ABSTRACT
Vaccination against SARS-CoV-2 with BNT162b2 mRNA vaccine plays a critical role in COVID-19 prevention. Although BNT162b2 is highly effective against COVID-19, a time-dependent decrease in neutralizing antibodies (NAbs) is observed. The aim of this study was to identify the individual features that may predict NAbs levels after vaccination. Machine learning techniques were applied to data from 302 subjects. Principal component analysis (PCA), factor analysis of mixed data (FAMD), k-means clustering, and random forest were used. PCA and FAMD showed that younger subjects had higher levels of neutralizing antibodies than older subjects. The effect of age is strongest near the vaccination date and appears to decrease with time. Obesity was associated with lower antibody response. Gender had no effect on NAbs at nine months, but there was a modest association at earlier time points. Participants with autoimmune disease had lower inhibitory levels than participants without autoimmune disease. K-Means clustering showed the natural grouping of subjects into five categories in which the characteristics of some individuals predominated. Random forest allowed the characteristics to be ordered by importance. Older age, higher body mass index, and the presence of autoimmune diseases had negative effects on the development of NAbs against SARS-CoV-2, nine months after full vaccination.
ABSTRACT
In this work, the Productivity Index (PI) was developed for evaluating ten plastic drop-off points along the Sukhumvit Road, Bangkok, Thailand. Factor Analysis of Mixed Data (FAMD) was employed to study the effects of various parameters on drop-off point's performance. To explore plastic separation behaviors, the structured questionnaires were created based on the extended Theory of Planned Behavior, and the questionnaire's responses were then analyzed through the Structural Equation Model (SEM). The highest PI (0.0058) was observed from a drop-off point located in the shopping mall while that installed in a restaurant exhibited the lowest PI (0.0008). Besides environmental attitude and perceived behavioral control, a drop-off point facility was proved as another factor influencing people's intention towards plastic waste separation behavior. To improve the PI, drop-off point's bin design and location should be carefully optimized. Moreover, public relation on drop-off point campaigns and knowledge on household plastic waste separation should be promoted. These findings are helpful for the improvement or expansion of plastic drop-off point facilities as well as for the future development of waste recycling policy.
ABSTRACT
The COVID-19 outbreak has brought great challenges to healthcare resources around the world. Patients with COVID-19 exhibit a broad spectrum of clinical characteristics. In this study, the Factor Analysis of Mixed Data (FAMD)-based cluster analysis was applied to demographic information, laboratory indicators at the time of admission, and symptoms presented before admission. Three COVID-19 clusters with distinct clinical features were identified by FAMD-based cluster analysis. The FAMD-based cluster analysis results indicated that the symptoms of COVID-19 were roughly consistent with the laboratory findings of COVID-19 patients. Furthermore, symptoms for mild patients were atypical. Different hospital stay durations and survival differences among the three clusters were also found, and the more severe the clinical characteristics were, the worse the prognosis. Our aims were to describe COVID-19 clusters with different clinical characteristics, and a classifier model according to the results of FAMD-based cluster analysis was constructed to help provide better individualized treatments for numerous COVID-19 patients in the future.