Effectiveness of a third dose of BNT162b2 or mRNA-1273 vaccine for preventing post-vaccination COVID-19 infection: an observational study (preprint)

Background BNT162b2 and mRNA-1273 vaccines are highly protective against COVID-19. Concern about waning immunity and reduced effectiveness against SARS-COV-2 variants led to use of a third dose six months after completing the primary series. We used data from the Veterans Health Administration to evaluate the effectiveness of a third dose of BNT162b2 or mRNA-1273 compared to the primary series in preventing post-vaccination COVID-19. Methods During January 1 - November 22, 2020, third dose recipients were matched (1:1) to demographically similar controls who did not receive a third dose. Eligible participants had completed the primary series at least six months (180 days) before recruitment date. Long-term care residents were excluded. Primary outcomes were documented SARS-CoV-2 infection and COVID-19 hospitalization. Effectiveness was estimated as 1-incidence rate ratio. Findings Following matching, the BNT162b2 group included 99,856 pairs and the mRNA-1273 group included 74,116 pairs. In BNT162b2 and mRNA-1273 groups, median age was 72 (interquartile range [IQR]: 65-75) and 72 (IQR: 67-76) years, 94,990 (95.1%) and 71,213 (96.1%) were male, and 61,261 (61.3%) and 52,170 (70.4%) were non-Hispanic White, respectively. Effectiveness of a third dose of BNT162b2 or mRNA-1273 compared to the primary series was 49.4% (95% confidence interval [CI]: 41.2-56.5%) and 46.0% (95% CI: 33.5-56.2%) for documented SARS-CoV-2 infection and 52.3% (95% CI: 33.8-65.6%) and 44.7% (95% CI: 10.7-65.7%) for COVID-19 hospitalization, respectively. Interpretation A third dose of BNT162b2 or mRNA-1273 is moderately effective against post-vaccination COVID-19 infection compared to the primary series.

COVID-19 Vaccine Breakthrough Infections in the Veterans Health Administration. (preprint)

BackgroundVaccination against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been accompanied by rising concern of vaccine breakthrough due to SARS-CoV-2 variants, waning protection over time, differential vaccine effectiveness, and regional resurgence of Coronavirus 2019 (COVID-19). Characterizing the frequency and drivers of vaccine breakthrough is necessary to inform COVID-19 control efforts. MethodsWe performed a retrospective cohort study of vaccine breakthrough infections in fully vaccinated persons in Veterans Health Administration. We applied Cox proportional hazard models to estimate cumulative incidence, assess differences in outcomes by vaccine, and identify associations with individual characteristics as well as time-dependent geographic variation in COVID-19 incidence, proportion of delta variant, and vaccine coverage. ResultsAmong 3,032,561 fully vaccinated persons, documented SARS-CoV-2 infection occurred in 11,197 (0.37%) and COVID-19 hospitalization occurred in 2,080 (0.07%). Compared to Ad26.COV2.S, BNT162b2 and mRNA-1273 had lower occurrence of documented SARS-CoV-2 infection (aHR 0.54, 95% confidence interval (CI) 0.51-0.58; aHR 0.36; 95% CI 0.33-0.38; respectively) and COVID-19 hospitalization (aHR 0.56, 95% CI 0.47-0.66; aHR 0.30; 0.25-0.35; respectively). Documented SARS-CoV-2 infection and COVID-19 hospitalization were associated with younger age, Hispanic or Latino ethnicity, number of comorbidities, and previous SARS-CoV-2 infection. Regional proportion of delta variant and county-level COVID-19 incidence were predictors of vaccine breakthrough events; county-level vaccine coverage was inversely associated. ConclusionsVaccine breakthrough was rare among fully vaccinated persons. mRNA-1273 and BNT162b2 were more protective against documented SARS-CoV-2 infection and COVID-19 hospitalization compared to Ad26.COV2.S. Efforts to limit COVID-19 transmission and bolster vaccine coverage would also curtail vaccine breakthrough.

Vulnerability disclosure and cybersecurity awareness campaigns on twitter during COVID-19

Abstract The COVID-19 pandemic introduced the new norm that changed the way we work and live. During these unprecedented times, most of the organizations expected their employees to work from home. Remote working created new opportunities for hackers since more users were making use of digital platforms for online shopping, accessing Virtual Private Network (VPN), videoconferencing platforms, and software alike. Consequently, cybercrime increased due to the increase in the attack surface, and software vulnerabilities were exploited for launching cyberattacks. There is existing research that explores vulnerability disclosure on Twitter. However, there is a lack of study on opportunistic targeted attacks where specific vulnerabilities are exploited in a way that benefit adversaries the most in times such as COVID-19. The primary aim of this work is to study the effectiveness of vulnerability disclosure pattern on Twitter in COVID-19, and discuss how Twitter can be leveraged as Open-Source Intelligence (OSINT) during a pandemic where the global users can follow a coordinated approach to share security-related information and conduct awareness campaigns. The study identifies Twitter as an apt source for conducting cybersecurity awareness campaigns as 99.83% of the security vulnerabilities are found to be accurate. The information can help global cybersecurity agencies to proactively identify vulnerabilities, coordinate activities, and plan for mitigation strategies since releasing patches from the vendor might take time.

"Who can help me?": Knowledge Infused Matching of Support Seekers and Support Providers during COVID-19 on Reddit (preprint)

During the ongoing COVID-19 crisis, subreddits on Reddit, such as r/Coronavirus saw a rapid growth in user's requests for help (support seekers - SSs) including individuals with varying professions and experiences with diverse perspectives on care (support providers - SPs). Currently, knowledgeable human moderators match an SS with a user with relevant experience, i.e, an SP on these subreddits. This unscalable process defers timely care. We present a medical knowledge-infused approach to efficient matching of SS and SPs validated by experts for the users affected by anxiety and depression, in the context of with COVID-19. After matching, each SP to an SS labeled as either supportive, informative, or similar (sharing experiences) using the principles of natural language inference. Evaluation by 21 domain experts indicates the efficacy of incorporated knowledge and shows the efficacy the matching system.