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The lockdown of March and April 2020 declared by Spanish authorities in the Valencian Region to bending the Covid-19 curve, caused a drastic reduction of the economic activity and a severe limitation of mobility. People were asked to stay at home as much as possible. Education and administrative centers, as well as restaurants, theaters, sport arenas, etc., were closed. Work at conventional workplaces was prohibited for people who could tele-work, and students were compelled to attend classes on-line. Such limitation of mobility and spending so many time at home, could affect the wellbeing of people. The objective of the present paper is to present a study on the differences on wellbeing according to the mobility of respondents during the lockdown. Information from 1,827 individuals regarding the satisfaction of the basic psychological needs (Autonomy, Competence and Relatedness) and Positive and Negative affect were collected through and web-survey during the first lockdown of the Covid-19 pandemic, together with mobility data and sociodemographic characteristics. Mann-Whitney U tests, Confirmatory Factor Analyses and Structural Equation models are used to find out differences in the wellbeing of people according to their mobility characteristics during the first lockdown, and how these mobility characteristics are associated to the psychological variables studied. Mobility of people during the first lockdown reduced drastically, especially the youngest ones, and the main travel mode was walking. In general, the youngest participants in this research and females present lower values of the psychological wellbeing variables during the lockdown. A very low or very high degree of mobility is also associated to discomfort, although the more time spent traveling the better people feel. Those who walked more are related to lower values of wellbeing. Some lessons are learned to improve transport and mobility planning during a pandemic. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic led to significant changes in domestic water use in many cities. One important question is whether and to what extent the pandemic stressed the resilience of the water supply system. Another question examined in the paper is whether changes in water use during confinement differed according to the sociospatial configuration of cities. We examine these questions in the context of a medium size city of Northern Spain characterized by a mix of residential land uses, and internal variations in education, age composition, and income, producing step gradients in water use. First, we select and analyze influencing factors in domestic water consumption. Second, using statistical regression tests, we assess whether or not and to what extent the same factors influenced consumption during the COVID 19 lockdown. Results indicate that COVID-19 related confinement increased work and study from home and reinforced WASH habits, leading to more water consumption. However, this increase was larger in medium and low-income neighborhoods than in high-income neighborhoods, showing the relevance of sociodemographic factors but also of other variables more relevant for outdoor water use such as precipitation. Despite this rise in consumption, the water supply system was able to withstand the shock caused by confinement showing a robust resilience both in physical and in social terms. However, more challenging scenarios such as the coincidence of a pandemic with a severe drought should be taken into account in future resilience development. • Sociodemographic and urban factors played an important role in explaining domestic water consumption patterns during COVID-19 lockdowns. • Households in multifamily buildings or apartment blocks with medium-low incomes and significant presence of young people recorded the largest relative increases of water consumption during these lockdowns. • Weather conditions are key in domestic water consumption during the COVID 19 lockdown especially in areas with a large presence of single houses. • The water supply system was resilient and could withstand the increase in consumption caused by confinement as well as provide for financial assistance to vulnerable households. Policies should take into account the possible coincidence in time of unexpected phenomena such as COVID-19 with droughts already stressing urban resilience. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic and lockdown in Spain during Spring 2020 resulted in a ban on most uses of public space, producing a social redefinition of not only public spaces, but also private spaces such as homes. This paper examines the discourses related to balconies that appeared at this time on Twitter (X), an intermediate microspace and hybrid between the public and private. The study uses data from tweets posted in Spain during the strictest phase of the lockdown (from 15 March to 1 May 2020). Based on a descriptive analytical approach – both quantitative and textual – the study applies text content analysis and natural language processing to identify and analyse the main topics of conversation related to the use of balconies. The study brought to light two primary and complementary results. Firstly, the balcony during the pandemic became a revitalized space in an exceptional situation, partially adapting to the most common practices characteristic of public space and thus reaffirming the importance of the existence of public spaces for citizen encounters. Secondly, the various uses of balconies received different types of attention and generated stronger or weaker interactions on social networks, demonstrating the complexity and diversity of relationships with public spaces. • The impossibility of using public spaces multiplied the importance of balconies • Using Twitter data, we studied the use of balconies in Spain in the spring of 2020 • Balconies became a public microspace through their use towards the outside • Technology did not isolate citizens but was key for the promotion of the new uses • We found important differences by use in attachments to the tweets and interactions [ABSTRACT FROM AUTHOR]

Aim: To establish contact lens wear and care practices during the COVID-19 pandemic in Spain.Method: A 58-item anonymous online survey was distributed during the period 30th April to 10th May via Qualtrics. The survey explored: a) demographic characteristics (age, sex, general health and where they were living during lockdown), b) changes in their contact lens use during lockdown, c) hygiene and contact lens compliance and d) concerns associated with contact lens wear and ways to support wearers during the pandemic.Results: Two hundred and sixty responses were analysed (38.8 ± 11.4 years old, 75% female). Three-quarters of participants reported that they were self-isolating or rigorously following social distancing advice. Sixty-seven percent of participants reported using their contact lenses less during the pandemic. Respondents were found to be compliant with handwashing prior to inserting and removing contact lenses (in both cases 97% doing this 'most times' or 'every time'). However, only 44 % complied with the '20 s rule' and 48 % used a shared towel to dry their hands. A higher proportion of hydrogen peroxide users replaced the lens case monthly compared to multi-purpose users (64% vs.49%; p < 0.001). Twenty-four percent admitted wearing lenses whilst showering and 16% did not consider ceasing lens wear if feeling unwell with flu/cold symptoms.Conclusion: Eye care practitioners should continue to educate contact lens wearers to ensure safe contact lens wear to minimise the chance of developing contact lens related complications during the pandemic. Modifiable factors that need particular attention in Spain include: handwashing for at least 20s before lens handling, drying hands with single use paper towels, including a rub-and-rinse step for reusable lenses, lens case cleaning and renewal, avoidance of water exposure and when to cease lens wear during the pandemic. [ABSTRACT FROM AUTHOR]

Positive sentiments towards urban green spaces (UGS) unequivocally increased worldwide amid COVID-19. In contrast, this paper documents that views on mobility restrictions applicable to UGS are of a contested nature. That is, while residents unambiguously report positive sentiments towards UGS, they do not share views on how to administer access to UGS—which is a matter of public policy. These contesting views reflect opposite demands that managers of UGS had to balance during the pandemic as they faced the challenge of reducing risk of spread while providing services that support physical and mental health of residents. The empirical analysis in this paper relies on views inferred through a text classification algorithm implemented on Twitter messages posted from January to October 2020, by urban residents in three Latin American countries—Argentina, Colombia, and Mexico—and Spain. The focus on Latin America is motivated by the documented lack of compliance with mobility restrictions; Spain works as a comparison point to learn differences with respect to other regions. Understanding and following in real-time the evolution of contesting views amid a pandemic is useful for managers and city planners to inform adaptation measures—e.g. communication strategies can be tailored to residents with specific views. • Amid COVID-19, social media showcased contesting views on mobility restrictions applicable to UGS. • Contesting views on regulation of access hold together with unambiguous increase in positive sentiments towards UGS. • These views reflect residents' stands with respect to how UGS should be used during a pandemic event. • Social media provides unstructured information to design and communicate adaptation measures in real-time. [ABSTRACT FROM AUTHOR]

A key characteristic of electricity prices is their sensitivity to changes in supply and demand. In this sense, the Covid-19 lockdown policies modified electricity consumption patterns at both business and household levels, affecting the shape and position of the electricity demand curve and, thus, leading to a direct effect on electricity prices. However, could this demand-side effect be greater than other supply-induced effects? Is it persistent over time? This paper uses a synthetic bidding approach and concludes that the strict lockdown phase had a strong, immediate – but not persistent – effect on the Spanish electricity price. Furthermore, a high share of renewable energy and a reduction in fossil fuel and emission prices have also proven crucial in driving prices down, though lockdown policies had more impact on prices. • The cubic fit is the best approach for Spanish supply and demand curves in 2019 and 2020. • The strict Covid-19 lockdown worsened the fit and made the electricity demand steeper. • During the strict lockdown the horizontal part of the demand curve decreased by 7.1%. • The Covid-19 effect dominated over other supply-induced effects in 2020 in Spain. • When electricity demand recovered its elasticity, supply-induced effects increased. [ABSTRACT FROM AUTHOR]

Many types of research have been carried out with the aim of combating the COVID-19 pandemic since the first outbreak was detected in Wuhan, China. Anticipating the evolution of an outbreak helps to devise suitable economic, social and health care strategies to mitigate the effects of the virus. For this reason, predicting the SARS-CoV-2 transmission rate has become one of the most important and challenging problems of the past months. In this paper, we apply a two-stage mid and long-term forecasting framework to the epidemic situation in eight districts of Andalusia, Spain. First, an analytical procedure is performed iteratively to fit polynomial curves to the cumulative curve of contagions. Then, the extracted information is used for estimating the parameters and structure of an evolutionary artificial neural network with hybrid architectures (i.e., with different basis functions for the hidden nodes) while considering single and simultaneous time horizon estimations. The results obtained demonstrate that including polynomial information extracted during the training stage significantly improves the mid- and long-term estimations in seven of the eight considered districts. The increase in average accuracy (for the joint mid- and long-term horizon forecasts) is 37.61% and 35.53% when considering the single and simultaneous forecast approaches, respectively. • Estimated polynomial model coefficients serve as COVID19 contagion rate descriptors. • EANNs are used to build valid predictors for different pandemic stages. • Considering curve polynomial descriptors significantly improve the model performances. • Simultaneous Multi-Task EANN forecast perform better in Málaga with simpler models. [ABSTRACT FROM AUTHOR]

The Spanish real estate and its 'sea and sun' tourism model, were profoundly disrupted during the Great Recession of 2008–2014 As a result, hedge funds and their speculative operations have favoured an intense process of urban touristification in the largest Spanish cities, especially over the past ten years. The aim of this paper is to examine how the COVID-19 crisis has triggered shifts in the supply of short-term rentals and the type of demand of such rentals. By taking into account such changes, we will address the potential changes that the current pandemic scenario might bring between the 'classical' real estate market and short-term rentals in Spain. • Its high dependence on tourism makes Spain the best case study to analyze pandemic-related impacts on urban tourism • The exhaustion of the "Sun & Beach" tourism model had been replaced by the touristification of consolidated urban areas • Short-term rental and residential markets have redefined their complex relationship in new, uncertain directions • Platforms are the new frontier of capital accumulation in cities, anda powerful new mechanism to extract rents from housing [ABSTRACT FROM AUTHOR]

The experience of Singapore and South Korea makes it clear that under certain circumstances massive testing is an effective way for containing the advance of the COVID-19. In this paper, we propose a modified SEIR model which takes into account tracing and massive testing, proving theoretically that more tracing and testing implies a reduction of the total number of infected people in the long run. We apply this model to the spreading of the first wave of the disease in Spain, obtaining numerical results. After that, we introduce a heuristic approach in order to minimize the COVID-19 spreading by planning effective test distributions among the populations of a region over a period of time. As an application, the impact of distributing tests among the counties of New York according to this method is computed in terms of the number of saved infected individuals. • A SEIR model is introduced to analyse the efficiency of test distributions. • It is proved that massive testing helps reducing the number of infected people. • A heuristic method is developed to increase the number of saved infections. • Extensive computational experience quantifies the number of saved infections. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel genetic algorithm-based seasonal fractional grey model is proposed. • Monthly electricity generation is forecasted since the Covid-19 lockdown started. • Seasonal grey and machine learning models are used for forecasting. • The selected countries are France, Germany, Spain, Turkey, and the UK. • The share of renewables in total electricity generation is forecasted. Balances in the energy sector have changed since the implementation of the Covid-19 pandemic lockdown in Europe. This paper analyses how the lockdown affected electricity generation in European countries and how it will reshape future energy generation. Monthly electricity generation from total renewables and non-renewables in France, Germany, Spain, Turkey, and the UK from January 2017 to September 2020 were evaluated and compared. Four seasonal grey prediction models and three machine learning methods were used for forecasting; the quarterly results are presented to the end of 2021. Additionally, the share of electricity generation from renewables in total electricity generation from 2017 to 2021 for the selected countries was compared. Electricity generation from total non-renewables in the second quarter of 2020 for France, Germany, Spain, and the UK decreased by 21%–25% compared to the same period of 2019; the decline in Turkey was approximately 11%. Additionally, electricity generation from non-renewables in the third quarter of 2020 for all countries, except Turkey, decreased compared to the same period of the previous year. All grey prediction models and support vector machine method forecast that the share of renewables in total electricity generation will increase continuously in France, Germany, Spain, and the UK to the end of 2021. The forecasting methods provided by this study open new avenues for research on the impact of the Covid-19 pandemic on the future of the energy sector. [ABSTRACT FROM AUTHOR]

• Harmonization and quality control of originally heterogenous observational databases. • Large-scale application of machine learning methods in a distributed data network. • Transparent use of open-source software tools and publicly shared analytical code. As a response to the ongoing COVID-19 pandemic, several prediction models in the existing literature were rapidly developed, with the aim of providing evidence-based guidance. However, none of these COVID-19 prediction models have been found to be reliable. Models are commonly assessed to have a risk of bias, often due to insufficient reporting, use of non-representative data, and lack of large-scale external validation. In this paper, we present the Observational Health Data Sciences and Informatics (OHDSI) analytics pipeline for patient-level prediction modeling as a standardized approach for rapid yet reliable development and validation of prediction models. We demonstrate how our analytics pipeline and open-source software tools can be used to answer important prediction questions while limiting potential causes of bias (e.g. , by validating phenotypes, specifying the target population, performing large-scale external validation, and publicly providing all analytical source code). We show step-by-step how to implement the analytics pipeline for the question: 'In patients hospitalized with COVID-19, what is the risk of death 0 to 30 days after hospitalization?'. We develop models using six different machine learning methods in a USA claims database containing over 20,000 COVID-19 hospitalizations and externally validate the models using data containing over 45,000 COVID-19 hospitalizations from South Korea, Spain, and the USA. Our open-source software tools enabled us to efficiently go end-to-end from problem design to reliable Model Development and evaluation. When predicting death in patients hospitalized with COVID-19, AdaBoost, random forest, gradient boosting machine, and decision tree yielded similar or lower internal and external validation discrimination performance compared to L1-regularized logistic regression, whereas the MLP neural network consistently resulted in lower discrimination. L1-regularized logistic regression models were well calibrated. Our results show that following the OHDSI analytics pipeline for patient-level prediction modelling can enable the rapid development towards reliable prediction models. The OHDSI software tools and pipeline are open source and available to researchers from all around the world. [ABSTRACT FROM AUTHOR]

Background and objectives: Epidemiological models of epidemic spread are an essential tool for optimizing decision-making. The current literature is very extensive and covers a wide variety of deterministic and stochastic models. However, with the increase in computing resources, new, more general, and flexible procedures based on simulation models can assess the effectiveness of measures and quantify the current state of the epidemic. This paper illustrates the potential of this approach to build a new dynamic probabilistic model to estimate the prevalence of SARS-CoV-2 infections in different compartments. Methods: We propose a new probabilistic model in which, for the first time in the epidemic literature, parameter learning is carried out using gradient-free stochastic black-box optimization techniques simulating multiple trajectories of the infection dynamics in a general way, solving an inverse problem that is defined employing the daily information from mortality records. Results : After the application of the new proposal in Spain in the first and successive waves, the result of the model confirms the accuracy to estimate the seroprevalence and allows us to know the real dynamics of the pandemic a posteriori to assess the impact of epidemiological measures by the Spanish government and to plan more efficiently the subsequent decisions with the prior knowledge obtained. Conclusions: The model results allow us to estimate the daily patterns of COVID-19 infections in Spain retrospectively and examine the population's exposure to the virus dynamically in contrast to seroprevalence surveys. Furthermore, given the flexibility of our simulation framework, we can model situations —even using non-parametric distributions between the different compartments in the model— that other models in the existing literature cannot. Our general optimization strategy remains valid in these cases, and we can easily create other non-standard simulation epidemic models that incorporate more complex and dynamic structures. [ABSTRACT FROM AUTHOR]

• Intensive care unit admission for COVID-19 positive patients is considered. • Fuzzy AHP is used to prioritize 19 criteria. • Multi MOORA is used to rank COVID-19 positive patients. • Which patient is more urgent for ICU is found out. The COVID-19 pandemic results in an intense flow of patients to hospitals especially to the intensive care units (ICUs) to be treated. The ICUs will therefore be confronted with a massive influx of patients (e.g. Spain and Italy). However, if the number of patients is higher than the resources available in ICUs, rationing decisions such as determining and evaluating the criteria for ICU admission becomes essential. In this case, the decision of which patients will be admitted to the ICUs may put significant pressure on healthcare personnel. The goal of this paper is to determine the criteria to be used in the decision of admission of COVID-19 patients to the ICUs. A three-step methodology is applied. In the first step, the evaluation criteria are determined, and then the criteria are prioritized using a fuzzy analytical hierarchy process (AHP) in an uncertain and multiple-criteria environment choice. Finally, COVID-19 patients are ranked using the Multi-Objective Optimization Method by Ratio Analysis to find out which patient is more urgent. According to experts' evaluation of ICU admission criteria, "increment of >2 in SOFA score" seems the most dominant factor among others. The proposed methodology is tested on 10 anonymous COVID-19 positive patients being treated in a public hospital and the ICU admission results are discussed. Obtained priorities and ranking is in line with the hospitals' behavior that potentially depicts the usefulness and validity of the proposed approach. [ABSTRACT FROM AUTHOR]