COVID-19 vaccination dynamics in the US: coverage velocity and carrying capacity based on socio-demographic vulnerability indices in California's pediatric population.

Introduction: COVID-19 vaccine inequities have been widespread across California, the United States, and globally. As COVID-19 vaccine inequities have not been fully understood in the youth population, it is vital to determine possible factors that drive inequities to enable actionable change that promotes vaccine equity among vulnerable minor populations. Methods: The present study used the social vulnerability index (SVI) and daily vaccination numbers within the age groups of 12-17, 5-11, and under 5 years old across all 58 California counties to model the growth velocity and the anticipated maximum proportion of population vaccinated. Results: Overall, highly vulnerable counties, when compared to low and moderately vulnerable counties, experienced a lower vaccination rate in the 12-17 and 5-11 year-old age groups. For age groups 5-11 and under 5 years old, highly vulnerable counties are expected to achieve a lower overall total proportion of residents vaccinated. In highly vulnerable counties in terms of socioeconomic status and household composition and disability, the 12-17 and 5-11 year-old age groups experienced lower vaccination rates. Additionally, in the 12-17 age group, high vulnerability counties are expected to achieve a higher proportion of residents vaccinated compared to less vulnerable counterparts. Discussion: These findings elucidate shortcomings in vaccine uptake in certain pediatric populations across California and may help guide health policies and future allocation of vaccines, with special emphasis placed on vulnerable populations, especially with respect to socioeconomic status and household composition and disability.

A Novel Threshold-Based Segmentation Method for Quantification of COVID-19 Lung Abnormalities.

Since December 2019, the novel coronavirus disease 2019 (COVID-19) has claimed the lives of more than 3.75 million people worldwide. Consequently, methods for accurate COVID-19 diagnosis and classification are necessary to facilitate rapid patient care and terminate viral spread. Lung infection segmentations are useful to identify unique infection patterns that may support rapid diagnosis, severity assessment, and patient prognosis prediction, but manual segmentations are time-consuming and depend on radiologic expertise. Deep learning-based methods have been explored to reduce the burdens of segmentation; however, their accuracies are limited due to the lack of large, publicly available annotated datasets that are required to establish ground truths. For these reasons, we propose a semi-automatic, threshold-based segmentation method to generate region of interest (ROI) segmentations of infection visible on lung computed tomography (CT) scans. Infection masks are then used to calculate the percentage of lung abnormality (PLA) to determine COVID-19 severity and to analyze the disease progression in follow-up CTs. Compared with other COVID-19 ROI segmentation methods, on average, the proposed method achieved improved precision ( 47.49 % ) and specificity ( 98.40 % ) scores. Furthermore, the proposed method generated PLAs with a difference of ± 3.89 % from the ground-truth PLAs. The improved ROI segmentation results suggest that the proposed method has potential to assist radiologists in assessing infection severity and analyzing disease progression in follow-up CTs.