DOES COVID-19 INCREASE THE RISK OF DEVELOPING CYSTIC LUNG DISEASE?

SESSION TITLE: Late Breaking Chest Infections Posters SESSION TYPE: Original Investigation Posters PRESENTED ON: 10/18/2022 01:30 pm - 02:30 pm PURPOSE: (1) Assess the characteristics of COVID-19 patients who developed pulmonary cysts, bullae, blebs, and pneumatoceles. (2) Investigate outcomes of patients who developed cystic lung disease from COVID-19. METHODS: A literature search using Pubmed, Cochrane, and Embase was performed for case reports from 2020 to 2022 describing COVID-19 patients who developed lung cysts, bullae, blebs and pneumatoceles. The following data were extracted: patient demographics, presence of underlying lung disease, history of smoking, maximum oxygen requirements during acute illness, imaging findings, complications, and patient mortality. RESULTS: 65 publications (11 case series and 54 case reports) with a total sample size of 76 patients were analyzed. The mean age of patients was 52.2 ± 15.8 years. A majority of the cases were males (n=67, 88.2%). Twelve (15.8%) cases had an underlying lung disease, such as COPD or asthma, and 16 (21.1%) cases had a history of smoking tobacco. We categorized severity of illness based on the levels of oxygen requirement defined as: (1) mild - 0 to 2 liters of oxygen, (2) moderate - greater than 2 liters of oxygen to face mask/venturi mask and (3) severe - high flow nasal cannula, non-invasive ventilation, or mechanical ventilation. The majority of patients (n=40, 52.6%) had severe illness while 7 (9.2%) and 17 (22.4%) presented with mild and moderate disease, respectively. Of the 25 (32.9%) patients who required invasive mechanical ventilation, duration of ventilator days was provided for 14 patients, with a median of 40 days (interquartile range=54). Twenty-one (27.6%) patients were found to have cysts on imaging, 26 (34.2%) were found to have bullae, 3 (3.9%) were found to have blebs, 15 (19.7%) were found to have pneumatoceles, and 11 (14.5%) were found to have more than one of the aforementioned findings. A total of 53 (69.7%) patients developed pneumothorax and 12 (15.8%) developed pneumomediastinum. Seventeen (22.4%) patients were on the mechanical ventilator while pulmonary complications occurred. Additionally, 41 (53.9%) required chest tube placement, 16 (21.1%) required surgical intervention including open thoracotomy or video assisted thoracoscopy. A total of 47 (61.8%) cases reported either resolution of symptoms and complications, or improved imaging findings following interventions. The rate of inpatient mortality was 11.8%. CONCLUSIONS: Patients with severe COVID-19 may have a higher risk for developing cystic lung disease, hence, increasing the risk for complications such as pneumothorax and pneumomediastinum. CLINICAL IMPLICATIONS: Patients who had severe COVID-19 may benefit from closer follow up and serial imaging for early detection of cystic lung disease. DISCLOSURES: No relevant relationships by Kavita Batra No relevant relationships by Rajany Dy No relevant relationships by Christina Fanous No relevant relationships by Wilbur Ji No relevant relationships by Max Nguyen No relevant relationships by Omar Sanyurah

Investigating intention to receive the COVID-19 vaccine and willingness to pay among the general public in Pakistan

The coronavirus (COVID-19) vaccine has become recently available, and to make vaccination campaigns successful, we should increase the acceptability of the COVID-19 vaccine within the public. Thus, this study has been conducted to provide insights into the factors affecting vaccine acceptance and pricing considerations. A both online and paperbased cross-sectional study was conducted from August 1 to August 30, 2020, among the general population of Pakistan. The health belief model (HBM) was used to assess predictors of the intent to receive the vaccine and the willingness to pay (WTP). Descriptive analysis was done, and a chi-squared test was used to assess the demographic association with HBM items. The majority (73.4%) showed a definite/probable intent to receive the vaccine, and marital status and education were significantly associated with getting the vaccine. WTP for a dose of the COVID-19 vaccine was highest for less than 1,000 Pakistani Rupees (PKR) and lowest for 10,001–20,000 PKR. This research indicates that the acceptability of the COVID-19 vaccine in the Pakistani community is satisfactory but the majority of the population want to spend less money to get immunized. © 2022. Ali Hassan Gillani et al. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).

Rapid Perception of Public Opinion in Emergency Events through Social Media

Due to its near-real-time crowdsourcing nature, social media demonstrates a great potential of rapidly reflecting public opinion during emergency events. However, systematic approaches are still desired to perceive public opinion in a rapid and reliable manner through social media. This research proposes two quantitative metrics-the fraction of event-related tweets (FET) and the net positive sentiment (NPS)-to examine the intensity and direction dimensions of public opinion. While FET is modeled through normalizing population size differences, NPS is modeled through a Bayesian-based method to incorporate uncertainty from social media information. To illustrate the feasibility and applicability of the proposed FET and NPS, we studied public opinion on society reopening amid COVID-19 for the entire United States and four individual states (i.e., California, New York, Texas, and Florida). The reflected trends of public opinion have been supported by the reopening policy timeline, the number of COVID-19 cases, and the economy characteristics. This research is expected to assist policy makers in obtaining a prompt understanding of public opinion from the intensity and direction dimensions, thereby facilitating timely and responsive policy making in emergency events.

Estimating public demand following disasters through Bayesian-based information integration

Public demand estimation is essential to effective relief resource distribution following disasters. However, previous studies are incapable of deriving a reliable estimation mainly due to the complexity, dynamicity, and nonlinearity of public demand. This research proposes an innovative data-driven approach to estimate public demand by leveraging sample information, such as social media and surveys. Twitter-based demand percentage (TDP) is designed as the predictor of actual demand percentage, while survey-based demand percentage (SDP) is developed as the ground truth of actual demand percentage. Sampling bias of social media users is removed through a systematic process that comprises the prediction of social media user races/ethnicities and the aggregation of demand percentages. Sampling uncertainty of TDP and SDP is modeled through a Bayesian-based approach that integrates prior knowledge as well as new observations from social media and surveys. The relationship between TDP and SDP is learned through a polynomial model, which facilitates the estimation of future actual demand percentage. To illustrate the feasibility and applicability of the proposed approach, public demand for COVID-19 vaccines in the US is estimated. Results demonstrate that the TDP is a strong predictor of actual demand percentage. This research novelly takes the advantages of sample information-the near-real-time nature of social media and the high reliability of surveys-to achieve a reliable and rapid estimation of public demand following disasters.

Research on online learning of higher vocational students under the background of preventing the spread of COVID-19

During the epidemic of the COVID-19(coronavirus disease 2019), the Ministry of Education requires colleges and universities to implement online teaching to achieve "no suspension of classes". In this article, by using a questionnaire survey, it investigates and studies the teaching hardware and software, learning status, learning satisfaction, and main problems faced by students during the process of online learning. Suggestions such as flexible response to students' objective situation, paying attention to students' learning status and learning effects, and optimization of teaching platform construction are proposed. © 2021 ACM.

Comparison of the clinical and CT features between COVID-19 and H1N1 influenza pneumonia patients in Zhejiang, China.

OBJECTIVE: To explore the different clinical and CT features distinguishing COVID-19 from H1N1 influenza pneumonia. PATIENTS AND METHODS: We compared two independent cohorts of COVID-19 pneumonia (n=405) and H1N1 influenza pneumonia (n=78), retrospectively. All patients were confirmed by RT-PCR. Four hundred and five cases of COVID-19 pneumonia were confirmed in nine hospitals of Zhejiang province, China from January 21 to February 20, 2020. Seventy-eight cases of H1N1 influenza pneumonia were confirmed in our hospital from January 1, 2017 to February 29, 2020. Their clinical manifestations, laboratory test results, and CT imaging characteristics were compared. RESULTS: COVID-19 pneumonia patients showed less proportions of underlying diseases, fever and respiratory symptoms than those of H1N1 pneumonia patients (p<0.01). White blood cell count, neutrophilic granulocyte percentage, C-reactive protein, procalcitonin, D-Dimer, and lactate dehydrogenase in H1N1 pneumonia patients were higher than those of COVID-19 pneumonia patients (p<0.05). H1N1 pneumonia was often symmetrically located in the dorsal part of inferior lung lobes, while COVID-19 pneumonia was unusually showed as a peripheral but non-specific lobe distribution. Ground glass opacity was more common in COVID-19 pneumonia and consolidation lesions were more common in H1N1 pneumonia (p<0.01). COVID-19 pneumonia lesions showed a relatively clear margin compared with H1N1 pneumonia. Crazy-paving pattern, thickening vessels, reversed halo sign and early fibrotic lesions were more common in COVID-19 pneumonia than H1N1 pneumonia (p<0.05). Pleural effusion in COVID-19 pneumonia was significantly less common than H1N1 pneumonia (p<0.01). CONCLUSIONS: Compared with H1N1 pneumonia in Zhejiang, China, the clinical manifestations of COVID-19 pneumonia were more concealed with less underlying diseases and slighter respiratory symptoms. The more common CT manifestations of COVID-19 pneumonia included ground-glass opacity with a relatively clear margin, crazy-paving pattern, thickening vessels, reversed halo sign, and early fibrotic lesions, while the less common CT manifestations of COVID-19 pneumonia included consolidation and pleural effusion.

An early study on intelligent analysis of speech under COVID-19: Severity, sleep quality, fatigue, and anxiety

The COVID-19 outbreak was announced as a global pandemic by the World Health Organisation in March 2020 and has affected a growing number of people in the past few weeks. In this context, advanced artificial intelligence techniques are brought to the fore in responding to fight against and reduce the impact of this global health crisis. In this study, we focus on developing some potential use-cases of intelligent speech analysis for COVID-19 diagnosed patients. In particular, by analysing speech recordings from these patients, we construct audio-only-based models to automatically categorise the health state of patients from four aspects, including the severity of illness, sleep quality, fatigue, and anxiety. For this purpose, two established acoustic feature sets and support vector machines are utilised. Our experiments show that an average accuracy of.69 obtained estimating the severity of illness, which is derived from the number of days in hospitalisation. We hope that this study can foster an extremely fast, low-cost, and convenient way to automatically detect the COVID-19 disease. © 2020 ISCA

Methods and techniques to improve international learning experiences online

International courses in physical classroom tend to be infeasible under the influence of epidemic diseases, such as the COVID-19. Consequently, the transition of teaching activities to online events, which is advantageous in reducing transportation and other expenses, has been acknowledged as an effective and practical solution. However, it also brings new challenges to the teaching in quarantine for vocational education and training, because most of learners at their working places may need to do other things in the online environment during their online class time, such as temporary works, attending meetings and receiving instant messages. In addition, they are usually from different cultures and time zones. Therefore, it is reasonable to create online engaging learning experiences with some careful planning. Taking the International Distance Training Course on Short-term Climate Monitoring and Prediction in Disaster Prevention and Mitigation (from 17th to 31st, May, 2020) as an example, this paper introduces some relevant practical instructional designs, collaborative methods and interactive techniques for improving learning experiences online, and analyses their effects. © 2020, Springer Nature Singapore Pte Ltd.