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This paper aims to describe and discuss the significance of the social policy measures implemented in Southern European countries—Greece, Italy, Portugal and Spain—in response to the first wave of COVID‐19. Our analysis covers interventions from 1 March to June 30, 2020. Despite significant differences in how the COVID‐19 pandemic spread—with Italy and Spain experiencing much higher rates of infection and lethality—Southern European economies are among the most hard‐hit—and are likely to find themselves in the eye of the storm, once more. The paper shows that despite differences in how countries have countered the spread of COVID‐19, there are important commonalities in the actions governments took to counteract the economic impact of the pandemic. Foremost efforts were directed at wage subsidy schemes to contain mass job destruction, additional temporary benefits to compensate self‐employed and other non‐standard workers for the loss of earnings; the expansion of unemployment insurance; and finally, the introduction and/or strengthening of schemes to provide support to families with care responsibilities. The scale of the social policy and employment protection response has nevertheless been constrained by the fiscal position of each individual country in the post‐Euro crisis context. We argue that, in the long run, the response capacity of these governments and the social and economic consequences of this crisis will need to be contextualised against the backdrop of the deep and prolonged impact of austerity‐driven measures on public budgets, production and welfare regimes over the last decade. [ABSTRACT FROM AUTHOR]

The spread of COVID-19 implied a large and fast increase of demand for intensive care services. To face this increase in demand, health care systems need to adapt their response by increasing hospital beds, intensive care unit (ICU) capacity and by (re-)deploying doctors and other personnel. This paper proposes a forecast approach based on the Vector Error Correction model for the daily counts of hospitalized patients with symptoms and of patients in ICU, using publicly available data on the current COVID-19 outbreak in Italy, Switzerland and Spain. The level of analysis is the local government managing the health care system response, which corresponds to regions for Italy. The one-week-ahead forecasts are validated with out-of-sample data over successive weeks; they are found to provide timely and robust prediction of ICU capacity needs in Lombardy, the most-affected Italian region, starting from the sample of the first 2 weeks of data. The same methodology is successfully validated on other Italian regions, Switzerland and Spain. This approach may be used in other countries/regions/provinces to help adapt the health care system response to COVID-19 (or other similar disease); for this purpose, the open-source software code to produce the forecasts is provided with the paper. [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. [ABSTRACT FROM AUTHOR]

The three European countries included in the northwest littoral of the Mediterranean region – Italy, France and Spain – reported their first cases of the SARS-CoV-2 coronavirus early in 2020. The subsequent epidemic strongly impacted these countries, challenging the healthcare systems efficiency, and the capacity and transparency of their public statistics structures. Because public statistics were not fully adapted to such a new health situation, the available official data could not wholly describe the epidemic correctly. This paper developed an indicator derived from the prevalence of SARS-CoV-2 aiming to describe the time and spatial dynamics of the epidemic. Although the analysis was not applied to France due to the lack of data available, the comparative analysis among Spain and Italy highlighted some similarities and certain divergences, partially attributable to the features of each country‘s national demographic patterns and the peculiarities of applied health protocols. Finally, mortality during the first wave of the epidemic has been analysed for the three countries, demonstrating higher mortality index in Spain, although the reported number of SARS-CoV-2 declared deaths does not explain a portion of this mortality excess. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID‐19) is a viral disease which is declared as a pandemic by WHO. This disease is posing a global threat, and almost every country in the world is now affected by this disease. Currently, there is no vaccine for this disease, and because of this, containing COVID‐19 is not an easy task. It is noticed that elderly people got severely affected by this disease specially in Europe. In the present paper, we propose and analyze a mathematical model for COVID‐19 virus transmission by dividing whole population in old and young groups. We find disease‐free equilibrium and the basic reproduction number (R0). We estimate the parameter corresponding to rate of transmission and rate of detection of COVID‐19 using real data from Italy and Spain by least square method. We also perform sensitivity analysis to identify the key parameters which influence the basic reproduction number and hence regulate the transmission dynamics of COVID‐19. Finally, we extend our proposed model to optimal control problem to explore the best cost‐effective and time‐dependent control strategies that can reduce the number of infectives in a specified interval of time. [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]