Transmission risk of asymptomatic SARS-CoV-2 infection: a systematic review and meta-analysis.

Background: Global evidence on the transmission of asymptomatic SARS-CoV-2 infection needs to be synthesized. Methods: A search of 4 electronic databases (PubMed, EMBASE, Cochrane Library, and Web of Science databases) as of January 24, 2021 was performed. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Studies which reported the transmission rate among close contacts with asymptomatic SARS-CoV-2 cases were included, and transmission activities occurred were considered. The transmission rates were pooled by zero-inflated beta distribution. The risk ratios (RRs) were calculated using random-effects models. Results: Of 4923 records retrieved and reviewed, 15 studies including 3917 close contacts with asymptomatic indexes were eligible. The pooled transmission rates were 1.79 per 100 person-days (or 1.79%, 95% confidence interval [CI] 0.41%-3.16%) by asymptomatic index, which is significantly lower than by presymptomatic (5.02%, 95% CI 2.37%-7.66%; p<0.001), and by symptomatic (5.27%, 95% CI 2.40%-8.15%; p<0.001). Subgroup analyses showed that the household transmission rate of asymptomatic index was (4.22%, 95% CI 0.91%-7.52%), four times significantly higher than non-household transmission (1.03%, 95% CI 0.73%-1.33%; p=0.03), and the asymptomatic transmission rate in China (1.82%, 95% CI 0.11%-3.53%) was lower than in other countries (2.22%, 95% CI 0.67%-3.77%; p=0.01). Conclusions: People with asymptomatic SARS-CoV-2 infection are at risk of transmitting the virus to their close contacts, particularly in household settings. The transmission potential of asymptomatic infection is lower than symptomatic and presymptomatic infections. This meta-analysis provides evidence for predicting the epidemic trend and promulgating vaccination and other control measures. Registered with PROSPERO International Prospective Register of Systematic Reviews, CRD42021269446; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=269446.

Risk of SARS-CoV-2 reinfection: a systematic review and meta-analysis.

This meta-analysis aims to synthesize global evidence on the risk of reinfection among people previously infected with SARS-CoV-2. We systematically searched PubMed, Scopus, Embase and Web of Science as of April 5, 2021. We conducted: (1) meta-analysis of cohort studies containing data sufficient for calculating the incidence rate of SARS-CoV-2 reinfection; (2) systematic review of case reports with confirmed SARS-CoV-2 reinfection cases. The reinfection incidence was pooled by zero-inflated beta distribution. The hazard ratio (HR) between reinfection incidence among previously infected individuals and new infection incidence among infection-naïve individuals was calculated using random-effects models. Of 906 records retrieved and reviewed, 11 studies and 11 case reports were included in the meta-analysis and the systematic review, respectively. The pooled SARS-CoV-2 reinfection incidence rate was 0.70 (standard deviation [SD] 0.33) per 10,000 person-days. The incidence of reinfection was lower than the incidence of new infection (HR = 0.12, 95% confidence interval 0.09-0.17). Our meta-analysis of studies conducted prior to the emergency of the more transmissible Omicron variant showed that people with a prior SARS-CoV-2 infection could be re-infected, and they have a lower risk of infection than those without prior infection. Continuing reviews are needed as the reinfection risk may change due to the rapid evolution of SARS-CoV-2 variants.

GESLM algorithm for detecting causal SNPs in GWAS with multiple phenotypes.

With the development of genome-wide association studies, how to gain information from a large scale of data has become an issue of common concern, since traditional methods are not fully developed to solve problems such as identifying loci-to-loci interactions (also known as epistasis). Previous epistatic studies mainly focused on local information with a single outcome (phenotype), while in this paper, we developed a two-stage global search algorithm, Greedy Equivalence Search with Local Modification (GESLM), to implement a global search of directed acyclic graph in order to identify genome-wide epistatic interactions with multiple outcome variables (phenotypes) in a case-control design. GESLM integrates the advantages of score-based methods and constraint-based methods to learn the phenotype-related Bayesian network and is powerful and robust to find the interaction structures that display both genetic associations with phenotypes and gene interactions. We compared GESLM with some common phenotype-related loci detecting methods in simulation studies. The results showed that our method improved the accuracy and efficiency compared with others, especially in an unbalanced case-control study. Besides, its application on the UK Biobank dataset suggested that our algorithm has great performance when handling genome-wide association data with more than one phenotype.