cohort of children from Montreal, Canada. Based on data from our fourth round of data collection (May–October 2022), we estimated SARS-CoV-2 seroprevalence and seroconversion. Using multivariable regression, we identified factors associated with seroconversion. Our results show that previously seronegative children were approximately 9–12 times more likely to seroconvert during the early Omicron-dominant period compared to pre-Omicron rounds. Unlike the pre-Omicron rounds, the adjusted rate of seroconversion among 2- to 4-year-olds was higher than older age groups. As seen previously, higher seroconversion rates were associated with ethnic/racial minority status.     

SARS-CoV-2 seroprevalence trends in healthy blood donors during the COVID-19 outbreak in Milan

BackgroundThe Milan metropolitan area in Northern Italy was among the most severely hit by the SARS-CoV-2 outbreak. The aim of this study was to examine the seroprevalence trends of SARS-CoV-2 in healthy asymptomatic adults, and the risk factors and laboratory correlates of positive tests.Materials and methodsWe conducted a cross-sectional study in a random sample of blood donors, who were asymptomatic at the time of evaluation, at the beginning of the first phase (February 24^th to April 8^th 2020; n=789). Presence of IgM/IgG antibodies against the SARS-CoV-2-Nucleocapsid protein was assessed by a lateral flow immunoassay.ResultsThe test had a 100/98.3 sensitivity/specificity (n=32/120 positive/negative controls, respectively), and the IgG test was validated in a subset by an independent ELISA against the Spike protein (n=34, p<0.001). At the start of the outbreak, the overall adjusted seroprevalence of SARS-CoV-2 was 2.7% (95% CI: 0.3–6%; p<0.0001 vs 120 historical controls). During the study period, characterised by a gradual implementation of social distancing measures, there was a progressive increase in the adjusted seroprevalence to 5.2% (95% CI: 2.4–9.0; 4.5%, 95% CI: 0.9–9.2% according to a Bayesian estimate) due to a rise in IgG reactivity to 5% (95% CI: 2.8–8.2; p=0.004 for trend), but there was no increase in IgM⁺ (p=not significant). At multivariate logistic regression analysis, IgG reactivity was more frequent in younger individuals (p=0.043), while IgM reactivity was more frequent in individuals aged >45 years (p=0.002).DiscussionSARS-CoV-2 infection was already circulating in Milan at the start of the outbreak. The pattern of IgM/IgG reactivity was influenced by age: IgM was more frequently detected in participants aged >45 years. By the end of April, 2.4–9.0% of healthy adults had evidence of seroconversion.     

SARS-CoV-2 seroprevalence in blood donors before and after the first wave in Catalonia (Spain)

BackgroundDue to the COVID-19 pandemic, a national lockdown was applied in Spain from March to May 2020. It is uncertain when SARS-CoV-2 started to circulate in Catalonia, and only a few cases were diagnosed in this period. We assessed the SARS-CoV-2 seroprevalence in blood donors before and after the first wave and compared it with public health service (PHS) data.Materials and methodsRetrospective archive or prospective fresh blood samples were obtained from blood donors aged 18 to 70 and anonymized after demographic data had been recorded (gender, age, place of residence, blood collection date). Two CE-marked enzyme-linked immunosorbent assays were used to test for anti-SARS-CoV-2. A SARS-CoV-2 IgM test was additionally performed in positive samples. Individuals aged 18 to 70 from among the general population diagnosed as having SARS-CoV-2 by the PHS were included for comparison with blood donor results.ResultsA total of 10,170 blood donations were included in the first period, between 24 February and 9 March 2020, and 6,829 in the second period, between 16 May and 17 June 2020. The observed SARS-CoV-2 seroprevalence among blood donors rose from 0.27% (95% CI: 0.18–0.39) before the first wave to 5.55% (95% CI: 5.03–6.12) after it, and was even higher (6.90% [95% CI: 5.64–8.41]) among blood donors aged 18 to 29. The seroprevalence among blood donors was higher in more populated areas (Barcelona: 7.69%). A comparison of blood donor data with officially diagnosed cases showed a global 87.44% underestimation of SARS-CoV-2 in June 2020.DiscussionWe analyzed the explosive 3-month increase in blood donor SARS-CoV-2 seroprevalence (from 0.27% to 5.55%) and show that more than 87% of cases went undiagnosed, despite the unprecedented deployment of testing measures. SARS-CoV-2 IgM results suggest that the virus was circulating among blood donors in February 2020. Blood donors are definitively proven to be a valuable resource for emerging disease surveillance studies.     

LinearTurboFold: Linear-time global prediction of conserved structures for RNA homologs with applications to SARS-CoV-2

The constant emergence of COVID-19 variants reduces the effectiveness of existing vaccines and test kits. Therefore, it is critical to identify conserved structures in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes as potential targets for variant-proof diagnostics and therapeutics. However, the algorithms to predict these conserved structures, which simultaneously fold and align multiple RNA homologs, scale at best cubically with sequence length and are thus infeasible for coronaviruses, which possess the longest genomes (∼30,000 nt) among RNA viruses. As a result, existing efforts on modeling SARS-CoV-2 structures resort to single-sequence folding as well as local folding methods with short window sizes, which inevitably neglect long-range interactions that are crucial in RNA functions. Here we present LinearTurboFold, an efficient algorithm for folding RNA homologs that scales linearly with sequence length, enabling unprecedented global structural analysis on SARS-CoV-2. Surprisingly, on a group of SARS-CoV-2 and SARS-related genomes, LinearTurboFold’s purely in silico prediction not only is close to experimentally guided models for local structures, but also goes far beyond them by capturing the end-to-end pairs between 5′ and 3′ untranslated regions (UTRs) (∼29,800 nt apart) that match perfectly with a purely experimental work. Furthermore, LinearTurboFold identifies undiscovered conserved structures and conserved accessible regions as potential targets for designing efficient and mutation-insensitive small-molecule drugs, antisense oligonucleotides, small interfering RNAs (siRNAs), CRISPR-Cas13 guide RNAs, and RT-PCR primers. LinearTurboFold is a general technique that can also be applied to other RNA viruses and full-length genome studies and will be a useful tool in fighting the current and future pandemics.

RNA plays important roles in many cellular processes (1, 2). To maintain their functions, secondary structures of RNA homologs are conserved across evolution (3–5). These conserved structures provide critical targets for diagnostics and treatments. Thus, there is a need for developing fast and accurate computational methods to identify structurally conserved regions.Commonly, conserved structures involve compensatory base pair changes, where two positions in primary sequences mutate across evolution and still conserve a base pair; for instance, an AU or a CG pair replaces a GC pair in homologous sequences. These compensatory changes provide strong evidence for evolutionarily conserved structures (6–10). Meanwhile, they make it harder to align sequences when structures are unknown. Initially, the process of determining a conserved structure, termed comparative sequence analysis, was manual and required substantial insight to identify the conserved structure. A notable early achievement was the determination of the conserved transfer RNA (tRNA) secondary structure (11). Comparative analysis was also demonstrated to be 97% accurate compared to crystal structures for ribosomal RNAs, where the models were refined carefully over time (12).To automate comparative analysis, three distinct algorithmic approaches were developed (13, 14). The first, “joint fold-and-align” method, seeks to simultaneously predict structures and a structural alignment for two or more sequences. This was first proposed by Sankoff (15) using a dynamic programming algorithm. The major limitation of this approach is that the algorithm runs in O(n3k) against k sequences with the average sequence length n. Several software packages provide implementations of the Sankoff algorithm (16–21) that use simplifications to reduce runtime. The second, “align-then-fold” approach, is to input a sequence alignment and predict the conserved structure that can be identified across sequences in the alignment. This was described by Waterman (22) and was subsequently refined and popularized by RNAalifold (23). The third, “fold-then-align” approach, is to predict plausible structures for the sequences and then align the structures to determine the sequence alignment and the optimal conserved structures. This was described by Waterman (24) and implemented in RNAforester (25) and MARNA (26) (SI Appendix, Fig. S1).As an alternative, TurboFold II (27), an extension of TurboFold (28), provides a more computationally efficient method to align and fold sequences. Taking multiple unaligned sequences as input, TurboFold II iteratively refines alignments and structure predictions so that they conform more closely to each other and converge on conserved structures. TurboFold II is significantly more accurate than other methods (16, 18, 23, 29, 30) when tested on RNA families with known structures and alignments.However, the cubic runtime and quadratic memory usage of TurboFold II prevent it from scaling to longer sequences such as full-length severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes, which contain ∼30,000 nucleotides; in fact, no joint-align-and-fold methods can scale to these genomes, which are the longest among RNA viruses. As a (not very principled) workaround, most existing efforts for modeling SARS-CoV-2 structures (31–36) resort to local folding methods (37, 38) with sliding windows plus a limited pairing distance, abandoning all long-range interactions, and only consider one SARS-CoV-2 genome (Fig. 1 B and C), ignoring signals available in multiple homologous sequences. To address this challenge, we designed a linearized version of TurboFold II, LinearTurboFold (Fig. 1A), which is a global homologous folding algorithm that scales linearly with sequence length. This linear runtime makes it, to our knowledge, the first joint-fold-and-align algorithm scale to full-length coronavirus genomes without any constraints on window size or pairing distance, taking about 13 h to analyze a group of 25 SARS-CoV homologs. It also leads to significant improvement on secondary structure prediction accuracy as well as an alignment accuracy comparable to or higher than all benchmarks.Open in a separate windowFig. 1.(A) The LinearTurboFold framework. Like TurboFold II, LinearTurboFold takes multiple unaligned homologous sequences as input and outputs a secondary structure for each sequence and a multiple-sequence alignment (MSA). But unlike TurboFold II, LinearTurboFold employs two linearizations to ensure linear runtime: a linearized alignment computation (module 1) to predict posterior coincidence probabilities (red squares) for all pairs of sequences (first four sections in Methods) and a linearized partition function computation (module 2) to estimate base-pairing probabilities (yellow triangles) for all the sequences (Methods, Extrinsic Information Calculation and Methods, LinearPartition for Base Pairing Probabilities Estimation with Extrinsic Information). These two modules take advantage of information from each other and iteratively refine predictions (SI Appendix, Fig. S2). After several iterations, module 3 generates the final multiple-sequence alignments (Methods, MSA Generation and Secondary Structure Prediction), and module 4 predicts secondary structures. Module 5 can stochastically sample structures. (B and C) Prior studies (31–36) [except for the purely experimental work by Ziv et al. (39)] used local folding methods with limited window size and maximum pairing distance. B shows the local folding of the SARS-CoV-2 genome by Huston et al. (32), which used a window of 3,000 nt that was advanced 300 nt. It also limited the distance between nucleotides that can form base pair at 500. Some studies also used homologous sequences to identify conserved structures (32–36), but they predicted only structures for one genome and utilized sequence alignments to identify mutations. By contrast, LinearTurboFold is a global folding method without any limitations on sequence length or paring distance, and it jointly folds and aligns homologs to obtain conserved structures. Consequently, LinearTurboFold can capture long-range interactions even across the whole genome (the long arc in B and Fig. 3).Over a group of 25 SARS-CoV-2 and SARS-related homologous genomes, LinearTurboFold predictions are close to the canonical structures (40) and structures modeled with the aid of experimental data (32–34) for several well-studied regions. Due to global rather than local folding, LinearTurboFold discovers a long-range interaction involving 5′ and 3′ untranslated regions (UTRs) (∼29,800 nt apart), which is consistent with recent purely experimental work (39) and yet is out of reach for local folding methods used by existing studies (Fig. 1 B and C). In short, our in silico method of folding multiple homologs can achieve results similar to, and sometimes more accurate than, those of experimentally guided models for one genome. Moreover, LinearTurboFold identifies conserved structures supported by compensatory mutations, which are potential targets for small-molecule drugs (41) and antisense oligonucleotides (ASOs) (36). We further identify regions that are 1) sequence-level conserved; 2) at least 15 nt long; and 3) accessible (i.e., likely to be completely unpaired) as potential targets for ASOs (42), small interfering RNA (siRNA) (43), CRISPR-Cas13 guide RNA (gRNA) (44), and RT-PCR primers (45). LinearTurboFold is a general technique that can also be applied to other RNA viruses (e.g., influenza, Ebola, HIV, Zika, etc.) and full-length genome studies.     

Evolution of seroprevalence to SARS-CoV-2 in blood donors in Sarajevo Canton,Federation of Bosnia and Herzegovina: Cross-sectional and longitudinal studies

Background

Sarajevo Canton in the Federation of Bosnia and Herzegovina has recorded several waves of high SARS-CoV-2 transmission and has struggled to reach adequate vaccination coverage. We describe the evolution of infection- and vaccine-induced SARS-CoV-2 antibody response and persistence.

Methods

We conducted repeated cross-sectional analyses of blood donors aged 18–65 years in Sarajevo Canton in November–December 2020 and 2021. We analyzed serum samples for anti-nucleocapsid (anti-N) and anti-spike (anti-S) antibodies. To assess immune durability, we conducted longitudinal analyses of seropositive participants at 6 and 12 months.

Results

One thousand fifteen participants were included in Phase 1 (November–December 2020) and 1152 in Phase 2 (November–December 2021). Seroprevalence increased significantly from 19.2% (95% CI: 17.2%–21.4%) in Phase 1 to 91.6% (95% CI: 89.8%–93.1%) in Phase 2. Anti-S IgG titers were significantly higher among vaccinated (58.5%) than unvaccinated infected participants across vaccine products (p < 0.001), though highest among those who received an mRNA vaccine. At 6 months, 78/82 (95.1%) participants maintained anti-spike seropositivity; at 12 months, 58/58 (100.0%) participants were seropositive, and 33 (56.9%) had completed the primary vaccine series within 6 months. Among 11 unvaccinated participants who were not re-infected at 12 months, anti-S IgG declined from median 770.1 (IQR 615.0–1321.7) to 290.8 (IQR 175.7–400.3). Anti-N IgG antibodies waned earlier, from 35.4% seropositive at 6 months to 24.1% at 12 months.

Conclusions

SARS-CoV-2 seroprevalence increased significantly over 12 months from end of 2020 to end of 2021. Although individuals with previous infection may have residual protection, COVID-19 vaccination is vital to strengthening population immunity.     

Pooled SARS-CoV-2 antigen tests in asymptomatic children and their caregivers: Screening for SARS-CoV-2 in a pediatric emergency department

BackgroundUniversal admission screening for SARS-CoV-2 in children and their caregivers (CG) is critical to prevent hospital outbreaks. We evaluated pooled SARS-CoV-2 antigen tests (AG) to identify infectious individuals while waiting for polymerase chain reaction (PCR) test results.MethodsThis single-center study was performed from November 5, 2020 to March 1, 2021. Nasal mid-turbinate and oropharyngeal swabbing for AG and PCR testing was performed in children with 2 individual swabs that were simultaneously inserted. Nasopharyngeal swabs were obtained from their CG. AG swabs were pooled in a single extraction buffer tube and PCR swabs in a single viral medium. Results from an adult population were used for comparison, as no pooled testing was performed.ResultsDuring the study period, 710 asymptomatic children and their CG were admitted. Pooled AG sensitivity and specificity was 75% and 99.4% respectively for detection of infectious individuals. Four false negatives were observed, though 3 out of 4 false negative child-CG pairs were not considered infectious at admission. Unpooled AG testing in an adult population showed a comparable sensitivity and specificity of 50% and 99.7%. AG performed significantly better in samples with lower Ct values in the corresponding PCR (32.3 vs 21, P-value < .001).ConclusionsPooled SARS-CoV-2 AGs are an effective method to identify potentially contagious individuals prior admission, without adding additional strain to the child.