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A global disaster, such as the recent Covid-19 pandemic, affects every aspect of our lives and there is a need to investigate these highly complex phenomena if one aims to diminish their impact in the health of the population, as well as their socio-economic stability. In this paper we present an attempt to understand the role of the governmental authorities and the response of the rest of the population facing such emergencies. We present a mathematical model that takes into account the epidemiological features of the pandemic and also the actions of people responding to it, focusing only on three aspects of the system, namely, the fear of catching this serious disease, the impact on the economic activities and the compliance of the people to the mitigating measures adopted by the authorities. We apply the model to the specific case of Spain, since there are accurate data available about these three features. We focused on tourism as an example of the economic activity, since this sector of economy is one of the most likely to be affected by the restrictions imposed by the authorities, and because it represents an important part of Spanish economy. The results of numerical calculations agree with the empirical data in such a way that we can acquire a better insight of the different processes at play in such a complex situation, and also in other different circumstances. [ABSTRACT FROM AUTHOR]

European countries struggled to fight against the second and the third waves of the COVID-19 pandemic, as the Test-Trace-Isolate (TTI) strategy widely adopted over the summer and early fall 2020 failed to contain the spread of the disease effectively. This paper sheds light on the effectiveness of such a strategy in two European countries (Spain and Italy) by analysing data from June to December 2020, collected via a large-scale online citizen survey with 95,251 and 43,393 answers in Spain and Italy, respectively. Our analysis describes several weaknesses in each of the three pillars of the TTI strategy: Test, Trace, and Isolate. We find that 40% of respondents had to wait more than 48 hours to obtain coronavirus tests results, while literature has shown that a delay of more than one day might make tracing all cases inefficient. We also identify limitations in the manual contact tracing capabilities in both countries, as only 29% of respondents in close contact with a confirmed infected individual reported having been contact traced. Moreover, our analysis shows that more than 45% of respondents report being unable to self-isolate if needed. We also analyse the mitigation strategies deployed to contain the second wave of coronavirus. We find that these interventions were particularly effective in Italy, where close contacts were reduced by more than 20% in the general population. Finally, we analyse the participants' perceptions about the coronavirus risk associated with different daily activities. We observe that they are often gender- and age-dependent, and not aligned with the actual risk identified by the literature. This finding emphasises the importance of deploying public-health communication campaigns to debunk misconceptions about SARS-CoV-2. Overall, our work illustrates the value of online citizen surveys to quickly and efficiently collect large-scale population data to support and evaluate policy decisions to combat the spread of infectious diseases, such as coronavirus., (© 2022. The Author(s).)

Population confinements have been one of the most widely adopted non-pharmaceutical interventions (NPIs) implemented by governments across the globe to help contain the spread of the SARS-CoV-2 virus. While confinement measures have been proven to be effective to reduce the number of infections, they entail significant economic and social costs. Thus, different policy makers and social groups have exhibited varying levels of acceptance of this type of measures. In this context, understanding the factors that determine the willingness of individuals to be confined during a pandemic is of paramount importance, particularly, to policy and decision-makers. In this paper, we study the factors that influence the unwillingness to be confined during the COVID-19 pandemic by the means of a large-scale, online population survey deployed in Spain. We perform two types of analyses (logistic regression and automatic pattern discovery) and consider socio-demographic, economic and psychological factors, together with the 14-day cumulative incidence per 100,000 inhabitants. Our analysis of 109,515 answers to the survey covers data spanning over a 5-month time period to shed light on the impact of the passage of time. We find evidence of pandemic fatigue as the percentage of those who report an unwillingness to be in confinement increases over time; we identify significant gender differences, with women being generally less likely than men to be able to sustain long-term confinement of at least 6 months; we uncover that the psychological impact was the most important factor to determine the willingness to be in confinement at the beginning of the pandemic, to be replaced by the economic impact as the most important variable towards the end of our period of study. Our results highlight the need to design gender and age specific public policies, to implement psychological and economic support programs and to address the evident pandemic fatigue as the success of potential future confinements will depend on the population's willingness to comply with them., (© 2021. The Author(s).)

In 2020, the COVID-19 pandemic followed a two-wave pattern in most countries. Hospital admission for COVID-19 in one wave or another could have affected mortality, especially among the older persons. The objective of this study was to evaluate whether the admission of older patients during the different waves, before SARS-CoV-2 vaccination was available, was associated with a different mortality. We compared the mortality rates of patients hospitalized during 2020 before (first wave) and after (second wave) July 7, 2020, included in the SEMI-COVID-19 Registry, a large, multicenter, retrospective cohort of patients admitted to 126 Spanish hospitals for COVID-19. A multivariate logistic regression analysis was performed to control for changes in either the patient or disease profile. As of December 26, 2022, 22,494 patients had been included (17,784 from the first wave and 4710 from the second one). Overall mortality was 20.4% in the first wave and 17.2% in the second wave (risk difference (RD) − 3.2%; 95% confidence interval (95% CI) − 4.4 to − 2.0). Only patients aged 70 and older (10,973 patients: 8571 in the first wave and 2386 in the second wave) had a significant reduction in mortality (RD − 7.6%; 95% CI − 9.7 to − 5.5) (unadjusted relative risk reduction: 21.6%). After adjusting for age, comorbidities, variables related to the severity of the disease, and treatment received, admission during the second wave remained a protective factor. In Spain, patients aged 70 years and older admitted during the second wave of the COVID-19 pandemic had a significantly lower risk of mortality, except in severely dependent persons in need of corticosteroid treatment. This effect is independent of patient characteristics, disease severity, or treatment received. This suggests a protective effect of a better standard of care, greater clinical expertise, or a lesser degree of healthcare system overload. [ABSTRACT FROM AUTHOR]

This work aims to find out the effectiveness of sources based on Big Data like mobile phone records to analyze mobility flows and changes in the population of Spain in different scenarios during the period of the pandemic caused by the COVID-19 virus. To this end, we have used mobile phone data provided by the National Institute of Statistics from four days corresponding to different phases of the pandemic. Origin–Destination matrices and population estimation calculations at the spatial level of population cells have been elaborated. The results show different patterns that correspond to the phenomena that have occurred, as the decrease of the population during the periods associated with the confinement measures. The consistency of findings with the reality and the generally good correlation with the population census data indicate that mobile phone records are a useful source of data for the elaboration of demographic and mobility studies during pandemics. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic was an inevitable outcome of a globalized world in which a highly infective disease is able to reach every country in a matter of weeks. While lockdowns and strong mobility restrictions have proven to be efficient to contain the exponential transmission of the virus, its pervasiveness has made it impossible for economies to maintain this kind of measures in time. Understanding precisely how the spread of the virus occurs from a territorial perspective is crucial not only to prevent further infections but also to help with policy design regarding human mobility. From the large spatial differences in the behavior of the virus spread we can unveil which areas have been more vulnerable to it and why, and with this information try to assess the risk that each community has to suffer a future outbreak of infection. In this work we have analyzed the geographical distribution of the cumulative incidence during the first wave of the pandemic in the region of Galicia (north western part of Spain), and developed a mathematical approach that assigns a risk factor for each of the different municipalities that compose the region. This risk factor is independent of the actual evolution of the pandemic and incorporates geographic and demographic information. The comparison with empirical information from the first pandemic wave demonstrates the validity of the method. Our results can potentially be used to design appropriate preventive policies that help to contain the virus. [ABSTRACT FROM AUTHOR]