Modelling output gaps in the Euro Area with structural breaks: The COVID-19 recession

This paper proposes a novel unobserved component model with a COVID-19 structural break in the trend growth rate to model output gaps. Using historical real GDP data for the Euro Area between 1995Q1 and 2022Q1, we test our framework against a battery of competing models, including a standard unobserved components model, a correlated model with a second-order Markov process, a Hodrick–Prescott filter and an augmented version of it. To examine the impact on the fitting performance, we test the inclusion and exclusion of pandemic quarters and we also extend the estimation to a country-level detail. We find that: (i) our suggested model outperforms the competing ones;(ii) when excluding pandemic quarters, the standard unobserved component model outperforms their counterparts;(iii) our model yields the best fitting performance for most of the Euro Area countries and (iv) the Hodrick–Prescott filter model has the poorest fitting performance.

Comparison of the healthcare system of Chile and Brazil: strengths, inefficiencies, and expenditures.

BACKGROUND: Governments in Latin America are constantly facing the problem of managing scarce resources to satisfy alternative needs, such as housing, education, food, and healthcare security. Those needs, combined with increasing crime levels, require financial resources to be solved. OBJECTIVE: The objective of this review was to characterizar the health system and health expenditure of a large country (Brazil) and a small country (Chile) and identify some of the challenges these two countries face in improving the health services of their population. METHODS: A literature review was conducted by searching journals, databases, and other electronic resources to identify articles and research publications describing health systems in Brazil and Chile. RESULTS: The review showed that the economic restriction and the economic cycle have an impact on the funding of the public health system. This result was true for the Brazilian health system after 2016, despite the change to a unique health system one decade earlier. In the case of Chile, there are different positions about which one is the best health system: a dual public and private or just public one. As a result, a referendum on September 4, 2022, of a new constitution, which incorporated a unique health system, was rejected. At the same time, the Government ended the copayment in the public health system in September 2022, excluding illnesses referred to the private sector. Another issue detected was the fragility of the public and private sector coverage due to the lack of funding. CONCLUSIONS: The health care system in Chile and Brazil has improved in the last decades. However, the public healthcare systems still need additional funding and efficiency improvement to respond to the growing health requirements needed from the population.

Nursing Home Residents Hospitalization at the End of Life: Experience and Predictors in Portuguese Nursing Homes.

(1) Background: Nursing Home (NH) residents are a population with health and social vulnerabilities, for whom emergency department visits or hospitalization near the end of life can be considered a marker of healthcare aggressiveness. With the present study, we intend to identify and characterize acute care transitions in the last year of life in Portuguese NH residents, to characterize care integration between the different care levels, and identify predictors of death at hospital and potentially burdensome transitions; (2) Methods: a retrospective after-death study was performed, covering 18 months prior to the emergence of the COVID-19 pandemic, in a nationwide sample of Portuguese NH with 614 residents; (3) Results: 176 deceased patients were included. More than half of NH residents died at hospital. One-third experienced a potentially burdensome care transition in the last 3 days of life, and 48.3% in the last 90 days. Younger age and higher technical staff support were associated with death at hospital and a higher likelihood of burdensome transitions in the last year of life, and Palliative Care team support with less. Advanced Care planning was almost absent; (4) Conclusions: The studied population was frail and old without advance directives in place, and subject to frequent hospitalization and potentially burdensome transitions near the end of life. Unlike other studies, staff provisioning did not improve the outcomes. The results may be related to a low social and professional awareness of Palliative Care and warrant further study.

The effects of COVID-19 on female and male bike sharing users: Insights from Lisbon's GIRA.

Women are among the groups most affected by the pandemic as they are more likely to be dependent on public transport (PT), which was heavily restricted during COVID-19. Thus, there is a need to consider transport alternatives such as bike sharing that can ensure their mobility needs. By conducting a survey to the bike sharing system (BSS) of Lisbon, we explored differences in travel behaviour and attitudes between female and male users before and during COVID-19. We found men to have higher bike ownership rates, a higher modal share of personal bicycle regarding commuting, and more likely to use their own bikes if BSS was unavailable. Conversely, women more frequently combined BSS with PT and were more likely to use PT if BSS was unavailable. Moreover, while men were using BSS more frequently than women pre-pandemic, during COVID-19 women are using BSS as frequently as men. Our research provides evidence on the potential role of BSS as a transport alternative during pandemics, inducing women to take up cycling who otherwise would not cycle, therefore, potentially decreasing the current cycling gender gap. Findings suggest that introducing family/friend discounts and promoting BSS for exercising may increase the share of female cyclists.

The strengths and weaknesses of bike sharing as an alternative mode during disruptive public health crisis: A qualitative analysis on the users' motivations during COVID-19.

The coronavirus pandemic may provoke an increase on our overreliance on private car usage due to a permanent loss of confidence on public transport (PT), threatening current decarbonization efforts of the transport sector. Thus, alternative modes like bike sharing systems (BSS) must be considered. In this study, through conducting 16 semi-structured interviews and by employing thematic analysis, we explore the users' perceptions of using Lisbon's BSS during this pandemic. Our findings show that the observed decrease on BSS usage during the COVID-19 lockdowns was mostly due to mandatory teleworking than to a perceived infection risk. Even during the height of the pandemic, users still turned to BSS to fulfil their essential trip needs. Users considered bike sharing to have a lower infection risk comparatively to PT, with some users joining BSS during the pandemic to specifically avoid using PT. Furthermore, users associate riding a shared bicycle with a pleasant activity that reduces their travel times and costs, while also providing health and environmental benefits. Consequently, bike sharing contributes to the resilience of transport systems by providing its users with a transport alternative perceived to have a low infection risk, ensuring their mobility needs during disruptive events. Findings from this research provide evidence that support policies, such as, expanding BSS coverage areas, optimizing rebalancing operations, introducing shared e-bikes, and implementing segregated cycling lanes alongside BSS. These policies may be particularly effective at increasing the competitiveness of BSS as an alternative mode during disruptive public health crises and beyond.

The role of bike sharing during the coronavirus pandemic: An analysis of the mobility patterns and perceptions of Lisbon's GIRA users.

COVID-19 has dramatically impacted urban mobility, of which public transport (PT) has been particularly affected. With PT ridership plummeting due to infection fears and many people returning to work, there is a danger of a steep rise in car use that would exacerbate environmental and health problems. Therefore, other modes such as bike sharing should be considered as potential alternatives during the coronavirus pandemic. This study focuses on assessing how coronavirus has impacted bike sharing by implementing a travel behaviour survey to the users of GIRA, the bike sharing system (BSS) of Lisbon. While the coronavirus has led some to decrease the frequency of use or quit the system, other users have increased the usage or joined GIRA during the pandemic. Furthermore, most users who have quit or decreased the usage of GIRA justify their decision not so much on avoiding the risk of infection (although for some it is an important reason) but on having stopped commuting due to COVID-19. The survey has also revealed substantial changes not only on the usage patterns of GIRA users but also on their relationship with other modes of transport. While before the pandemic, most respondents were shifting from PT to GIRA, that percentage has declined, with an increase on the share of users replacing walking, private car, and personal cycling. Moreover, the motivations for using bike sharing related with avoiding PT and maintaining a social distance during the trip have gained more relevance. Concurrently, the perceived safety of using PT has drastically declined, and while the perceived safety of using GIRA has also decreased it was in a much smaller scale. Policy insights can be derived from this research on how bike sharing can contribute to a more sustainable and resilient urban transport system. During infectious public health crises such as COVID-19, BSS can be a viable transport alternative, not only providing the population with an affordable mode of transport where social distancing can be maintained in most of the trip but also mitigating a modal shift from PT to the private car.

COVID-19 Time Series Forecasting - Twenty Days Ahead.

The new Coronavirus, responsible for the COVID-19 disease, is the most discussed topic in the current days, and the forecast numbers of new cases and deaths are the most important source of data in governmental decision-making. The present work presents a prediction model with two different approaches concerning the input data, by using Artificial Neural Networks (ANN). The use of a substantial mitigation procedure adopted (mandatory use of masks) was experimented as an input to the network, in order to evaluate the improvement in the results. The ANN forecasting model was demonstrated to predict with higher accuracy within the next twenty days using the information about the mandatory use of face masks. The final results showed that the twenty days ahead forecasting was made with an error of 24,7% and 1,6% for the number of cumulative cases of infection and deaths for Brazil, and 37,9% and 33,8% for Portuguese time series, respectively.

Analyzing and Forecasting Tourism Demand in Vietnam with Artificial Neural Networks

Vietnam has experienced a tourism expansion over the last decade, proving itself as one of the top tourist destinations in Southeast Asia. The country received more than 18 million international tourists in 2019, compared to only 1.5 million twenty-five years ago. Tourist spending has translated into rising employment and incomes for Vietnam’s tourism sector, making it the key driver to the socio-economic development of the country. Following the COVID-19 pandemic, only 3.8 million international tourists visited Vietnam in 2020, plummeting by 78.7% year-on-year. The latest outbreak in early summer 2021 made the sector continue to hit bottom. Although Vietnam’s tourism has suffered extreme losses, once the contagion is under control worldwide, the number of international tourists to Vietnam is expected to rise again to reach pre-pandemic levels in the next few years. First, the paper aims to provide a summary of Vietnam’s tourism characteristics with a special focus on international tourists. Next, the predictive capability of artificial neural network (ANN) methodology is examined with the datasets of international tourists to Vietnam from 2008 to 2020. Some ANN architectures are experimented with to predict the monthly number of international tourists to the country, including some lockdown periods due to the COVID-19 pandemic. The results show that, with the correct selection of ANN architectures and data from the previous 12 months, the best ANN models can be forecast for next month with a MAPE between 7.9% and 9.2%. As the method proves its forecasting accuracy, it would serve as a valuable tool for Vietnam’s policymakers and firm managers to make better investment and strategic decisions.

Self-Disinfecting Paints with the Natural Antimicrobial Substances: Colophony and Curcumin.

The risk of infection arising from indirect sources-namely, contaminated surfaces-has been proved, particularly in healthcare facilities. In the attempt to minimize this problem, innumerable research projects involving the development of surfaces with self-disinfecting properties are being conducted. In this work, wall-paints with self-disinfecting properties were developed with the scope of being applied in environments prone to contamination, such as those at healthcare settings. Our approach was to develop new paint formulations containing two natural plant-based products with known antimicrobial activity-colophony (CLF) and curcumin (CUR). The natural substances were separately incorporated on a commercial paint and their antibacterial activity was evaluated with several bacterial species following ISO 22196. To assess the paints' safety, cytotoxicity tests were performed on HaCaT and A549 cell lines, using tests on extracts and direct contact tests, as suggested by the standardized protocol ISO 10993. In general, both paints containing CLF and CUR were able to reduce the bacterial growth after 24 h, compared with the control, the commercial unmodified paint. Colophony was even able to reduce the number of culturable bacteria by over 2 log for Staphylococcus aureus, Escherichia coli, and Bacillus cereus. Regarding the cytotoxicity tests performed (WST-1, NRU, and LDH), both formulations revealed promising results regardless of the methodology used.

The motivations for using bike sharing during the COVID-19 pandemic: Insights from Lisbon.

Urban mobility has been severely impacted by the coronavirus pandemic, with public transport (PT) particularly affected due to infection risks and fears. The promotion of alternative modes of transport such as bike sharing systems (BSS) has gained a new drive as a possible way of providing an alternative to PT and limit a potential surge in private car use. In this study, we provide insights on the motivations for using bike sharing during the COVID-19 pandemic through a survey to the BSS users of Lisbon (entitled GIRA). Before the coronavirus pandemic, the most influential motivations were those connected to the BSS' Service Coverage & Quality (such as the convenient location of BSS stations near the users' destinations or the availability of shared e-bikes) as well as to the Personal Interests & Well-being of BSS users (namely the pleasure of cycling as well as the perceived environmental and health benefits). With the emergence of the COVID-19 pandemic, although the motivations of Service Coverage & Quality continue to be the most valued by respondents, the motivations associated with using BSS to avoid PT and to maintain a social distance during the trip are now as important as the motivations linked to Personal Interests & Well-being. Furthermore, new users who have joined bike sharing during COVID-19 give more importance to the Social Influence (such as seeing other people using the system or the influence of their social circle) comparatively to those who were already users before the pandemic and continue to use BSS. This research provides evidence on the importance of bike sharing to the resilience of urban transport systems, particularly during disruptive public health crises. It supports that BSS should continue to operate during the coronavirus pandemic as such systems offer a transport alternative to PT that is perceived to be capable of preserving a physical distance.

Binding affinities of 438 HLA proteins to complete proteomes of seven pandemic viruses and distributions of strongest and weakest HLA peptide binders in populations worldwide.

We report detailed peptide-binding affinities between 438 HLA Class I and Class II proteins and complete proteomes of seven pandemic human viruses, including coronaviruses, influenza viruses and HIV-1. We contrast these affinities with HLA allele frequencies across hundreds of human populations worldwide. Statistical modelling shows that peptide-binding affinities classified into four distinct categories depend on the HLA locus but that the type of virus is only a weak predictor, except in the case of HIV-1. Among the strong HLA binders (IC50 ≤ 50), we uncovered 16 alleles (the top ones being A*02:02, B*15:03 and DRB1*01:02) binding more than 1% of peptides derived from all viruses, 9 (top ones including HLA-A*68:01, B*15:25, C*03:02 and DRB1*07:01) binding all viruses except HIV-1, and 15 (top ones A*02:01 and C*14:02) only binding coronaviruses. The frequencies of strongest and weakest HLA peptide binders differ significantly among populations from different geographic regions. In particular, Indigenous peoples of America show both higher frequencies of strongest and lower frequencies of weakest HLA binders. As many HLA proteins are found to be strong binders of peptides derived from distinct viral families, and are hence promiscuous (or generalist), we discuss this result in relation to possible signatures of natural selection on HLA promiscuous alleles due to past pathogenic infections. Our findings are highly relevant for both evolutionary genetics and the development of vaccine therapies. However they should not lead to forget that individual resistance and vulnerability to diseases go beyond the sole HLA allelic affinity and depend on multiple, complex and often unknown biological, environmental and other variables.

Auto-Disinfectant Acrylic Paints Functionalised with Triclosan and Isoborneol-Antibacterial Assessment.

Environmental surface contamination with microorganisms is a serious concern worldwide. Triclosan and isoborneol present good antimicrobial activity. Their immobilisation to paint substrates allows for development of a material that stays effective over a longer time. In this work, we disclosed the preliminary studies to evaluate the antimicrobial activity of the active molecule after being functionalised with isocyanates for further immobilisation on the paint substrate. Overall, the newly developed non-release antimicrobial coating provides an effective way of preventing the spread of diseases and has been proven to inhibit bacterial growth and with a considerable antimicrobial activity towards S. aureus, E. coli, and K. variicola at the tested concentrations.

Analysis and Forecasting Incidence, Intensive Care Unit Admissions, and Projected Mortality Attributable to COVID-19 in Portugal, the UK, Germany, Italy, and France: Predictions for 4 Weeks Ahead.

The use of artificial neural networks (ANNs) is a great contribution to medical studies since the application of forecasting concepts allows for the analysis of future diseases propagation. In this context, this paper presents a study of the new coronavirus SARS-COV-2 with a focus on verifying the virus propagation associated with mitigation procedures and massive vaccination campaigns. There were two proposed methodologies in making predictions 28 days ahead for the number of new cases, deaths, and ICU patients of five European countries: Portugal, France, Italy, the United Kingdom, and Germany. A case study of the results of massive immunization in Israel was also considered. The data input of cases, deaths, and daily ICU patients was normalized to reduce discrepant numbers due to the countries' size and the cumulative vaccination values by the percentage of population immunized (with at least one dose of the vaccine). As a comparative criterion, the calculation of the mean absolute error (MAE) of all predictions presents the best methodology, targeting other possibilities of use for the method proposed. The best architecture achieved a general MAE for the 1-to-28-day ahead forecast, which is lower than 30 cases, 0.6 deaths, and 2.5 ICU patients per million people.

A COVID-19 time series forecasting model based on MLP ANN.

With the accelerated spread of COVID-19 worldwide and its potentially fatal effects on human health, the development of a tool that effectively describes and predicts the number of infected cases and deaths over time becomes relevant. This makes it possible for administrative sectors and the population itself to become aware and act more precisely. In this work, a machine learning model based on the multilayer Perceptron artificial neural network structure was used, which effectively predicts the behavior of the series mentioned in up to six days. The model, which is trained with data from 30 countries together in a 20-day context, is assessed using global and local MSE and MAE measures. For the construction of training and test sets, four time series (number of: accumulated infected cases, new cases, accumulated deaths and new deaths) from each country are used, which are started on the day of the first confirmed infection case. In order to soften the sudden transitions between samples, a moving average filter with a window size 3 and a normalization by maximum value were used. It is intended to make the model's predictions available online, collaborating with the fight against the pandemic.

Recurrent thrombosis of an arteriovenous fistula as a complication of COVID-19 in a chronic hemodialysis patient: A case report.

Patients with end-stage kidney disease are at increased risk of death from coronavirus disease 2019 (COVID-19). In addition, severe COVID-19 has been associated with an increased risk of arterial and venous thromboses. In this report, we describe the case of a hemodialysis patient who developed an otherwise-unexplained thrombosis of an arteriovenous fistula during a symptomatic COVID-19 infection. Despite prompt treatment with three technically successful thrombectomies along with systemic intravenous heparin and two rounds of catheter-directed thrombolysis with tissue plasminogen activator, the fistula rapidly re-thrombosed each time and he required tunneled dialysis catheter placement. He subsequently required admission for hypoxemia from COVID-19 pneumonia and ultimately developed a catheter-related blood stream infection that likely contributed to his death. As the fistula had been previously well functioning and no angiographic explanation for the thrombosis was found, we speculate in this case the recurrent thromboses were related to the hypercoagulable state characteristic of severe COVID-19. Interventionalists performing hemodialysis access procedures should be aware of the prothrombotic state associated with COVID-19 and should consider it when deliberating how to best plan and approach access interventions in patients with symptomatic COVID-19.

Insights on cross-species transmission of SARS-CoV-2 from structural modeling.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the ongoing global pandemic that has infected more than 31 million people in more than 180 countries worldwide. Like other coronaviruses, SARS-CoV-2 is thought to have been transmitted to humans from wild animals. Given the scale and widespread geographical distribution of the current pandemic and confirmed cases of cross-species transmission, the question of the extent to which this transmission is possible emerges, as well as what molecular features distinguish susceptible from non-susceptible animal species. Here, we investigated the structural properties of several ACE2 orthologs bound to the SARS-CoV-2 spike protein. We found that species known not to be susceptible to SARS-CoV-2 infection have non-conservative mutations in several ACE2 amino acid residues that disrupt key polar and charged contacts with the viral spike protein. Our models also allow us to predict affinity-enhancing mutations that could be used to design ACE2 variants for therapeutic purposes. Finally, our study provides a blueprint for modeling viral-host protein interactions and highlights several important considerations when designing these computational studies and analyzing their results.

Structural models of human ACE2 variants with SARS-CoV-2 Spike protein for structure-based drug design.

Emergence of coronaviruses poses a threat to global health and economy. The current outbreak of SARS-CoV-2 has infected more than 28,000,000 people and killed more than 915,000. To date, there is no treatment for coronavirus infections, making the development of therapies to prevent future epidemics of paramount importance. To this end, we collected information regarding naturally-occurring variants of the Angiotensin-converting enzyme 2 (ACE2), an epithelial receptor that both SARS-CoV and SARS-CoV-2 use to enter the host cells. We built 242 structural models of variants of human ACE2 bound to the receptor binding domain (RBD) of the SARS-CoV-2 surface spike glycoprotein (S protein) and refined their interfaces with HADDOCK. Our dataset includes 140 variants of human ACE2 representing missense mutations found in genome-wide studies, 39 mutants with reported effects on the recognition of the RBD, and 63 predictions after computational alanine scanning mutagenesis of ACE2-RBD interface residues. This dataset will help accelerate the design of therapeutics against SARS-CoV-2, as well as contribute to prevention of possible future coronaviruses outbreaks.

The link between bike sharing and subway use during the COVID-19 pandemic: The case-study of New York's Citi Bike.

The full societal impact COVID-19 pandemic is laid bare in urban mobility patterns. This research explored the recently published data on the operation of subway and bike share systems (BSS) during the COVID-19 outbreak in New York city, providing evidence on its impact over urban transport systems, but also on how its different components can work in conjunction. The BSS has proved to be more resilient than the subway system, with a less significant ridership drop (71% vs 90% ridership drop and 50% decrease on the ridership ratio) and an increase on its trips' average duration (from 13 min to 19 min per trip). Moreover, the study found evidence of a modal transfer from some subway users to the bike sharing system. The first effects of the free BSS programs aimed at essential service workers were also evaluated. BSS can improve the resilience of urban transport systems to disruptive events. Overall, this paper offers clues on how bike sharing, and cycling in general, can support the transition to a post-coronavirus society.