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In the context of the COVID-19 pandemic, global vaccine campaigns are a mass vaccination intervention conducted through routine service provision for individuals who have reached a specified age. However, obtaining a high uptake rate to reach herd immunity may be undermined by various social motivations. To scrutinize the practical and dynamic strategies for a successful vaccination campaign, we map out the determinants that exacerbate vaccine hesitancy by leveraging the capacity of rich metadata from Twitter. Here, we uncover the collective propensities underlying dynamic social motivations and the uneven distribution of vaccines across the globe. Our findings suggest that profiling the status quo of public perceptions and engaging in introspection about vaccine-promoting policies in due course are integral components of preparedness against the ongoing pandemic. Simultaneously, we propose several recommendations to remind governments of the importance of building confidence in vaccination in a targeted way, and we assert that national barriers should be abandoned and that international responsibility should be assumed.

ObjectivesTo establish a multi-classification model for precisely predicting the invasiveness (pre-invasive adenocarcinoma, PIA; minimally invasive adenocarcinoma, MIA; invasive adenocarcinoma, IAC) of lung adenocarcinoma manifesting as pure ground-glass nodules (pGGNs).MethodsBy the inclusion and exclusion criteria, this retrospective study enrolled 346 patients (female, 297, and male, 49; age, 55.79 ± 10.53 (24-83)) presenting as pGGNs from 1292 consecutive patients with pathologically confirmed lung adenocarcinoma. A total of 27 clinical were collected and 1409 radiomics features were extracted by PyRadiomics package on python. After feature selection with L2,1-norm minimization, logistic regression (LR), extra w(ET) and gradient boosting decision tree (GBDT) were used to construct the three-classification model. Then, an ensemble model of the three algorithms based on model ensemble strategy was established to further improve the classification performance.ResultsAfter feature selection, a hybrid of 166 features consisting of 1 clinical (short-axis diameter, ranked 27th) and 165 radiomics (4 shape, 71 intensity and 90 texture) features were selected. The three most important features are wavelet-HLL_firstorder_Minimum, wavelet-HLL_ngtdm_Busyness and square_firstorder_Kurtosis. The hybrid-ensemble model based on hybrid clinical-radiomics features and the ensemble strategy showed more accurate predictive performance than other models (hybrid-LR, hybrid-ET, hybrid-GBDT, clinical-ensemble and radiomics-ensemble). On the training set and test set, the model can obtain the accuracy values of 0.918 ± 0.022 and 0.841, and its F1-scores respectively were 0.917 ± 0.024 and 0.824.ConclusionThe multi-classification of invasive pGGNs can be precisely predicted by our proposed hybrid-ensemble model to assist patients in the early diagnosis of lung adenocarcinoma and prognosis.