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The manner in which individuals worldwide shared art during the most challenging months of the COVID-19 pandemic stands as one of the most significant instances of creative social resistance in recent history. As a collective tool of resistance against emotional trauma, and as a means to foster a sense of community and well-being, the study of this phenomenon offers a compelling avenue for research into creativity and its social functions. This paper presents a descriptive case study of a successful 50-day collective experience within a neighborhood community in Madrid, Spain, during a period when the city, as a notably exceptional case study for research, bore one of the heaviest burdens of COVID-19 in the world. Data were gathered through in-depth personal interviews and direct observations. Applying a connected approach drawing on the fields of the Sociology of Art and Media Studies, three key findings emerge: (1) participants emphasized shared live artistic performances as the primary catalyst for fostering a sense of community, collective resilience, and overall well-being; (2) their sense of togetherness was further bolstered by digital and media support, as recordings of live performances were shared with loved ones living elsewhere, as well as with journalists and on social networks. This network of communication played a pivotal role in connecting individuals; (3) the combined efforts of both initiatives contributed to the development of a more positive individual and shared narrative surrounding the crisis. [ABSTRACT FROM AUTHOR]

• Socio-demographic attributes and daily habits influence the COVID-19 infection. • Madrid's measures make public transport a relatively safe option during the pandemic. • High crowding increases the risk of COVID-19 on certain public transport services. The COVID-19 pandemic has changed people's mobility patterns, increasing the preference for private modes and reducing the public transportation demand. Most scientific contributions have studied the role of mobility levels in the spread of the virus and the influence of public transport on COVID-19 infections, but ignoring the importance of individual-level variables potentially affecting COVID-19 infection, such as daily habits. This paper analyses the relationship between the probability of being infected by COVID-19 and using public transport through a survey data analysis, taking Madrid (Spain) as the case study. This research uses a survey campaign with more than 15,000 responses, capturing socio-demographic aspects, COVID-19 infections, daily habits, and mobility patterns with high risk of COVID-19 infection. Through a multilevel probit model, this paper explores the extent to which a higher use of public transport is related to a greater likelihood of COVID-19 infection. The results suggest a relationship, although not very strong, between the probability of infection and the conjunction of higher frequency of use of metro services and level of crowding during the trip, whereas the use of bus services and travel time within the vehicle do not appear to affect. [ABSTRACT FROM AUTHOR]

In most big cities, public transports are enclosed and crowded spaces. Therefore, they are considered as one of the most important triggers of COVID-19 spread. Most of the existing research related to the mobility of people and COVID-19 spread is focused on investigating highly frequented paths by analyzing data collected from mobile devices, which mainly refer to geo-positioning records. In contrast, this paper tackles the problem by studying mass mobility. The relations between daily mobility on public transport (subway or metro) in three big cities and mortality due to COVID-19 are investigated. Data collected for these purposes come from official sources, such as the web pages of the cities' local governments. To provide a systematic framework, we applied the IBM Foundational Methodology for Data Science to the epidemiological domain of this paper. Our analysis consists of moving averages with a moving window equal to seven days so as to avoid bias due to weekly tendencies. Among the main findings of this work are: a) New York City and Madrid show similar distribution on studied variables, which resemble a Gauss bell, in contrast to Mexico City, and b) Non-pharmaceutical interventions don't bring immediate results, and reductions to the number of deaths due to COVID are observed after a certain number of days. This paper yields partial evidence for assessing the effectiveness of public policies in mitigating the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

This paper combines historical and contemporary sources to examine 'epidemic urban planning' from the first decades of the 20th century through to the present day. It considers how infamous early 20th-century epidemics triggered the development of several urban regulations that profoundly shaped the city's future. To reduce the risk of contagion in bourgeois space, the city began displacing and spatially segregating the urban poor, leading to deprived neighbourhoods in the city's suburbs. The social and urban structure of these deprived, 'vulnerable' neighbourhoods remains to this day. Madrid was also greatly impacted by the COVID-19 crisis, and the initial distribution of COVID geographies seemed to reflect these historical legacies. Epidemic-influenced segregation kept wealthy neighbourhoods relatively safe during the first waves of the COVID-19 pandemic, concentrating the disease in poorer areas. [ABSTRACT FROM AUTHOR]

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The COVID-19 virus continues to generate waves of infections around the world. With major areas in developing countries still lagging behind in vaccination campaigns, the risk of new variants that can cause re-infections worldwide makes the monitoring and forecasting of the evolution of the virus a high priority. Having accurate models able to forecast the incidence of the spread of the virus provides help to policymakers and health professionals in managing the scarce resources in an optimal way. In this paper, a new machine learning model is proposed to forecast the spread of the virus one-week ahead in a geographic area which combines mobility and COVID-19 incidence data. The area is divided into zones or districts according to the location of the COVID-19 measuring points. A traffic-driven mobility estimate among adjacent districts is proposed to capture the spatial spread of the virus. Traffic-driven mobility in adjacent districts will be used together with COVID-19 incidence data to feed a new deep learning LSTM-based model which will extract patterns from mobility-modulated COVID-19 incidence spatiotemporal data in order to optimize one-week ahead estimations. The model is trained and validated with open data available for the city of Madrid (Spain) for 3 different validation scenarios. A baseline model based on previous literature able to extract temporal patterns in COVID-19 incidence time series is also trained with the same dataset. The results show that the proposed model, based on the combination of traffic and COVID-19 incidence data, is able to outperform the baseline model in all the validation scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Revista Española de Quimioterapia is the property of Sociedad Espanola de Quimioterapia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper examines the effect of the COVID-19 induced lockdown upon six pollutants, CO, NO, NO2, PM10, PM2.5, and O3, in the Spanish community of Madrid. The paper relies on clustering methods and multiple regression techniques to control for a battery of potential confounding factors. The results show that the nationwide lockdown, decreed on 13 March by the Spanish government, exerted a statistically significant effect upon most pollution indicators. The estimates range from approximately −82% (NO and NO2) to −3% (CO). Reversely, the COVID-19 induced lockdown raised O3 levels by an average of 20%. By using data from 43 stations spread out among the region, the paper provides a local level analysis. This analysis reveals substantial differences across areas and across pollutants. This observation indicates that any successful approach to improve air quality in the region must be multidimensional. [ABSTRACT FROM AUTHOR]

While the COVID-19 pandemic is still in progress, being under the fifth COVID-19 wave in Madrid, over more than one year, Spain experienced a four wave pattern. The transmission of SARS-CoV-2 pathogens in Madrid metropolitan region was investigated from an urban context associated with seasonal variability of climate and air pollution drivers. Based on descriptive statistics and regression methods of in-situ and geospatial daily time series data, this study provides a comparative analysis between COVID-19 waves incidence and mortality cases in Madrid under different air quality and climate conditions. During analyzed period 1 January 2020–1 July 2021, for each of the four COVID-19 waves in Madrid were recorded anomalous anticyclonic synoptic meteorological patterns in the mid-troposphere and favorable stability conditions for COVID-19 disease fast spreading. As airborne microbial temporal pattern is most affected by seasonal changes, this paper found: 1) a significant negative correlation of air temperature, Planetary Boundary Layer height, and surface solar irradiance with daily new COVID-19 incidence and deaths; 2) a similar mutual seasonality with climate variables of the first and the fourth COVID-waves from spring seasons of 2020 and 2021 years. Such information may help the health decision makers and public plan for the future. • Climate factors and air quality can trigger SARS-CoV-2 viral infection transmission. • Synoptic atmospheric circulation patterns are related to COVID-19 waves start-up. • 1st and 4th COVID-19 waves have similar correlations with environmental variables. • COVID-19 and climate driving factors have mutual seasonality patterns in Madrid. • Early health strategies are essential to control the COVID-19 pandemic in Madrid. [ABSTRACT FROM AUTHOR]

Introduction: The paper will describe the experience as consultation-liaison psychiatrist during the Covid 19 Pandemic in a private hospital in Madrid, what we have learned and its implications given the considerable and increasing interest in European Consultation-Liaison research. Objectives: Following the request of one of the internal medicine department doctors the service was initially provided for patients admitted with the infection but very quickly included relatives and also the hospital staff. Methods: Patients were offered a telephone consultation that in most cases took place on a daily basis. Referrals where made by a doctor, some of them were locums due to the increasing demands of the service since patients from public hospitals were also admitted. Relatives were also referred by doctors and the frequency was more varied, depending on their needs. Members of the multidisciplinary team referred themselves.. Results: Patients and their families felt that the telephone consultation was useful to them. The work with some members of the staff is ongoing and will continue given the toxic levels of stress that they had to face and the changes taking place at the institution at the time. Conclusions: The COVID-19 pandemic and the short and long term consequences that will follow will increase our understanding the breadth and depth of Consultation-Liaison Psychiatry and the broad perspective required for a comprehensive evaluation and treatment of patients. My experience as psychoanalytic psychotherapist and organizational consultant proved most helpful. [ABSTRACT FROM AUTHOR]