Pathways to post-capitalist tourism

Potential to identify and cultivate forms of post-capitalism in tourism development has yet to be explored in depth in current research. Tourism is one of the world's largest industries, and hence a powerful global political and socio-economic force. Yet numerous problems associated with conventional tourism development have been documented over the years, problems now greatly exacerbated by impacts of the ongoing COVID-19 pandemic. Calls for sustainable tourism development have long sought to address such issues and set the industry on a better course. Yet such calls tend to still promote continued growth as the basis of the tourism industry's development, while mounting demands for "degrowth" suggest that growth is itself the fundamental problem that needs to be addressed in discussion of sustainability in tourism and elsewhere. This critique asserts that incessant growth is intrinsic to capitalist development, and hence to tourism's role as one of the main forms of global capitalist expansion. Touristic degrowth would therefore necessitate postcapitalist practices aiming to socialise the tourism industry. While a substantial body of research has explored how tourism functions as an expression of a capitalist political economy, thus far no research has systematically explored what post-capitalist tourism might look like or how to achieve it. Applying Erik Olin Wright's innovative typology for conceptualizing different forms of post-capitalism as components of an overarching strategy for "eroding capitalism" to a series of illustrative allows for exploration of their potential to contribute to an analogous strategy to similarly "erode tourism" as a quintessential capitalist industry.

Use of Voluntary Cough Sounds and Deep Learning for Pulmonary Disease Screening in Low-Resource Areas

In low-resource areas, pulmonary diseases are often misdiagnosed or underdiagnosed due to a lack of trained clinical staff and diagnostic lab equipment (e.g. spirometry, DLCO). In these settings, traditional methods of pulmonary disease screening often include a lengthy questionnaire (>30 questions) and stethoscope auscultation. Unfortunately, such tools are not appropriate for general practitioner (GP) doctors or community health workers who have little time or experience diagnosing pulmonary disease. We propose a computer-based deep learning algorithm that could enable rapid screening of the most common pulmonary diseases (COPD, Asthma, and respiratory infection (COVID-19)) using voluntary cough sounds alone. Using a dataset of 348 cough recordings, raw cough recordings were segmented into individual coughs and converted to Mel Spectrogram images. We trained two types of models for comparison, binary and multi-class, using transfer learning with VGG19. The resulting Receiver Operating Characteristic (ROC) curves and the Area Under Curve (AUC) accuracy for each model was calculated to evaluate performance. Binary AUC accuracies were 0.73, 0.70, 0.87, and 0.70 for healthy, asthma, COPD, and COVID-19 respectively, while multi-class AUC accuracies were 0.78, 0.67, 0.95, 0.70. This demonstrates good potential for creating a simple low-cost screening tool that is fast to administer. Future versions of the model will use ongoing data collection to expand to more diseases including tuberculosis and pneumonia. © 2022 IEEE.

Socialisation at scale: Post-capitalist tourism in a post-COVID-19 world

This chapter explores the potential to “scale up” socialisation of the global tourism industry in the wake of the COVID-19 crisis. Tourism is one of the world’s largest industries, and hence a powerful global political and socio-economic force. Yet numerous problems associated with conventional tourism development are now greatly exacerbated by the impacts of the ongoing COVID-19 pandemic. Longstanding calls for sustainable tourism development to address such issues still tend to promote continued growth as the basis of the tourism industry’s development, while mounting demands for “degrowth” suggest that growth is itself the fundamental problem that needs to be addressed in discussion of sustainability in tourism and elsewhere. Given that incessant growth is intrinsic to capitalist development, pursuing touristic degrowth would necessarily entail post-capitalist practices aiming to socialise the tourism industry. Recent calls to foreground socialisation in tourism development largely focus on community-level initiatives. While this is important, the bulk of the tourism industry remains translocal in scale. We therefore apply a set of principles for conceptualising post-capitalist and degrowth-oriented tourism development to a series of case studies at different levels to conceptualise potential to scale up socialisation in a post-COVID-19 world within an overarching strategy of “eroding capitalism”. © 2022 selection and editorial matter, Freya Higgins-Desbiolles, Adam Doering and Bobbie Chew Bigby.

Low Incidence of COVID-19 Infection in Patients with Acute Myeloid Leukemia Undergoing Reduced Intensity/Venetoclax Based Treatment: Initial Results of the PACE Prospective Clinical Study from the UK Trials Acceleration Program

The impact of Coronavirus disease 2019 (COVID-19) on outcomes in patients with cancer remains unclear. Acute Myeloid Leukemia (AML)/high-risk myelodysplasia (MDS) are common hematological malignancies resulting in profound immunosuppression, which is exacerbated by intensive and less-intensive chemotherapy. Importantly, venetoclax based regimens have been increasingly used during the pandemic as a strategy to reduce patient hospitalization however, there is little information concerning the impact of such regimens on COVID-19 infection rates. We therefore opened a prospective clinical study (PACE), at the start of the current pandemic in April 2020 to characterize the risk of COVID-19 infection in patients with AML/MDS-EB2 receiving intensive or non-intensive treatment, including patients treated with venetoclax-based regimens. The primary aim was to determine the incidence of COVID-19 in patients with AML /MDS-EB2 including both, prior to study entry and during treatment until 4 weeks after the last cycle of treatment. Secondary aims were to: characterize the presentation of COVID-19;define the severity and type of both non-COVID-19 and COVID-19 infections;and undertake an exploratory analysis to quantify the incidence of COVID-19 infection in patients receiving (less-intensive) venetoclax based regimens. All analysis conducted to date has been descriptive. 211/230 recruited patients had full treatment histories available, of whom 116 patients received intensive chemotherapy and 95 low intensity regimens. 48 patients received a venetoclax-based regimen. The median age of the non-intensive treatment arm was 72 years;(range 19.1-86.5) and of the intensive arm was 59 years (range 16.1-76.1). There were more cases of secondary AML and relapsed disease in the non-intensive arm as compared to the intensive arm. 25/226 evaluable patients tested positive for COVID-19 as defined by positive SARS-CoV2 PCR test, 10 with a prior diagnosis at study entry and 15 tested positive during the study. The incidence of COVID-19 infection for patients with AML/MDS-EB2 was 11.1% (90%CI: 7.8%-15.1%) (Table). A lower proportion of patients (n=6/91 6.6%) undergoing non-intensive treatment suffered COVID-19 as compared to those undergoing more intensive chemotherapy regimens (n=19/116, 16.4%). Specifically, only 3/48 (6.3%) patients undergoing a venetoclax regimen were infected with SARS-CoV2. The most common presenting symptoms of COVID-19 in this study, regardless of the intensity of chemotherapy, was fever and cough with 6/25 patients asymptomatic. The risk of death at 30 days following study entry in patients who had prior COVID-19 infection or who contracted COVID-19 during this period was 13.6%, compared to 3.9% in the overall cohort without COVID-19 infection. There was a lower incidence of non-COVID-19 related infections in patients receiving venetoclax-based regimens, n=43 infections in 24 (50.0%) of patients;with 313 infections in 94 (81%) of intensively treated patients. The overall occurrence of non-COVID-19 infection in the non-intensive arm was 87 infections in 50 (54.9%) patients. Our multi-center study provides real-world estimates for the incidence and presentation of COVID-19 infection in a cohort of patients with AML/MDS-EB2, and indicates a higher risk of death at 30 days in patients with prior COVID-19 infection prior to, or during treatment. Venetoclax based, and other non-intensive, regimens, increasingly implemented during the pandemic, to minimize patient exposure and reduce usage of hospital beds, appeared to be associated with a low incidence of COVID-19. Further follow-up will be required to understand the long-term impact of this strategy. Analysis of immune responses to COVID-19 infection and vaccination is on-going. Acknowledgments: This study was funded by Cure Leukaemia under the Trials Acceleration Program (TAP), and grants from BMS and Blood Cancer UK. [Formula presented] Disclosures: Loke: Novartis: Other: Travel;Janssen: Honoraria;Amgen: Honoraria;Pfizer: Honoraria;Daichi Sankyo: Other: Travel. K apper: Pfizer: Consultancy, Speakers Bureau;Astellas: Ended employment in the past 24 months, Speakers Bureau;Jazz: Consultancy, Speakers Bureau;Novartis: Consultancy, Research Funding, Speakers Bureau. Khan: Abbvie: Honoraria;Astellas: Honoraria;Takeda: Honoraria;Jazz: Honoraria;Gilead: Honoraria;Novartis: Honoraria. Dillon: Amgen: Other: Research support (paid to institution);Astellas: Consultancy, Other: Educational Events, Speakers Bureau;Menarini: Membership on an entity's Board of Directors or advisory committees;Novartis: Membership on an entity's Board of Directors or advisory committees, Other: Session chair (paid to institution), Speakers Bureau;Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: educational events;Jazz: Other: Education events;Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Research Support, Educational Events;Shattuck Labs: Membership on an entity's Board of Directors or advisory committees. Culligan: AbbVie Ltd: Honoraria, Speakers Bureau;Celgene Ltd: Honoraria, Speakers Bureau;Gilead: Honoraria, Speakers Bureau;Jazz Pharma: Honoraria, Speakers Bureau;Takeda UK Ltd: Honoraria, Speakers Bureau. McMullin: Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees, Other: clinical trial support, Research Funding;Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau;AbbVie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau;Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau;AOP Orphan: Research Funding, Speakers Bureau. Murthy: Abbvie: Other: support to attend educational conferences. Craddock: Novartis Pharmaceuticals: Other: Advisory Board;Celgene/BMS: Membership on an entity's Board of Directors or advisory committees, Research Funding.

COVID-19 Patient Outcome Prediction Using Selected Features from Emergency Department Data and Feed-Forward Neural Networks

The severity of COVID-19 varies dramatically, ranging from asymptomatic infection to severe respiratory failure and death. Currently, few prognostic markers for disease outcomes exist, impairing patient triaging and treatment. Here, we train feed-forward neural networks on electronic health records of 819 confirmed SARS-CoV-2 positive patients admitted to a two-site NHS Trust hospital in London, England. To allow early risk assessment, the models ingest data collected in the emergency department (ED) to predict subsequent admission to intensive care, need for mechanical ventilation and in-hospital mortality. We apply univariate selection and recursive feature elimination to find the minimal subset of clinical variables needed for accurate prediction. Our models achieve AUC-ROC scores of 0.78 to 0.87, outperforming standard clinical risk scores. This accuracy is reached with as few as 13% of clinical variables routinely collected within the ED, which increases the practical applicability of such algorithms. Hence, state-of-the-art neural networks can predict severe COVID-19 accurately and early from a small subset of clinical variables. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.