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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]

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]

In 1918 many countries, but not Spain, were fighting World War I. Spanish press could report about the diffusion and severity of a new infection without censorship for the first-time, so that this pandemic is commonly defined as "Spanish flu", even though Spain was not its place of origin. "Spanish flu" was one of the deadliest pandemics in history and has been frequently compared with the coronavirus disease (COVID)-19 pandemic. These pandemics share similarities, being both caused by highly variable and transmissible respiratory RNA viruses, and diversity, represented by diagnostics, therapies, and especially vaccines, which were made rapidly available for COVID-19, but not for "Spanish flu". Most comparison studies have been carried out in the first period of COVID-19, when these resources were either not yet available or their use had not long started. Conversely, we wanted to analyze the role that the advanced diagnostics, anti-viral agents, including monoclonal antibodies, and innovative COVID-19 vaccines, may have had in the pandemic containment. Early diagnosis, therapies, and anti-COVID-19 vaccines have markedly reduced the pandemic severity and mortality, thus preventing the collapse of the public health services. However, their influence on the reduction of infections and re-infections, thus on the transition from pandemic to endemic condition, appears to be of minor relevance. The high viral variability of influenza and coronavirus may probably be contained by the development of universal vaccines, which are not easy to be obtained. The only effective weapon still remains the disease prevention, to be achieved with the reduction of promiscuity between the animal reservoirs of these zoonotic diseases and humans. [ABSTRACT FROM AUTHOR]

Due to the exponential growth of the SARS-CoV-2 pandemic in Spain (2020), the Spanish Government adopted lockdown measures as mitigating strategies to reduce the spread of the pandemic from 14 March. In this paper, we report the results of the change in air quality at two Atlantic Coastal European cities (Northwest Spain) during five lockdown weeks. The temporal evolution of gaseous (nitrogen oxides, comprising NOx, NO, and NO2; sulfur dioxide, SO2; carbon monoxide, CO; and ozone, O3) and particulate matter (PM10; PM2.5; and equivalent black carbon, eBC) pollutants were recorded before (7 February to 13 March 2020) and during the first five lockdown weeks (14 March to 20 April 2020) at seven air quality monitoring stations (urban background, traffic, and industrial) in the cities of A Coruña and Vigo. The influences of the backward trajectories and meteorological parameters on air pollutant concentrations were considered during the studied period. The temporal trends indicate that the concentrations of almost all species steadily decreased during the lockdown period with statistical significance, with respect to the pre-lockdown period. In this context, great reductions were observed for pollutants related mainly to fossil fuel combustion, road traffic, and shipping emissions (−38 to −78% for NO, −22 to −69% for NO2, −26 to −75% for NOx, −3 to −77% for SO2, −21% for CO, −25 to −49% for PM10, −10 to −38% for PM2.5, and −29 to −51% for eBC). Conversely, O3 concentrations increased from +5 to +16%. Finally, pollutant concentration data for 14 March to 20 April of 2020 were compared with those of the previous two years. The results show that the overall air pollutants levels were higher during 2018–2019 than during the lockdown period. [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]

During the outbreak of a disease caused by a pathogen with unknown characteristics, the uncertainty of its progression parameters can be reduced by devising methods that, based on rational assumptions, exploit available information to provide actionable insights. In this study, performed a few (~6) weeks into the outbreak of COVID-19 (caused by SARS-CoV-2), one of the most important disease parameters, the average time-to-recovery, was calculated using data publicly available on the internet (daily reported cases of confirmed infections, deaths, and recoveries), and fed into an algorithm that matches confirmed cases with deaths and recoveries. Unmatched cases were adjusted based on the matched cases calculation. The mean time-to-recovery, calculated from all globally reported cases, was found to be 18.01 days (SD 3.31 days) for the matched cases and 18.29 days (SD 2.73 days) taking into consideration the adjusted unmatched cases as well. The proposed method used limited data and provided experimental results in the same region as clinical studies published several months later. This indicates that the proposed method, combined with expert knowledge and informed calculated assumptions, could provide a meaningful calculated average time-to-recovery figure, which can be used as an evidence-based estimation to support containment and mitigation policy decisions, even at the very early stages of an outbreak. [ABSTRACT FROM AUTHOR]

Pain symptoms after the acute phase of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) are present in almost 50% of COVID-19 survivors. The presence of kinesiophobia is a risk factor which may promote and perpetuate pain. This study aimed to investigate variables associated with the presence of kinesiophobia in a sample of previously hospitalized COVID-19 survivors exhibiting post-COVID pain. An observational study was conducted in three urban hospitals in Spain, including one hundred and forty-six COVID-19 survivors with post-COVID pain. Demographic (age, weight, height), clinical (intensity and duration of pain), psychological (anxiety level, depressive level, sleep quality), cognitive (catastrophizing), sensitization-associated symptoms, and health-related quality of life variables were collected in 146 survivors with post-COVID pain, as well as whether they exhibited kinesiophobia. Stepwise multiple linear regression models were conducted to identify variables significantly associated with kinesiophobia. Patients were assessed a mean of 18.8 (SD 1.8) months after hospital discharge. Kinesiophobia levels were positively associated with anxiety levels (r: 0.356, p < 0.001), depression levels (r: 0.306, p < 0.001), sleep quality (r: 0.288, p < 0.001), catastrophism (r: 0.578, p < 0.001), and sensitization-associated symptoms (r: 0.450, p < 0.001). The stepwise regression analysis revealed that 38.1% of kinesiophobia variance was explained by catastrophism (r2 adj: 0.329, B = 0.416, t = 8.377, p < 0.001) and sensitization-associated symptoms (r2 adj: 0.381, B = 0.130, t = 3.585, p < 0.001). Kinesiophobia levels were associated with catastrophism and sensitization-associated symptoms in previously hospitalized COVID-19 survivors with post-COVID pain. Identification of patients at a higher risk of developing a higher level of kinesiophobia, associated with post-COVID pain symptoms, could lead to better therapeutic strategies. [ABSTRACT FROM AUTHOR]

Background: Early detection of melanoma is one of the main diagnostic goals of dermatologists worldwide, due to the increasing incidence of the disease in our environment. However, the irruption of the SARS-CoV-2 pandemic has posed a challenge to global healthcare, forcing systems to focus their resources on the fight against COVID-19. Methods: Retrospective cohort study. The exposed cohort were patients diagnosed with melanoma in the year after the general confinement in Spain (15 March 2020) and the unexposed cohort were patients with melanoma diagnosed in the previous year. Results: 130 patients were included. No differences were observed between demographic characteristics in both cohorts. The mean Breslow of melanoma before the onset of the pandemic was 1.08, increasing to 2.65 in the year after the onset of the pandemic (p < 0.001). On the other hand, the percentage of melanomas in situ decreased from 38.96% to 16.98% in the year after the declaration of the state of alarm in Spain. Conclusions: The SARS-CoV-2 outbreak has led to a reduction in the early diagnosis of melanoma, with an increase in invasive melanomas with poor prognosis histological factors. This could lead to an increase in melanoma-related mortality in the coming years in our environment. [ABSTRACT FROM AUTHOR]

In some areas of Spain, health services and professionals working in the front line against the Sars-Cov-2 virus have been widely overwhelmed at all levels. Therefore, the objective of this study was to assess the level of work engagement of Spanish nurses during the COVID-19 pandemic. A cross-sectional study was carried out. The sample consisted of 510 active nurses from all over Spain, without age exclusion, who voluntarily accepted to participate in the study. Work engagement was assessed with the 9-item Utrecht Work Engagement Scale (UWES) questionnaire, through an online questionnaire and non-probabilistic snowball sampling. The results showed a mean age of 45.9 years (SD = 10.7 years), most of them women (78.1%), and 58.5% were in primary care. The mean score for the UWES-9 questionnaire was 4.6 points (SD = 1.35). The categorical regression analysis performed revealed an R2 value of 0.75 and a significance of p < 0.01 in the sex, type of unit, and training variables. The Spanish nurses in the sample present high levels of work engagement in all dimensions in general, although the lowest mean scores are found in the vigor dimension, among men, and nurses working in hospital and critical units. [ABSTRACT FROM AUTHOR]

The present work develops an accurate prediction model of the COVID-19 pandemic, capable not only of fitting data with a high regression coefficient but also to predict the overall infections and the infection peak day as well. The model is based on the Verhulst equation, which has been used to fit the data of the COVID-19 spread in China, Italy, and Spain. This model has been used to predict both the infection peak day, and the total infected people in Italy and Spain. With this prediction model, the overall infections, the infection peak, and date can accurately be predicted one week before they occur. According to the study, the infection peak took place on 23 March in Italy, and on 29 March in Spain. Moreover, the influence of the total and partial lockdowns has been studied, without finding any meaningful difference in the disease spread. However, the infected population, and the rate of new infections at the start of the lockdown, seem to play an important role in the infection spread. The developed model is not only an important tool to predict the disease spread, but also gives some significant clues about the main factors that affect to the COVID-19 spread, and quantifies the effects of partial and total lockdowns as well. [ABSTRACT FROM AUTHOR]