Understanding the shared working spaces: a Geography of Co-working supply in Delhi.

The present paper investigates the location pattern of co-working spaces in Delhi which is absent in the existing body of knowledge. Delhi is a political, administrative, educational, scientific and innovation capital that accommodates many co-working spaces in India. We developed Ordinary least squares (OLS) and geographically weighted regression (GWR) models to understand the associations of co-working spaces of digital labourers with other urban socio-economic, services and lifestyle variables in Delhi using secondary data for 117 coworking locations in 280 municipal wards of NCT-Delhi. Model diagnostic suggested that the GWR model provides additional information regarding geographical distribution of coworking spaces, and density of bars, median house rent, fitness centres, metro train stations, restaurants, cinemas, cafés, and creative enterprises are statistically significant parameters to estimate them. The importance of coworking spaces has increased in the post-disaster period, so this study informs public policies to benefit people and companies who choose coworking routes, and recommends urban planners, developers, and real-estate professionals to consider the proximity of creative industries in planning and developing coworking spaces in the future. Also, in the post COVID-19 period, to increase local jobs and long-term place sustainability, a localised policy intervention for coworking spaces in Delhi is highly recommended.

Evaluation of Optimal Mechanical Ventilation Strategies for Schools for Reducing Risks of Airborne Viral Infection

Ventilation systems are one of the most effective strategies to reduce the risk of viral infection transmission in buildings. However, insufficient ventilation rates in crowded spaces, such as schools, would lead to high risks of infection transmission. On the other hand, excessive ventilation rates might significantly increase cooling energy consumption. Therefore, energy-efficient control methods, such as Demand Control Ventilation systems (DCV), are typically considered to maintain acceptable indoor air quality. However, it is unclear if the DCV-based controls can supply adequate ventilation rates to minimize the probability of infection (POI) in indoor spaces. This paper investigates the benefits of optimized ventilation strategies, including conventional mechanical systems (MV) and DCV, in reducing the POI and cooling energy consumption through a detailed sensitivity analysis. The study also evaluates the impact of the ventilation rate, social distancing, and number of infectors on the performance of the ventilation systems. A coupling approach of a calibrated energy model of a school building in Jeddah, KSA, with a validated Wells–Riley model is implemented. Based on the findings of this study, proper adjustment of the DCV set point is necessary to supply adequate ventilation rates and reduce POI levels. Moreover, optimal values of 2 ACH for ventilation rate and 2 m for social distance are recommended to deliver acceptable POI levels, cooling energy use, and indoor CO2 concentration for the school building. Finally, this study confirms that increasing the ventilation rate is more effective than increasing social distancing in reducing the POI levels. However, this POI reduction is achieved at the cost of a higher increase in the cooling energy.

Prevalence of venous thromboembolism in patients undergoing diagnostic venous ultrasound during the first SARS-CoV-2 pandemic.

Background: Venous thromboembolism appears to be associated with severe COVID-19 infection than in those without it. However, this varies considerably depending on the cohort studied. The aims of this single-centre, multi-site retrospective cross-sectional study were to assess the number of all venous scans performed in the first month of pandemic in a large university teaching hospital, to evaluate the incidence of deep venous thrombosis (DVT), and assess the predictive ability of the clinical information available on the electronic patient record in planning work-up for DVT and prioritising ultrasound scans. Patients and methods: All consecutive patients undergoing venous ultrasound for suspected acute DVT between 1st of March and 30th of April 2020 were considered. Primary outcome was the proportion of scans positive for DVT; the secondary outcomes included association of a positive SARS-CoV-2 PCR test, demographic, clinical factors, and Wells scores. Results: 819 ultrasound scans were performed on 762 patients across the Trust in March and April 2020. This number was comparable to the corresponding pre-pandemic cohort from 2019. The overall prevalence of DVT in the studied cohort was 16.1% and was higher than before the pandemic (11.5%, p=.047). Clinical symptoms consistent with COVID-19, irrespective of the SARS-CoV-2 PCR test result (positive_COVID_PCR OR 4.97, 95%CI 2.31-10.62, p<.001; negative_COVID_PCR OR 1.97, 95%CI 1.12-3.39, p=.016), a history of AF (OR 0.20, 95%CI 0.03-0.73, p=.037), and personal history of venous thromboembolism (VTE) (OR 1.95, 95%CI 1.13-3.31, p=.014), were independently associated with the diagnosis of DVT on ultrasound scan. Wells score was not associated with the incidence of DVT. Conclusions: Amongst those referred for the DVT scan, SARS-CoV-2 PCR test was associated with an increased risk of VTE and should be taken into consideration when planning DVT work-up and prioritising diagnostic imaging. We postulate that the threshold for imaging should possibly be lower.

Detection of COVID-19 Infection in CT and X-ray images using transfer learning approach.

BACKGROUND: The infection caused by the SARS-CoV-2 (COVID-19) pandemic is a threat to human lives. An early and accurate diagnosis is necessary for treatment. OBJECTIVE: The study presents an efficient classification methodology for precise identification of infection caused by COVID-19 using CT and X-ray images. METHODS: The depthwise separable convolution-based model of MobileNet V2 was exploited for feature extraction. The features of infection were supplied to the SVM classifier for training which produced accurate classification results. RESULT: The accuracies for CT and X-ray images are 99.42% and 98.54% respectively. The MCC score was used to avoid any mislead caused by accuracy and F1 score as it is more mathematically balanced metric. The MCC scores obtained for CT and X-ray were 0.9852 and 0.9657, respectively. The Youden's index showed a significant improvement of more than 2% for both imaging techniques. CONCLUSION: The proposed transfer learning-based approach obtained the best results for all evaluation metrics and produced reliable results for the accurate identification of COVID-19 symptoms. This study can help in reducing the time in diagnosis of the infection.

A qualitative geographical information system interpretation of mobility and COVID-19 pandemic intersection in Uttar Pradesh, India.

Mobility of individuals and their physical social networks are the root causes for the spread of current coronavirus pandemic. We propose here a method of visualizing the spatial and chronological aspects of the spread of this virus based on geographical information systems (GIS) and Gephi graphs. For this approach we used qualitative data from newspaper reports and prepared layouts varying from macro to micro scales that show that this approach can enrich traditional GIS approaches, thereby assisting mobility planners and policymakers.

Modelling and analysis of COVID-19 epidemic in India

COVID-19 epidemic is declared as the public health emergency of international concern by the World Health Organisation in the last week of March 2020. This disease originated from China in December 2019 has already caused havoc around the world, including India. The first case in India was reported on 30th January 2020, with the cases crossing 4 million on the day paper was written. This pandemic has caused more than 80,000 fatalities with 3 million recoveries. Strict lockdown of the nation for two months, immediate isolation of infected cases and app-based tracing of infected are some of the proactive steps taken by the authorities. For a better understanding of the evolution of COVID-19 in the world, study on evolution and growth of cases in India could not be avoided. To understand the same, one of the compartment model: Susceptible-Infectious-Quarantined-Recovered (SIQR) is used. Recovery rate and doubling rate of the total reported positive cases in the country had crossed 75% and 25 days, respectively. It is also estimated that there is a strong positive correlation between testing rate and detection of new cases up to 6 million tests per day. Using the SIQR modelling effective reproduction number, epidemic doubling rate and infected to quarantined ratio is determined to check the temporal evolution of the pandemic in the country. Effective reproduction number that was at its peak during first half of the April is gradually converging to 1. It is also estimated using this model that with each detected cases in India, there could be 10-50 undetected cases. Like every mathematical model, this model also has some assumptions. To make this model more robust, a technique with weighted parameter that can avoid a person with a strong immune system to be equally vulnerable to the infection, can be worked out. Machine learning algorithms can also be used to train our model with the data of other countries to make the analysis and prediction more precise and accurate.