Differences in virus and immune dynamics for SARS-CoV-2 Delta and Omicron infections by age and vaccination histories (preprint)

Vaccination against COVID-19 was integral to controlling the pandemic that persisted with the continuous emergence of SARS-CoV-2 variants. Using a mathematical model describing SARS-CoV-2 within-host infection dynamics, we estimate differences in virus and immunity due to factors of infecting variant, age, and vaccination history (vaccination brand, number of doses and time since vaccination). We fit our model in a Bayesian framework to upper respiratory tract viral load measurements obtained from cases of Delta and Omicron infections in Singapore, of whom the majority only had one nasopharyngeal swab measurement. With this dataset, we are able to recreate similar trends in URT virus dynamics observed in past within-host modelling studies fitted to longitudinal patient data. We found that Omicron had greater infection potential than Delta, indicating greater propensity to establish infection. Moreover, heterogeneities in infection dynamics across patient subgroups could be recreated by fitting immunity-related parameters as vaccination history-specific, with or without age modification. Our model results are consistent with the notion of immunosenescence in SARS-CoV-2 pathogenesis in elderly individuals, and the issue of waning immunity with increased time since last vaccination. Lastly, vaccination was not found to subdue virus dynamics in Omicron infections as well as it had for Delta infections. This study provides insight into the influence of vaccine-elicited immunity on SARS-CoV-2 within-host dynamics, and the interplay between age and vaccination history. Furthermore, it demonstrates the need to disentangle host factors and changes in pathogen to discern factors influencing virus dynamics. Finally, this work demonstrates a way forward in the study of within-host virus dynamics, by use of viral load datasets including a large number of patients without repeated measurements.

Prediction of atmospheric pollutants in urban environment based on coupled deep learning model and sensitivity analysis.

Accurate and efficient predictions of pollutants in the atmosphere provide a reliable basis for the scientific management of atmospheric pollution. This study develops a model that combines an attention mechanism, convolutional neural network (CNN), and long short-term memory (LSTM) unit to predict the O3 and PM2.5 levels in the atmosphere, as well as an air quality index (AQI). The prediction results given by the proposed model are compared with those from CNN-LSTM and LSTM models as well as random forest and support vector regression models. The proposed model achieves a correlation coefficient between the predicted and observed values of more than 0.90, outperforming the other four models. The model errors are also consistently lower when using the proposed approach. Sobol-based sensitivity analysis is applied to identify the variables that make the greatest contribution to the model prediction results. Taking the COVID-19 outbreak as the time boundary, we find some homology in the interactions among the pollutants and meteorological factors in the atmosphere during different periods. Solar irradiance is the most important factor for O3, CO is the most important factor for PM2.5, and particulate matter has the most significant effect on AQI. The key influencing factors are the same over the whole phase and before the COVID-19 outbreak, indicating that the impact of COVID-19 restrictions on AQI gradually stabilized. Removing variables that contribute the least to the prediction results without affecting the model prediction performance improves the modeling efficiency and reduces the computational costs.

Severity Assessment of COVID-19 Using a CT-Based Radiomics Model.

The coronavirus disease of 2019 (COVID-19) has evolved into a worldwide pandemic. Although CT is sensitive in detecting lesions and assessing their severity, these works mainly depend on radiologists' subjective judgment, which is inefficient in case of a large-scale outbreak. This work focuses on developing a CT-based radiomics model to assess whether COVID-19 patients are in the early, progressive, severe, or absorption stages of the disease. We retrospectively analyzed the CT images of 284 COVID-19 patients. All of the patients were divided into four groups (0-3): early (n = 75), progressive (n = 58), severe (n = 75), and absorption (n = 76) groups, according to the progression of the disease and the CT features. Meanwhile, they were split randomly to training and test datasets with the fixed ratio of 7 : 3 in each category. Thirty-eight radiomic features were nominated from 1688 radiomic features after using select K-best method and the ElasticNet algorithm. On this basis, a support vector machine (SVM) classifier was trained to build this model. Receiver operating characteristic (ROC) curves were generated to determine the diagnostic performance of various models. The precision, recall, and f 1-score of the classification model of macro- and microaverage were 0.82, 0.82, 0.81, 0.81, 0.81, and 0.81 for the training dataset and 0.75, 0.73, 0.73, 0.72, 0.72, and 0.72 for the test dataset. The AUCs for groups 0, 1, 2, and 3 on the training dataset were 0.99, 0.97, 0.96, and 0.93, and the microaverage AUC was 0.97 with a macroaverage AUC of 0.97. On the test dataset, AUCs for each group were 0.97, 0.86, 0.83, and 0.89 and the microaverage AUC was 0.89 with a macroaverage AUC of 0.90. The CT-based radiomics model proved efficacious in assessing the severity of COVID-19.

Recommendations for standardized management of CML patients in the core epidemic area of COVID-19(Multi-center survey results in Hubei Province, China) (preprint)

Background Since late December 2019, the outbreak of the novel coronavirus disease, COVID-19, that began in Wuhan, has become endemic in China and more than 100 countries and regions in the world. There is no report about the prevalence of COVID-19 in CML patients untill now. We aimed to describe the clinical course, outcomes of CML patients with COVID-19 and prevalence of COVID-19 in CML patients. Methods In this multi-center survey, cross-sectional survey, observational study, the clinical data of CML patients with COVID-19 in each center were collected. Simultaneously, an online survey was conducted for information about the CML patients under the management at each center by asking the CML patients to complete a questionnaire,from February 15, 2020 to February 21, 2020. The questionnaire includes demographic data, place of residence, smoking status, CML diagnosis and treatment, comorbidities, combined medications, epidemiological history, symptoms(fever, cough, shortness of breath, etc) during the epidemic. Additional clinical data was collected on respondents suspected or confirmed to have COVID-19. We described and analyzed the prevalence of COVID-19 in CML patients, and focus on the clinical characteristics and outcomes of COVID-19 patients. Data were compared between the CML patients with optimal response and those with non-optimal response. The primary outcome was prevalence of COVID-19 in CML patients, as of Feb 21, 2020. Secondary outcomes included the history of epidemiology of CML patients, the clinical characteristics and outcomes of CML patients with COVID-19 . Findings Of 392 respondents, 223( 56.9%) were males, and 240( 61.2%) were 50 years or younger. Only 10 patients took drugs irregularly due to the influence of the epidemic because of traffic control, pharmacies unable to operate normally, etc. In the history of epidemiology, there were 4 patients with definite contact with COVID-19, of which 3 were remote contact and 1 was close contact. 12 respondents had fever, cough or shortness of breath during the epidemic, 1 case (common type) was confirmed with COVID-19 and cured after treatment. 1 patient was clinically diagnosed and succumbed. 1 of 299 (0.3%) patients with an optimal response was diagnosed with COVID-19. Of the 50 patients who failed to respond to CML treatment or had a poor response, 1 patient (2%) had a clinical diagnosis of COVID-19. Interpretation While the 392 CML respondents required regular referrals to hospitals, they did not have much contact with COVID-19 patients during the outbreak. Patients who failed to achieved an optimal response to CML therapy appear more likely to have a symptomatic infection with SARS-CoV-2. Older patients with comorbidities are at increased risk of death.