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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]

Simple Summary: Recent studies have mostly focused on the treatment of advanced mycosis fungoides and Sézary syndrome and less on the treatment of relapsed or refractory (R/R) cases of cutaneous T-cell lymphoma (CTCL). There is also a paucity of data on the treatment and outcomes of primary cutaneous anaplastic large cell lymphoma and other rare CTCL subtypes. This European observational study provided real-world data on the current treatments used in the management of CTCL across three lines of therapy. As a result, there was a significant level of heterogeneity in treatment types than expected by European guidelines. These findings highlight the lack of consensus in relapsed CTCL, as well as the urgent unmet treatment needs. The treatment pattern of cutaneous T-cell lymphoma (CTCL) remains diverse and patient-tailored. The objective of this study was to describe the treatment patterns and outcomes in CTCL patients who were refractory or had relapsed (R/R) after a systemic therapy. A retrospective chart review study was conducted at 27 sites in France, Germany, Italy, Spain and the United Kingdom (UK) of patients who received a first course of systemic therapy and relapsed or were refractory. Data were collected longitudinally from diagnosis to first-, second- and third-line therapy. The study included 157 patients, with a median follow-up of 3.2 years. In total, 151 proceeded to second-line and 90 to third-line therapy. In the first line (n = 147), patients were treated with diverse therapies, including single- and multi-agent chemotherapy in 67 (46%), retinoids in 39 (27%), interferon in 31 (21%), ECP in 4 (3%), corticosteroids in 3 (2%) and new biological agents in 3 (2%). In the second line, the use of chemotherapy and retinoids remained similar to the first line, while the use of new biologics increased slightly. In sharp contrast to the first line, combination chemotherapy was extremely diverse. In the third line, the use of chemotherapy remained high and diverse as in the second line. From the time of first R/R, the median PFS was 1.2 years and the median OS was 11.5 years. The presented real-world data on the current treatments used in the management of R/R CTCL in Europe demonstrate the significant heterogeneity of systemic therapies and combination therapies, as expected from the European guidelines. [ABSTRACT FROM AUTHOR]

This paper studies the interplay between social distancing and the spread of the COVID-19 disease-a global pandemic that has affected most of the world's population. Our goals are to (1) to observe the correlation between the strictness of social distancing policies and the spread of disease and (2) to determine the optimal adoption level of social distancing policies. The earliest instances of the virus were found in China, and the virus has reached the United States with devastating consequences. Other countries severely affected by the pandemic are Brazil, Russia, the United Kingdom, Spain, India, Italy, and France. Although it is impossible to stop it, it is possible to slow down its spread to reduce its impact on the society and economy. Governments around the world have deployed various policies to reduce the virus spread in response to the pandemic. To assess the effectiveness of these policies, the system's dynamics of the society needs to be analyzed, which is generally not possible with mathematical linear equations or Monte Carlo methods because human society is a complex adaptive system with continuous feedback loops. Because of the challenges with the other methods, we chose agent-based methods to conduct our study. Moreover, recent agent-based modeling studies for the COVID-19 pandemic show significant promise in assisting decision-makers in managing the crisis by applying policies such as social distancing, disease testing, contact tracing, home isolation, emergency hospitalization, and travel prevention to reduce infection rates. Based on modeling studies conducted in Imperial College, increasing levels of interventions could slow the spread of disease and infection. We ran the model with six different percentages of social distancing while keeping the other parameters constant. The results show that social distancing affects the spread of COVID-19 significantly, in turn decreasing the spread of disease and infection rates when implemented at higher levels. We also validated these results by using the behavior space tool with ten experiments with varying social distancing levels. We conclude that applying and increasing social distancing policy levels leads to a significant reduction in infection spread and the number of deaths. Both experiments show that infection rates are reduced drastically when social distancing intervention is implemented between 80% to 100%.

(1) Background: accident rates prove the uneven development of the member countries in the area of work safety. Remedial actions and structural programmes should take into account, e.g., the level of work safety in all European Union (EU) countries. Aim: the identification of differences in the level of work safety in the production sector of EU countries, especially the so-called "old" and "new" EU countries. (2) Methods: for each country UE (in 2008-2018), the relative risk (RR) of an accident at work was determined and a comparative analysis was conducted. (3) Results: an increase in the RR of an accident at work was observed along with an increase in the GDP of a given country. It was found that the level of occupational safety in Sweden and the United Kingdom is higher than in other countries, and lower in Spain and Portugal. In the three largest economies of the EU, Germany, France, and Italy, the RR of the accident in the industrial sector in relation to the national data is one of the lowest in the entire EU, not exceeding 1.3. In The Netherlands, an increase of 1.7 RR of fatal accidents in the industrial sector was observed between 2008 and 2018. (4) Conclusions: RR in the manufacturing sector of the so-called "old" EU is higher than in the so-called "new" EU, which may result from the implementation of Industry 4.0 assumptions in the "old" EU. The presented results and conclusions may be useful in shaping the EU policy in the field of sustainable development of production sectors of individual member countries.

About three-quarters of food exports from European Union (EU) countries goes to the common market in which the free movement of products is ensured. Therefore, it is important to examine from which EU countries the food is exported, what food products they are, and what hazards may be present in these products. The data for research were obtained for 1999-2018 from the Eurostat database (according to the Standard International Trade Classification-SITC) and the Rapid Alert System for Food and Feed (RASFF) database. Then, cluster analysis was performed using joining (tree clustering) and two-way joining methods. The main food exporters were the following countries: Belgium, France, Germany, Italy, the Netherlands, Spain, and the United Kingdom. They exported: cereals, fruits and vegetables, beverages and feeding stuff (in quantitative terms) and fruits and vegetables, meat, dairy products, and cereals (in terms of value). In turn, the most frequently notified hazards in food originating from these countries were: pathogenic micro-organisms, microbial contaminants, metals, composition, foreign bodies, allergens, and pesticide residues. The increase in the number of alert notifications in the RASFF is particularly noticeable in recent years. The results of the research may be useful for activities related to food traceability, changes in the European law, and encouraging the use of extensive methods in agriculture.

We describe the outcomes in cancer patients during the initial outbreak of the COVID-19 in Europe from the retrospective, multi-center observational OnCovid study. We identified 204 cancer patients from eight centers in the United Kingdom, Italy, and Spain aged >18 (mean = 69) and diagnosed with COVID-19 between 26 February and 1 April 2020. A total of 127 (62%) were male, 184 (91%) had a diagnosis of solid malignancy, and 103 (51%) had non-metastatic disease. A total of 161 (79%) had >1 co-morbidity. A total of 141 (69%) patients had >1 COVID-19 complication. A total of 36 (19%) were escalated to high-dependency or intensive care. A total of 59 (29%) died, 53 (26%) were discharged, and 92 (45%) were in-hospital survivors. Mortality was higher in patients aged >65 (36% vs. 16%), in those with >2 co-morbidities (40% vs. 18%) and developing >1 complication from COVID-19 (38% vs. 4%, p = 0.004). Multi-variable analyses confirmed age > 65 and >2 co-morbidities to predict for patient mortality independent of tumor stage, active malignancy, or anticancer therapy. During the early outbreak of SARS-CoV-2 infection in Europe co-morbid burden and advancing age predicted for adverse disease course in cancer patients. The ongoing OnCovid study will allow us to compare risks and outcomes in cancer patients between the initial and later stages of the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

This paper examines the linkages in financial markets during coronavirus disease 2019 (COVID-19) pandemic outbreak. For this purpose, daily stock market returns were used over the period of December 31, 2019-April 20, 2020 for the following economies: USA, Spain, Italy, France, Germany, UK, China, and Romania. The study applied the autoregressive distributed lag (ARDL) model to explore whether the Romanian stock market is impacted by the crisis generated by novel coronavirus. Granger causality was employed to investigate the causalities among COVID-19 and stock market returns, as well as between pandemic measures and several commodities. The outcomes of the ARDL approach failed to find evidence towards the impact of Chinese COVID-19 records on the Romanian financial market, neither in the short-term, nor in the long-term. On the other hand, our quantitative approach reveals a negative effect of the new deaths' cases from Italy on the 10-year Romanian bond yield both in the short-run and long-run. The econometric research provide evidence that Romanian 10-year government bond is more sensitive to the news related to COVID-19 than the index of the Bucharest Stock Exchange. Granger causality analysis reveals causal associations between selected stock market returns and Philadelphia Gold/Silver Index.