Hawker Centres: A Social Space Approach to Promoting Community Wellbeing

This article sets out to examine how the use of social spaces, namely hawker centres, has contributed to community well-being during the Covid-19 pandemic. Using an extensive thematic analysis of online conversations, we have identified that the use of social spaces can have a positive influence on individual, relational and social wellbeing. Access to social spaces during stressful events contributes to the feeling of normalcy, supports routines and structured activities, encourages responsible behaviours, facilitates social connectedness, and helps maintain community resilience. We present a new framework for urban social space characterisation containing three dimensions: coaction, copresence, and colocation (the three Cs). Here, coaction is associated with better visibility of community practices, copresence enhances the sense of con-nectedness, and colocation is concerned with the use of spatial design factors for influencing movement and interactions. The framework is central to our understanding of social space and its impact on wellbeing. Underpinning the three Cs is the notion of the integration of policy, community wellbeing, and various urban agendas. The findings were considered in terms of their relevance for social space development in Singapore. © 2022 by the author(s);licensee Cogitatio (Lisbon, Portugal).

Post COVID-19: cautious or courageous travel behaviour?

This paper focuses on post-pandemic travel behaviour and examines the relationship between destination-risk image and pre-travel behaviour using health-protective behaviour and media engagement as mediators. It empirically tests the model proposed by Bhati et al. The researchers adopt a pragmatist paradigm and utilise mixed methods to develop and test the adapted PMT framework. The findings confirm that, in the COVID-19 pandemic context, destination health-risk image has an effect on pre-travel behaviour via media engagement and health protective behaviour. Respondents preferred destinations that handled the pandemic crisis effectively, implemented hygiene and safety protocols, and had robust healthcare systems. © 2022 Asia Pacific Tourism Association.

IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads

The drug discovery process currently employed in the pharmaceutical industry typically requires about 10 years and $2-3 billion to deliver one new drug. This is both too expensive and too slow, especially in emergencies like the COVID-19 pandemic. In silico methodologies need to be improved both to select better lead compounds, so as to improve the efficiency of later stages in the drug discovery protocol, and to identify those lead compounds more quickly. No known methodological approach can deliver this combination of higher quality and speed. Here, we describe an Integrated Modeling PipEline for COVID Cure by Assessing Better LEads (IMPECCABLE) that employs multiple methodological innovations to overcome this fundamental limitation. We also describe the computational framework that we have developed to support these innovations at scale, and characterize the performance of this framework in terms of throughput, peak performance, and scientific results. We show that individual workflow components deliver 100 × to 1000 × improvement over traditional methods, and that the integration of methods, supported by scalable infrastructure, speeds up drug discovery by orders of magnitudes. IMPECCABLE has screened ∼1011 ligands and has been used to discover a promising drug candidate. These capabilities have been used by the US DOE National Virtual Biotechnology Laboratory and the EU Centre of Excellence in Computational Biomedicine. © 2021 ACM.

Scalable HPC & AI infrastructure for COVID-19 therapeutics

COVID-19 has claimed more than 2.7 × 106 lives and resulted in over 124 × 106 infections. There is an urgent need to identify drugs that can inhibit SARS-CoV-2. We discuss innovations in computational infrastructure and methods that are accelerating and advancing drug design. Specifically, we describe several methods that integrate artificial intelligence and simulation-based approaches, and the design of computational infrastructure to support these methods at scale. We discuss their implementation, characterize their performance, and highlight science advances that these capabilities have enabled. © 2021 ACM.

Risk Index-Based Ventilator Prediction System for COVID-19 Infection

The current epidemic of the corona virus disease (COVID-19) in 2019 comprises a general wellbeing crisis of worldwide concern. Ongoing research shows that factors similar to ımmunity, environmental effect, age, heart and diabetes are significant supporters of this chronic infections. In this paper, a combined machine learning model and rule-based framework is proposed to offer medical decision support. The proposed system consists of a robust machine learning model utilizing gradient boosted tree technique to calculate CRI index for patients suffering from COVID-19 disease. This index is a measurement of COVID-19 patient mortality risk. Based on CRI index system predicts required number of ventilators in forthcoming days. The suggested model is trained and evaluated using a real-time dataset of 5440 COVID-19 positive patient obtained from John Hopkins University, World Health Organization and dataset of Indian COVID-19 patients obtained from open government data (OGD) platform India. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Prediction of COVID-19 Outbreak in India adopting bhilwara model of Containment

The epidemic of coronavirus disease-2019 (COVID-19) establishes a medical emergency of worldwide concern with an exceptionally high danger of spread and affect the entire worldwide. In India, there has been a steady ascent in the infection with 20,080 cases on April 21 even after a countrywide lockdown. Bhilwara lockdown &containment model flattens the infection curve of COVID-19 cases just within 10 days of initial spread. This paper has described the Bhilwara model and compare the model with India COVID-19 outbreak lockdown along with a prediction for a reduction in the number of upcoming cases with its implementation. In experimentation, the Bhilwara model is simulated using 3rd-degree polynomial curve fitting techniques, and the mean growth rate of infection is calculated on the COVID-19 spread curve for a group of days depicting the effect of policies defined by Bhilwara administration. Using calculated mean growth rate, COVID-19 spread is predicted with 3rd-degree polynomial regression utilizing a dataset of all states of India. Results found that with the implementation of the Bhilwara model all over India, the infection transmission rate is reduced to a significant level. Results motivate government authorities to implement new policies and adaption of the Bhilwara model of containment to flatten the COVID-19 outbreak curve. © 2020 IEEE.