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The COVID-19 outbreak was a major event that greatly impacted the economy and the health systems around the world. Understanding the behavior of the virus and being able to perform long-term and short-term future predictions of the daily new cases is a working field for machine learning methods and mathematical models. This paper compares Verhulst's, Gompertz´s, and SIR models from the point of view of their efficiency to describe the behavior of COVID-19 in Spain. These mathematical models are used to predict the future of the pandemic by first solving the corresponding inverse problems to identify the model parameters in each wave separately, using as observed data the daily cases in the past. The posterior distributions of the model parameters are then inferred via the Metropolis–Hastings algorithm, comparing the robustness of each prediction model and making different representations to visualize the results obtained concerning the posterior distribution of the model parameters and their predictions. The knowledge acquired is used to perform predictions about the evolution of both the daily number of infected cases and the total number of cases during each wave. As a main conclusion, predictive models are incomplete without a corresponding uncertainty analysis of the corresponding inverse problem. The invariance of the output (posterior prediction) with respect to the forward predictive model that is used shows that the methodology shown in this paper can be used to adopt decisions in real practice (public health). [ABSTRACT FROM AUTHOR]

A new mathematical model of Coronavirus (2019-nCov) using piecewise hybrid fractional order derivatives is given in this paper. Moreover, in order to be consistent with the physical model problem, a new parameter μ is presented. The boundedness, existence, and positivity of the solutions for the proposed model are discussed. Two improved numerical methods are presented in this paper. The Caputo proportional constant nonstandard modified Euler–Maruyama method is introduced to study the fractional stochastic model, and the Grünwald–Letnikov nonstandard finite difference method is presented to study the hybrid fractional order deterministic model. Comparative studies with real data from Spain and Wuhan are presented. [ABSTRACT FROM AUTHOR]

This paper assesses the influence on people's perception of the utility of the immunity passport (IP) program by sociodemographic factors, infectivity status, and the objective of its use. The material of this paper is a cross-sectional survey of 400 residents in Spain. The relation between utility perception and input variables is fitted with ordinary least squares (OLS) regression and linear quantile regression (LQR). The principal explanatory variable of usefulness perception is being vaccinated, especially when the objective of the IP is regulating mobility. The OLS estimate of the coefficient regression is (cr) = 0.415 (p = 0.001). We also found a positive and significant influence of that factor in all LQRs (cr = 0.652, p = 0.0026 at level (τ) = 0.75; cr = 0.482, p = 0.0047 at τ = 0.5 and cr = 0.201, p = 0.0385 at τ = 0.25). When the objective of the IP is regulating leisure, being vaccinated is relevant only to explain the central measures of usefulness perception. If the IP is used to regulate traveling, variables related to interviewees' infectivity have greater relevance than sociodemographic factors. When its objective is ruling assembly, the more important variables than being vaccinated are gender and age. To create an effective implementation of the IP, it is advisable to have a general agreement among the population on its convenience. Therefore, the findings in this study have important implications for public health decision-makers. [ABSTRACT FROM AUTHOR]

Hospitals generate huge amounts of nonwoven residues daily. This paper focused on studying the evolution of nonwoven waste generated in the Francesc de Borja Hospital, Spain, over the last few years and its relation to the COVID-19 pandemic. The main objective was to identify the most impacting pieces of nonwoven equipment in the hospital and to analyze possible solutions. The carbon footprint of the nonwoven equipment was studied through a life-cycle assessment. The results showed an apparent increase in the carbon footprint in the hospital from 2020. Additionally, due to the higher annual volume, the simple nonwoven gown used primarily for patients had a higher carbon footprint over a year than the more sophisticated surgical gowns. It can be concluded that developing a local circular economy strategy for medical equipment could be the solution to avoid the enormous waste generation and the carbon footprint of nonwoven production. [ABSTRACT FROM AUTHOR]

The evolution of some epidemics, such as influenza, demonstrates common patterns both in different regions and from year to year. On the contrary, epidemics such as the novel COVID-19 show quite heterogeneous dynamics and are extremely susceptible to the measures taken to mitigate their spread. In this paper, we propose empirical dynamic modeling to predict the evolution of influenza in Spain's regions. It is a non-parametric method that looks into the past for coincidences with the present to make the forecasts. Here, we extend the method to predict the evolution of other epidemics at any other starting territory and we also test this procedure with Spanish COVID-19 data. We finally build influenza and COVID-19 networks to check possible coincidences in the geographical distribution of both diseases. With this, we grasp the uniqueness of the geographical dynamics of COVID-19. [ABSTRACT FROM AUTHOR]

Spain was one of the countries in which more severe lockdown policies were imposed during the second term of 2020 to mitigate the unprecedented health crisis. The measures restricted citizens' mobility, obliging families to stay confined at homes for 99 days since 15 March 2020. The measures created a number of challenges that affect the family climate. This paper aims to empirically analyse how the family climate in Spain has been affected by COVID-19. The family climate assessment was based on an online questionnaire answered by 2034 citizens. A multi-criteria decision-making method rooted in fuzzy logic and TOPSIS, and a fuzzy clustering method, are applied to analyse the effects of the COVID-19 on the family climate. The fuzzy clustering method reveals that there are three different family climate profiles, namely (1) extreme positive, (2) extreme negative, and (3) intermediate. Our results show that some traits affect having a more or less positive family climate. The authors discuss the main contributions and the policy implications that could provide insights into future measures. [ABSTRACT FROM AUTHOR]

In this paper, the use of HVAC systems and non-HVAC control measures to reduce virus-laden bioaerosol exposure in a highly occupied indoor space is investigated. A simulation tool was used to model the fate and transport of bioaerosols in an indoor space in the hotel industry (bar or pub) with three types of HVAC system (central air handling system (CAHS), dedicated outdoor air system (DOAS), and wall unit system (WUS)). Non-HVAC control measures such as portable air cleaners (PAC) and local exhaust fans were considered. Occupant exposure was evaluated for 1 μm bioaerosols, which transport SARS-CoV-2, for 3 h/day of continuous source and exposure. The combined effects of ventilation (400 l/s of outdoor air), recirculated air filtration (90% efficacy), and a PAC with a capacity up to 900 m3/h mitigated the (normalized) integrated exposure of the occupant by 0.66 to 0.51 (CAHS) and 0.43 to 0.36 (DOAS). In the case of WUS, the normalized integrated exposure was reduced by up to 0.2 when the PAC with a capacity of up to 900 m3/h was used. The corresponding electricity consumed increased by 297.4 kWh/year (CAHS) and 482.7 kWh/year (DOAS), while for the WUS it increased by 197.1 kWh/year. [ABSTRACT FROM AUTHOR]

The unprecedented COVID-19 pandemic is showing dramatic impact across the world. Public health authorities attempt to fight against the virus while maintaining economic activity. In the face of the uncertainty derived from the virus, all the countries have adopted non-pharmaceutical interventions for limiting the mobility and maintaining social distancing. In order to support these interventions, some health authorities and governments have opted for sharing very fine-grained data related with the impact of the virus in their territories. Geographical science is playing a major role in terms of understanding how the virus spreads across regions. Location of cases allows identifying the spatial patterns traced by the virus. Understanding these patterns makes controlling the virus spread feasible, minimizes its impact in vulnerable regions, anticipates potential outbreaks, or elaborates predictive risk maps. The application of geospatial analysis to fine-grained data must be urgently adopted for optimal decision making in real and near-real time. However, some aspects related to process and map sensitive health data in emergency cases have not yet been sufficiently explored. Among them include concerns about how these datasets with sensitive information must be shown depending on aspects related to data aggregation, scaling, privacy issues, or the need to know in advance the particularities of the study area. In this paper, we introduce our experience in mapping fine-grained data related to the incidence of the COVID-19 during the first wave in the region of Galicia (NW Spain), and after that we discuss the mentioned aspects. [ABSTRACT FROM AUTHOR]

The aim of this paper is the imputation of missing data of COVID-19 hospitalized and intensive care curves in several Spanish regions. Taking into account that the curves of cases, deceases and recovered people are completely observed, a function-on-function regression model is proposed to estimate the missing values of the functional responses associated with hospitalized and intensive care curves. The estimation of the functional coefficient model in terms of principal components' regression with the completely observed data provides a prediction equation for the imputation of the unobserved data for the response. An application with data from the first wave of COVID-19 in Spain is developed after properly homogenizing, registering and smoothing the data in a common interval so that the observed curves become comparable. Finally, Canonical Correlation Analysis is performed on the functional principal components to interpret the relationship between hospital occupancy rate and illness response variables. [ABSTRACT FROM AUTHOR]

In this paper, we use an SIRD model to analyze the evolution of the COVID-19 pandemic in Spain, caused by a new virus called SARS-CoV-2 from the coronavirus family. This model is governed by a nonlinear system of differential equations that allows us to detect trends in the pandemic and make reliable predictions of the evolution of the infection in the short term. This work shows this evolution of the infection in various changing stages throughout the period of maximum alert in Spain. It also shows a quick adaptation of the parameters that define the disease in several stages. In addition, the model confirms the effectiveness of quarantine to avoid the exponential expansion of the pandemic and reduce the number of deaths. The analysis shows good short-term predictions using the SIRD model, which are useful to influence the evolution of the epidemic and thus carry out actions that help reduce its harmful effects. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is having a strong impact on the economies of all countries, negatively affecting almost all sectors. This paper compares Spanish electricity and natural gas prices in the first half-year of 2020 with the prices expected for that period at the end of 2019. The half-year of 2020 selected coincides with the period of greatest impact of COVID-19 on Spanish society. Expected prices and their future probability distributions are calculated using a stochastic model with deterministic and stochastic parts; the stochastic part includes mean-reverting and jumps behaviour. The model is calibrated with 2016–2019 daily spot prices for electricity and with day-ahead prices for natural gas. The results show large monthly differences between the prices expected at the end of the year 2019 and the actual prices for the half-year; in May 2020, wholesale electricity prices are found to be EUR 31.60/MWh lower than expected, i.e., 60% lower. In the case of natural gas, the prices in the same month are EUR 8.96/MWh lower than expected, i.e., 62% lower. The spark spread (SS) is positive but lower than expected and also lower than in the same months of the previous year. [ABSTRACT FROM AUTHOR]

In this paper, we address one of the most important topics in the field of Social Networks Analysis: the community detection problem with additional information. That additional information is modeled by a fuzzy measure that represents the risk of polarization. Particularly, we are interested in dealing with the problem of taking into account the polarization of nodes in the community detection problem. Adding this type of information to the community detection problem makes it more realistic, as a community is more likely to be defined if the corresponding elements are willing to maintain a peaceful dialogue. The polarization capacity is modeled by a fuzzy measure based on the J D J p o l measure of polarization related to two poles. We also present an efficient algorithm for finding groups whose elements are no polarized. Hereafter, we work in a real case. It is a network obtained from Twitter, concerning the political position against the Spanish government taken by several influential users. We analyze how the partitions obtained change when some additional information related to how polarized that society is, is added to the problem. [ABSTRACT FROM AUTHOR]