SARS-CoV-2 Reinfection Rate and Outcomes in Saudi Arabia: A National Retrospective Study.

BACKGROUND: The characterization of reinfection with SARS-CoV-2 has been a subject of concern and controversy, especially with the surge of infections with highly transmissible variants worldwide. METHODS: This retrospective national study used comorbidities, vaccination status, SARS-CoV-2 variants of concern, and demographics data to profile participants who were reinfected with SARS-CoV-2, defined as having two reverse transcriptase-polymerase chain reaction-positive SARS-CoV-2 tests within at least 90 days apart. A multivariate logistic regression model assessed the risk factors associated with reinfection . Two control groups were selected: nonreinfected participants reporting a positive test (control group one) and those reporting a negative test (control group two). RESULTS: Between March 2020 and December 2021, 4454 reinfected participants were identified in Saudi Arabia (0.8%, 95% confidence interval [CI] 0.7-0.8). The majority (67.3%) were unvaccinated (95% CI 65.9-68.7) and 0.8% (95% CI 0.6-1.1) had severe or fatal SARS-CoV-2 disease. COVID-19 vaccines were 100% effective against mortality in reinfected individuals who received at least one dose, whereas it conferred 61% (odds ratio [OR] 0.4, 95% CI 0.1-1.0) additional protection against severe disease after the first dose and 100% after the second dose. In the risk factor analysis, reinfection was highly associated with comorbidities, such as HIV (OR 2.5, 95% CI 1.3-5.2; P = 0.009), obesity (OR 2.3, 95% CI 1.3-3.9; P = 0.003), pregnancy (OR 3.2, 95% CI 1.4-7.4; P = 0.005), and working in health care facilities (OR 6.1, 95% CI 3.1-12.9; P <0.0001). The delta variant (B.1.617.2) was the most frequent variant of concern among the reinfected cohort. CONCLUSION: This in-depth study of the reinfection profile identified risk factors and highlighted the associated SARS-CoV-2 variants. Results showed that naturally acquired immunity to SARS-CoV-2 through multiple reinfections together with vaccine-induced immunity provided substantial protection against severe SARS-CoV-2 disease and mortality.

Scalability and cost-effectiveness analysis of whole genome-wide association studies on Google Cloud Platform and Amazon Web Services.

OBJECTIVE: Advancements in human genomics have generated a surge of available data, fueling the growth and accessibility of databases for more comprehensive, in-depth genetic studies. METHODS: We provide a straightforward and innovative methodology to optimize cloud configuration in order to conduct genome-wide association studies. We utilized Spark clusters on both Google Cloud Platform and Amazon Web Services, as well as Hail (http://doi.org/10.5281/zenodo.2646680) for analysis and exploration of genomic variants dataset. RESULTS: Comparative evaluation of numerous cloud-based cluster configurations demonstrate a successful and unprecedented compromise between speed and cost for performing genome-wide association studies on 4 distinct whole-genome sequencing datasets. Results are consistent across the 2 cloud providers and could be highly useful for accelerating research in genetics. CONCLUSIONS: We present a timely piece for one of the most frequently asked questions when moving to the cloud: what is the trade-off between speed and cost?