IMPORTANCEThe Delta variant (B.1.617.2) is estimated to be more transmissible than previous strains of SARS-CoV-2, especially among children and adolescents. However, to our knowledge, there are no reports confirming this to date. OBJECTIVETo gain a better understanding of the association of age with susceptibility to the Delta variant of SARS-CoV-2. DESIGN, SETTING, AND PARTICIPANTS This decision analytic model used an age-structured compartmental model using the terms symptom onset (S), exposure (E), infectious (I), and quarantine (Q) (SEIQ) to estimate the age-specific force of infection, combining age-specific contact matrices and observed distribution of periods between each stage of infection (E to I [ie, latent period], I given S, and S to Q [ie, diagnostic delay]) developed in a previous contact tracing study. A bayesian inference method was used to estimate the age-specific force of infection (S to E) and, accordingly, age-specific susceptibility. The age-specific susceptibility during the third wave (ie, before Delta, from October 15 to December 22, 2020, when the COVID-19 vaccination campaign was not yet launched) and the fourth wave (ie, the Delta-driven wave, from June 27 to August 21, 2021) in Korea were compared. As vaccine uptake increased, individuals who were vaccinated were excluded from the susceptible population in accordance with vaccine effectiveness against the Delta variant. This nationwide epidemiologic study included individuals who were diagnosed with COVID-19 during the study period in Korea. Data were analyzed from September to November 2021. EXPOSURES Age group during the third wave (ie, before Delta) and fourth wave (ie, Delta-driven) of the COVID-19 pandemic in South Korea. MAIN OUTCOMES AND MEASURES Age-specific susceptibility during the third and fourth waves was estimated. RESULTS Among 106 866 confirmed COVID-19 infections (including 26 597 infections and 80 269 infections during the third and fourth waves of COVID-19 in Korea, respectively), a significant difference in age-specific susceptibility to the Delta vs pre-Delta variant was found in the younger age group. After adjustment for contact pattern and vaccination status, the increase in susceptibility to the Delta vs pre-Delta variant was estimated to be highest in the group aged 10 to 15 years, approximately doubling (1.92-fold increase [95% CI, 1.86-fold to 1.98-fold]), whereas in the group aged 50 years or more, susceptibility to the Delta vs pre-Delta variant remained stable at an approximately 1-fold change (eg, among individuals aged 50-55 years: 0.997-fold [95% CI, 0.989fold to 1.001-fold). CONCLUSIONS AND RELEVANCEIn this study, the Delta variant of SARS-CoV-2 was estimated to propagate more easily among children and adolescents than pre-Delta strains, even after adjusting for contact pattern and vaccination status.

Background The Omicron variant (B.1.1.529) is estimated to be more transmissible than previous strains of SARS-CoV-2 especially among children, potentially resulting in croup which is a characteristic disease in children. Current coronavirus disease 2019 (COVID-19) cases among children might be higher because (i) school-aged children have higher contact rates and (ii) the COVID-19 vaccination strategy prioritizes the elderly in most countries. However, there have been no reports confirming the age-varying susceptibility to the Omicron variant to date. Methods We developed an age-structured compartmental model, combining age-specific contact matrix in South Korea and observed distribution of periods between each stage of infection in the national epidemiological investigation. A Bayesian inference method was used to estimate the age-specific force of infection and, accordingly, age-specific susceptibility, given epidemic data during the third (pre-Delta), fourth (Delta driven), and fifth (Omicron driven) waves in South Korea. As vaccine uptake increased, individuals who were vaccinated were excluded from the susceptible population in accordance with vaccine effectiveness against the Delta and Omicron variants, respectively. Results A significant difference between the age-specific susceptibility to the Omicron and that to the pre-Omicron variants was found in the younger age group. The rise in susceptibility to the Omicron/pre-Delta variant was highest in the 10–15 years age group (5.28 times [95% CI, 4.94–5.60]), and the rise in susceptibility to the Omicron/Delta variant was highest in the 15–19 years age group (3.21 times [95% CI, 3.12–3.31]), whereas in those aged 50 years or more, the susceptibility to the Omicron/pre-Omicron remained stable at approximately twofold. Conclusions Even after adjusting for contact pattern, vaccination status, and waning of vaccine effectiveness, the Omicron variant of SARS-CoV-2 tends to propagate more easily among children than the pre-Omicron strains.

Background Due to a limited initial supply of COVID-19 vaccines, the prioritisation of individuals for vaccination is of utmost importance for public health. Here, we provide the optimal allocation strategy for COVID-19 vaccines according to age in Japan and South Korea. Methods Combining national case reports, age-specific contact matrices, and observed periods between each stages of infection (Susceptible-Exposed-Infectious-Quarantined), we constructed a compartmental model. We estimated the age-stratified probability of transmission given contact (q_i) using Bayesian inference method and simulated different vaccination scenarios to reduce either case numbers or death toll. We also performed sensitivity analyses on the proportion of asymptomatic cases and vaccine efficacy. Findings The model inferred age-stratified probability of transmission given contact (q_i) showed similar age-dependent increase in Japan and South Korea. Assuming the reported COVID-19 vaccine efficacy, our results indicate that Japan and South Korea need to prioritise individuals aged 20-35 years and individuals aged over 60 years, respectively, to minimise case numbers. To minimise the death toll, both countries need to prioritise individuals aged over 75 years. These trends were not changed by proportions of asymptomatic cases and varying vaccine efficacy on individuals under 20 years. Interpretation We presented the optimal vaccination strategy for Japan and South Korea. Comparing the results of these countries demonstrates that not only the effective contact rates containing q_i but also the age-demographics of current epidemic in Japan (dominance in 20s) and South Korea (dominant cases over 50s) affect vaccine allocation strategy.

The proportion of population vaccinated cannot be directly translated into the herd immunity. We have to account for the age-stratified contact patterns to calculate the population immunity level, since not every individual gathers evenly. Here, we calculated the contact-adjusted population immunity against severe acute respiratory syndrome coronavirus 2 in South Korea using age-specific incidence and vaccine uptake rate. We further explored options to achieve the theoretical herd immunity with age-varying immunity scenarios. As of June 21, 2021, when a quarter of the population received at least one dose of a coronavirus disease 2019 (COVID-19) vaccine, the contact-adjusted immunity level was 12.5% under the social distancing level 1. When 80% of individuals aged 10 years and over gained immunity, we could achieve a 58.2% contact-adjusted immunity level. The pros and cons of vaccinating children should be weighed since the risks of COVID-19 for the young are less than the elderly, and the long-term safety of vaccines is still obscure.

As AI has a wide range of influence on human social life, issues of transparency and ethics of AI are emerging. In particular, it is widely known that due to the existence of historical bias in data against ethics or regulatory frameworks for fairness, trained AI models based on such biased data could also impose bias or unfairness against a certain sensitive group (e.g., non-white, women). Demographic disparities due to AI, which refer to socially unacceptable bias that an AI model favors certain groups (e.g., white, men) over other groups (e.g., black, women), have been observed frequently in many applications of AI and many studies have been done recently to develop AI algorithms which remove or alleviate such demographic disparities in trained AI models.In this paper, we consider a problem of using the information in the sensitive variable for fair prediction when using the sensitive variable as a part of input variables is prohibitive by laws or regulations to avoid unfairness. As a way of reflecting the information in the sensitive variable to prediction, we consider a two-stage procedure. First, the sensitive variable is fully included in the learning phase to have a prediction model depending on the sensitive variable, and then an imputed sensitive variable is used in the prediction phase. The aim of this paper is to evaluate this procedure by analyzing several benchmark datasets. We illustrate that using an imputed sensitive variable is helpful to improve prediction accuracies without hampering the degree of fairness much.