To estimate the relative effectiveness of vaccination (0, 1, 2, ≥3 doses) and prior infection, in combination, on risk of SARS-CoV-2 infection/reinfection.

Prospective cohort study.

We recruited participants for the Aegis Study from nine clinics across five US states. Participants must have been 18 years or older, had a history of a positive PCR for SARS-CoV-2, SARS-CoV-2 antigen or antibody test for SARS-CoV-2 with documentation or had no suspected or documented prior SARS-CoV-2 infection, intended to remain in study area for the next 12 months, and had elevated risk of future SARS-CoV-2 exposure. Exclusion criteria included acute illness, contraindication to phlebotomy, use of immunosuppressants or receipt of systemic immunoglobulins.

We used extended Cox regression with robust standard errors to estimate the association between time-varying number of vaccine doses and baseline prior infection on risk of infection/reinfection among a prospective cohort of US adults between February 2021 and January 2023, accounting for censoring using inverse probability of censoring weights. Additionally, to quantify possible exposure misclassification of prior infection by comparing prior infection operationalised as (1) documented/self-reported prior infection and (2) documented/self-reported prior infection plus nucleocapsid antibody indication of prior infection.

Of n=2178 who completed enrolment, n=1887 adults (63% female; 65% non-Latino White) contributed 366 905 days of observation. Participants contributed an average of 7.2 months of follow-up between February 2021 and January 2023. 28% (n=533) of individuals were infected or reinfected during the study period. Similar relative effectiveness was observed between the two different operationalisations of prior infection. After correction for prior infection status in the nearly 16% of those without study documentation of prior infection who had nucleocapsid antibody levels comparable to documented cases, relative to the unvaccinated with no prior infection, estimated effectiveness generally increased with increasing vaccine doses and prior infection (without prior infection: one (17%, 95% CI –31% to 47%), two (49%, 95% CI 31% to 63%), ≥three (71%, 95% CI 58% to 80%) vaccine doses; with prior infection: none (56%, 95% CI 30% to 72%), one (71%, 95% CI 42% to 86%), two (65%, 95% CI 49% to 76%), ≥three (80%, 95% CI 68% to 88%) vaccine doses). Pairwise comparisons at each vaccine dose (ref: no prior infection) revealed that prior infection provided additional protection, with stronger relationships for no and one dose (none: 56% (95% CI 30% to 72%), one: 66% (95% CI 28% to 84%), two: 31% (95% CI 7% to 49%), ≥three 31% (95% CI 0% to 53%)). There was a marked decrease in the protection offered by vaccination, prior infection, or both in the Omicron period versus pre-Omicron period.

In our real-world observational sample, vaccination (with two and ≥three vaccine doses of any Food and Drug Administration Emergency Use Authorization approved vaccine) and prior infection conferred benefits for protection against infection/reinfection. Re-classification of prior infection status based on antibody levels had little effect on results.

Early childhood—specifically, the period from 0 to 6 years of age—is a critical time in children’s lives with rapid growth in their cognitive, social and emotional development. This period has also been shown to be the most effective time for early interventions. The use of artificial Intelligence (AI) for supporting early child development is increasing alongside the rapid advancement of technology. AI can be used directly by children (eg, for implementing adaptive technologies), by individuals who interact with children (eg, educators, parents, nurses), and by individuals indirectly supporting early child development (eg, early childhood researchers or policy analysts). This scoping review will provide a roadmap for relevant stakeholders on how AI has been applied within and across different contexts to support infants and young children’s development, as well as the most predominant AI technologies used across various contexts.

The current study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Review The search syntax will be applied in PsycINFO, ERIC, Education Source, CINHAL, MEDLINE, Embase and IEEE Xplore. The purpose of this study is to curate and synthesise academic papers to examine the application of AI for supporting the development of children between birth and age 6 years of age. Studies with children or individuals who work directly or indirectly with children will be included. Part of the abstract and full-text screening will be conducted by two researchers, with discrepancies being resolved by the lead authors. In addition, AI will be used to help with study screening and data extraction once confirmed to be reliable (Cohen’s kappa >0.80). Thematic and content analyses will be conducted to identify the types of AI products used and their applications in different contexts, the most predominant AI products used within and across each context, as well as how children’s developmental outcomes are impacted by the use of these AI products. Where applicable, visualisations such as tables, graphs and figures will be used to synthesise the data across contexts and AI products used to support early development of young children.