Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 21
Filter
1.
JMIR Bioinform Biotech ; 3(1), 2022.
Article in English | PubMed Central | ID: covidwho-2198128

ABSTRACT

Background: In recent decades, the use of artificial intelligence has been widely explored in health care. Similarly, the amount of data generated in the most varied medical processes has practically doubled every year, requiring new methods of analysis and treatment of these data. Mainly aimed at aiding in the diagnosis and prevention of diseases, this precision medicine has shown great potential in different medical disciplines. Laboratory tests, for example, almost always present their results separately as individual values. However, physicians need to analyze a set of results to propose a supposed diagnosis, which leads us to think that sets of laboratory tests may contain more information than those presented separately for each result. In this way, the processes of medical laboratories can be strongly affected by these techniques. Objective: In this sense, we sought to identify scientific research that used laboratory tests and machine learning techniques to predict hidden information and diagnose diseases. Methods: The methodology adopted used the population, intervention, comparison, and outcomes principle, searching the main engineering and health sciences databases. The search terms were defined based on the list of terms used in the Medical Subject Heading database. Data from this study were presented descriptively and followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses;2020) statement flow diagram and the National Institutes of Health tool for quality assessment of articles. During the analysis, the inclusion and exclusion criteria were independently applied by 2 authors, with a third author being consulted in cases of disagreement. Results: Following the defined requirements, 40 studies presenting good quality in the analysis process were selected and evaluated. We found that, in recent years, there has been a significant increase in the number of works that have used this methodology, mainly because of COVID-19. In general, the studies used machine learning classification models to predict new information, and the most used parameters were data from routine laboratory tests such as the complete blood count. Conclusions: Finally, we conclude that laboratory tests, together with machine learning techniques, can predict new tests, thus helping the search for new diagnoses. This process has proved to be advantageous and innovative for medical laboratories. It is making it possible to discover hidden information and propose additional tests, reducing the number of false negatives and helping in the early discovery of unknown diseases.

2.
European journal of public health ; 32(Suppl 3), 2022.
Article in English | EuropePMC | ID: covidwho-2102273

ABSTRACT

Background Effective contact tracing, vaccination, and isolation of cases of SARS-CoV-2 infection and their high-risk contacts constituted measures to contain the spread of COVID-19. In Portugal, in October 2021, low-risk cohabitants were lifted the obligation to isolate. The aim of this study was to estimate the relative risk of infection for close contacts, regarding the type of close contact and being cohabitants. Methods A descriptive longitudinal study, with an analytical component was performed. Sociodemographic and epidemiologic data from close contacts and confirmed cases in Loures and Odivelas, between October and November 2021, was collected from a regional database and from Trace COVID-19 platform. We performed a descriptive analysis and estimated the relative risk of SARS-CoV-2 positive test, stratified by type of contact and cohabitation, with 95% confidence level. Results We identified 200 confirmed cases and 428 people who were close contacts, corresponding to 502 different close contacts (59 people had contact with more than a case). From 502 close contacts, 268 were classified as low-risk and 230 as high-risk. Full time cohabitation was present in 310 of close contacts. Between contact tracing day and the next 4 weeks, 58 (10.9%) of close contacts tested positive. Risk of high-risk contacts testing positive was 2.7 [1.5-4.6], compared with low-risk contacts. Risk of cohabitants testing positive was 3.5 [1.6-7.7], compared with non-cohabitants. Risk of a high-risk cohabitant testing positive was 2.2 [1.1-4.4], compared with low-risk cohabitants. There was no higher risk of high-risk cohabitants testing positive compared with high-risk non-cohabitants. Same was true for low-risk cohabitants and non-cohabitants. Conclusions These results allow us to understand how to better stratify close contacts and apply isolation measures, according to the risk of testing positive. Further studies should be developed to assess the impact of other variables. Key messages • We identified an increased risk of testing positive in high-risk contacts, and in cohabitants. • Cohabitants could be stratified regarding being high or low-risk, with different measures being applied.

3.
Event Management ; 26(7):1565-1576, 2022.
Article in English | CAB Abstracts | ID: covidwho-2055459

ABSTRACT

The purpose of this article is to explore the perceived impacts of COVID-19 in participating in academic events, relating the attractiveness of destinations and tourist opportunities as pull factors. Remote communication technologies have also been put into perspective to understand how they can influence future participation in events. A partial least squares (PLS) was used to test both research model and hypotheses. This study supports that COVID-19 has profoundly affected participation in events and has an impact on the attractiveness of the destination and tourist opportunities. During the pandemic context, remote communication technologies have replaced physical presence at events and are expected to continue to be present in future events. However, it is anticipated that technology will not permanently replace physical presence at events, due to the social character it represents. Also, destination attractiveness and tourist opportunities will be important in the decision to participate in physical events. This article addresses the current topic of COVID-19 and the impact on the future of physical events and gives some indicators that may contribute to a better planning of destinations in the recovery of this sector, namely by enhancing the tourist attractiveness of destinations as pull factor.

4.
International Conference on Tourism, Technology and Systems, ICOTTS 2021 ; 284:385-396, 2022.
Article in English | Scopus | ID: covidwho-1899049

ABSTRACT

The purpose of this paper is to analyse the consumer behaviour regarding the use of online food delivery apps during Covid-19 sanitary restrictions in Portugal. The study explores the behaviour of consumers towards digital food ordering and delivery services, and its impact in the restaurant industry. An online survey was applied, via email and social networks, for a period of 6 months. The questions were based mainly on consumers post-purchase behaviour and perceptions when using online food delivery apps during Covid-19 sanitary restriction in Portugal. In total, 258 valid responses were collected. The data was analysed using Microsoft Excel software. Findings suggest that lockdown, social distancing, and restrictions imposed to many activities, such as restaurants, lead to an increased use of Food Delivery App (FDA). Most respondents consider this increased use of FDA helped to maintain the activity of the food industry, and therefore to save many job positions at the restaurants. This research contributes to a better understanding of consumers behaviour towards the use of FDA and can provide a strategic contribution to restaurant managers and owners. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

5.
Eur Rev Med Pharmacol Sci ; 26(5): 1431-1432, 2022 03.
Article in English | MEDLINE | ID: covidwho-1897298
6.
Int J Environ Sci Technol (Tehran) ; : 1-22, 2022 May 03.
Article in English | MEDLINE | ID: covidwho-1821019

ABSTRACT

The Covid-19 pandemic has negatively disrupted the way our economy and society functions. Nonetheless, there have also been some positive externalities of the pandemic on the environment. This paper aims to evaluate the concentration of nitrogen dioxide in Brazilian metropolitan regions after the policies adopted to confront Covid-19. In terms of methodological approach, the study employs cross-sectional quantitative analyses to compare the period of 36 days, i.e., 12 March to 16 April-before (in 2019) and after (in 2020) the pandemic declaration. The data were obtained from the Sentinel 5-P low-Earth polar satellite concerning Brazilian metropolitan regions (n = 24). Thorough spatial and statistical analyses were undertaken to identify the pre- and during pandemic nitrogen dioxide concentrations. Complementarily, Spearman's correlation test was performed with variables that impact air quality. The study results a fall in nitrogen dioxide concentration levels in 21 of the 24 metropolitan regions which was observed. The Spearman's correlation coefficient between the nitrogen dioxide variation and the vehicle density was 0.485, at a significance level of 0.05. With these findings in mind, the paper advocates that while the pandemic has a significant negative consequence on the health of population globally, a series of measures that result in a new social organization directly interfere in the reduction of air pollution that contributes to the quality of the air we breathe.

7.
Data Science for COVID-19 Volume 1: Computational Perspectives ; : 1-24, 2021.
Article in English | Scopus | ID: covidwho-1787938

ABSTRACT

Following the World Health Organization proclaims a pandemic due to a disease that originated in China and advances rapidly across the globe, studies to predict the behavior of epidemics have become increasingly popular, mainly related to COVID-19. The critical point of these studies is to discuss the disease’s behavior and the progression of the virus’s natural course. However, the prediction of the actual number of infected people has proved to be a difficult task, due to a wide range of factors, such as mass testing, social isolation, underreporting of cases, among others. Therefore, the objective of this work is to understand the behavior of COVID-19 in the state of Ceará to forecast the total number of infected people and to aid in government decisions to control the outbreak of the virus and minimize social impacts and economics caused by the pandemic. So, to understand the behavior of COVID-19, this work discusses some forecast techniques using machine learning, logistic regression, filters, and epidemiologic models. Also, this work brings a new approach to the problem, bringing together data from Ceará with those from China, generating a hybrid dataset, and providing promising results. Finally, this work still compares the different approaches and techniques presented, opening opportunities for future discussions on the topic. The study obtains predictions with score of 0.99 to short-term predictions and 0.93 to long-term predictions. © 2021 Elsevier Inc. All rights reserved.

8.
Archivos De Medicina ; 21(2):535-547, 2021.
Article in English | Web of Science | ID: covidwho-1668007

ABSTRACT

Objective: to describe the sociodemographic, epidemiological and clinical characteristics of the initial cases of Covid-19 in the municipality of Sobral, Ceara, Brazil. Materials and methods: descriptive, temporal and quantitative epidemiological study, developed in the municipality of Sobral - Ceara, Brazil, with 110 confirmed cases of Covid-19. Descriptive and analytical analysis was performed, using the Chi-square test and Logistic Regression to verify the association between variables. The level of significance was set at 95% (p <= 0.05). Results: it was observed that 60% of cases occurred in women, 74.5% were adults between 20 and 59 years old, 15.5% health workers and the lethality rate was 1.8%. In 58.2% of cases the main reporting unit was the hospital, 10% required hospitalization, and 64.5% were diagnosed with rapid tes. The main symptoms manifested were: cough (58.2%), fever (57.3%), sore throat (36.4%) and difficulty breathing (31.9%). There was an association between age and the presence of fever, cough and sore throat (p=0.05). Conclusion: the results suggest that older people are more susceptible to some symptoms when compared to younger people. Associated with global estimates, this work can provide subsidies for Covid-19 prevention and control actions in small and medium-sized Brazilian municipalities.

9.
Quimica Nova ; 44(10):1236-1244, 2021.
Article in English | Web of Science | ID: covidwho-1622982

ABSTRACT

This work aims to evaluate the possible relations between the confirmed daily cases of COVID-19 and the environmental parameters for the Cuiaba-Varzea Grande conurbation in the state of Mato Grosso, Brazil. The data sets used to cover the rainy-dry periods, from January to December 2020, were achieved from a database of government institutions, and processed through the Spearman correlation test. Our results showed that atmospheric pressure and fire radiative power has a significant positive correlation, suggesting that these parameters favor the transmission of COVID-19. On the other hand, the relative humidity of the air and the total column of water vapor showed a significant negative correlation with the number of confirmed daily cases of COVID-19, which indicates that the water vapor present in the atmosphere acts in the regulation of virus transmission. Thus, taking into account the results obtained, there is a need for collaborative policies and measures among the three spheres of executive power in Brazil, to act in the surveillance of fire cases, which can favor the transmission of COVID-19. In addition, prevention and protection measures aimed at reducing the spread of coronavirus continue to be indispensable.

10.
Allergy: European Journal of Allergy and Clinical Immunology ; 76(SUPPL 110):484-485, 2021.
Article in English | EMBASE | ID: covidwho-1570404

ABSTRACT

Background: COVID-19 vaccines are being administered all over the world, but information is lacking about the frequency and type of allergic reactions associated to these new vaccines. Method: Retrospective study of health care professionals (HCP) from our hospital who received COVID 19 vaccine Comirnaty, between 29/12/2020 and 20/2/2021. We reviewed clinical data, particularly the immediate reactions after the administration (<6h), skin tests (ST) and graded vaccine administration. Following national guidelines, all HCP with previous history of food, drug or hymenoptera venom allergy or idiopathic anaphylaxis (IA) were first evaluated by an allergist. Vaccination was postponed if HCP had previous history of IA and/or recurrent anaphylaxis (RA), severe allergic reactions to vaccines and mast cell activation syndromes. ST to the vaccine (prick and intradermal) were performed in HCP with IA and/ or RA, severe allergic reactions to vaccines and HCP with immediate reactions to the 1st dose. Graded administration of the vaccine (0.1+0.2cc after 30') was performed in the postponed HCP and the ones with immediate reactions to the 1st dose. Results: From 3073 HCP who received the vaccine, 74.2% were female, mean age 40.2 years-old ± 13.4, 316 (10.3%) were evaluated by an allergist and 4 (1.3%) postponed the administration and performed allergy investigation. 2955 HCP (97%) were able to receive the 2 doses of the vaccine. 118 employees received only one dose: 98 had COVID-19 meanwhile, 7 got pregnant, 13 due to other conditions. Adverse reactions to the vaccine with possible hypersensitivity mechanisms, occurred in 17 (0.6%) HCP, 12 on the 1st dose and 5 on the 2nd dose. Observed reactions were 6 (0.2%) urticaria, 5 (0.16%) pruritus with or without flushing, 2 (0.07%) anaphylaxis (mild), 2 (0.07%) flushing and hoarseness, 1 (0.03%) flushing and nausea and 1 (0.03%) asthma exacerbation. ST with the vaccine were performed in 4 HCP, all negative in the immediate reading and 1 positive in non-immediate reading. 7 HCP undertook the graded administration with the vaccine: 6 tolerated, but one reproduced the immediate urticaria with 0.1cc of the vaccine (0.03% vaccine allergy). Conclusion: In the evaluated sample, suspicious allergic reactions to COVID19 vaccine Commirnaty were rare and allergy was only confirmed in one HCP. The allergist initial evaluation was essential for a safe risk stratification and permitted the non-exclusion of a considerable number of HCP from the vaccination program.

11.
Annals of Hepatology ; 24, 2021.
Article in English | EMBASE | ID: covidwho-1446404

ABSTRACT

Introduction: Hepatitis B and C infection are responsible for more than 300 million of chronic liver disease patients all over the world. One goal of WHO 2030 agenda is the eradication of hepatitis B and C. However poverty is a great obstacle to achieve this goal. In Brazil, more than 13 million of people live in poverty (PLP) and could be vulnerable to HBV and HCV. Objectives: This study aims to determine HBV and HCV prevalence and analyze the response to HBV vaccination by measuring anti -HBs antibodies in serum samples from PLP. Methods: This was a cross

12.
Annals of Hepatology ; 24, 2021.
Article in English | EMBASE | ID: covidwho-1446399

ABSTRACT

Introduction: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread rapidly around the world, posing a major threat to human health and the economy. Chronic Liver disease (CLD) patients could be at high risk for COVID-19. At this moment, there is little data about biochemical variation according to liver disease along to COVID-19 infection. Objectives: This study aims to report the levels of biochemical markers in CLD patients with or without COVID-19 to give more information that could help clinical monitoring. Methods: A total of 66 CLD patients were included in this study during year of 2020. Study was approved by Brazilian Ethics Committee. Blood and respiratory samples were collected after signed informed consent. At baseline and during follow-up, all subjects included in this study underwent routine examination, monitoring of biochemical markers, and SARS-CoV-2 nucleic acid testing with a median follow-up interval of 15 days. Results: Most of individuals were male 56% (37/66) and mean age of population was 49±17 years. Six out 66 CLD patients were SARS CoV-2 RNA positive at baseline. At the end of follow-up, all these 6 patients achieved SARS-CoV-2 clearance. At least once during follow-up, the CLD group versus CLD/COVID-19 group, 50% (30/60) vs. 33% (2/6) had abnormal alanine aminotransferase;47% (28/60) vs. 17% (1/6) had abnormal aspartate aminotransferase;60% (36/60) vs. 67% (4/6) had abnormal γ-glutamyltransferase, 32% CLD patients (19/60) had abnormal total bilirubin levels vs. none of the CLD/COVID-19 group. Conclusions: Previous liver disease did not seem to increase the biochemical levels, except GGT, during COVID-19 infection. However, liver function monitoring is still essential for both COVID-19 patients with and without liver disease.

13.
Physics Education ; 56(5), 2021.
Article in English | Scopus | ID: covidwho-1369027

ABSTRACT

This paper suggests possible futures for astronomy activities in a post-COVID environment. A corpus of video recordings of astronomy education sessions held in different settings allowed the authors to analyse how sessions are achieved, with inclusive focus on the astronomers, the public attending sessions, and people looking through telescopes. Videos were recorded in Portugal in 2019 and the beginning of 2020. From these video data, and experience as science educators, the authors identify practices that are characteristic of astronomy education-some of these will no longer be safe procedures while living with COVID-19. With planning, both indoor and outdoor astronomy education sessions can continue safely without sacrificing the excitement of seeing objects in the sky. There is more to astronomy than looking through a telescope. © 2021 The Author(s). Published by IOP Publishing Ltd.

14.
8th IEEE International Conference on Industrial Engineering and Applications, ICIEA 2021 ; : 401-405, 2021.
Article in English | Scopus | ID: covidwho-1276446

ABSTRACT

During the Covid-19 pandemics, many companies had to cease their activities due to the scarcity of raw material supply or availability of goods' transportation modes. Simulataneously, vehicles from different enterprises were still performing similar routes, delivering goods to the same clients or nearby locations, with a small percentage of their capacity filled. The ability to optimize resource usage, re-adjust, and search for alternatives should depend on an integrated real-time decision. Open collaboration between stakeholders in terms of human resources, assets, and data sharing is vital. Industry 4.0 and mostly additive manufacturing can leverage the production closer to the client, eliminating logistic intermediaries' steps, cutting warehouse expenses and delivery costs, and promoting sustainability. Therefore, this paper proposes an adapted framework from the 5W1H (Who, Why, What, Where, When, and How) quality management methodology to organize the supply chain based on the client's personalized inputs and stakeholders' integration. © 2021 IEEE.

15.
Computer Communications ; 177:1-9, 2021.
Article in English | Scopus | ID: covidwho-1275229

ABSTRACT

Wireless communication systems play an essential role in everyday life situations and enable a wide range of location-based services to their users. The imminent adoption of 5G networks worldwide and the future establishment of next-generation wireless networks will allow various applications, such as autonomous vehicles, connected robotics, and most recently, crowd monitoring for fighting infectious diseases, such as COVID-19. In this context, radio localization techniques have become an essential tool to provide solid performance for mobile positioning systems, through increased accuracy or less computational time. With this in mind, we propose a trilateration-based approach using machine learning (ML) and sequential least-square programming (SLSQP) optimization to estimate the outdoor position of mobile terminals in cellular networks. The ML technique employed is the k-nearest neighbors (k-NN). The optimization methods analyzed are Nelder–Mead (NM), genetic algorithms (GA), and SLSQP. Different environments (noise-free and noisy) and network scenarios (different numbers of base stations) are considered to evaluate the approaches. Numerical results indicate that the k-NN/SLSQP technique has similar accuracy compared to the k-NN/GA with eight generations. Both perform better than k-NN/NM in all scenarios and environments. When comparing computational times, our proposal is considerably more time-efficient. Aside from that, SLSQP computational time is less affected by network scenarios with more base stations in comparison with GA. That feature is significant considering the ultra-dense base station deployment forecasted for the next-generation cellular networks. © 2021 Elsevier B.V.

16.
SpringerBriefs in Applied Sciences and Technology ; : 1-13, 2021.
Article in English | Scopus | ID: covidwho-968063

ABSTRACT

The task known as prediction is widely applied in several different areas of knowledge, from popular applications such as weather forecasting, going through supply chain management, an increasing range of adoption in healthcare and, more specifically in epidemiology, the central topic of this book. The new challenges brought with the COVID-19 pandemic highlighted the possibilities and necessity of using prediction techniques to support decisions related to epidemiology in both managerial and clinical areas. In practice, the current outbreak created a strong need for the adoption of different computational models to support both medical teams and public health administrators. The methods vary from simple linear regressions to very complex algorithms based on Artificial Intelligence (AI) techniques. The present chapter contextualizes the use of prediction for decision support as a foundation of the following chapters which are focused on the application for the COVID-19 pandemic time series. With such a large number of methods for data-driven predictions, a clear distinction between explanation and prediction is firstly provided. From there, a methodological framework is presented, from the data source definition and selection of countries as references for the analysis, going through data handling for validation, until the definition of the evaluation criteria for the proposed models. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

17.
SpringerBriefs in Applied Sciences and Technology ; : 89-98, 2021.
Article in English | Scopus | ID: covidwho-968062

ABSTRACT

The support provided by geographic data and the corresponding processing tools can play an essential role to support decision-making process, especially for public healthcare during the current pandemic outbreak of the COVID-19. Geographic data collection may be challenging when is necessary to obtain precise latitude and longitude, for example. The current chapter presents a new tool for the geographic location prediction of new cases of COVID-19, considering the confirmed cases in the city of Fortaleza, capital of the State of Ceara, Brazil. The methodology is based on a sequential approach of four clustering algorithms: Agglomerative Clustering, DBSCAN, Mean Shift, and K-Means followed by a two-dimensional predictor based on the Kalman filter. The results are presented following a case study approach with different examples of implementation and the corresponding analysis of the results. The proposed technique could generally predict the trend of the infection geographically in Fortaleza and effectively supported the decision-making process of public healthcare analysts and managers from the Secretariat of Health of the State of Ceara. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

18.
SpringerBriefs in Applied Sciences and Technology ; : 55-68, 2021.
Article in English | Scopus | ID: covidwho-968061

ABSTRACT

Considering the application of prediction techniques to support the decision-making process during a dynamic environment such as the one faced during the COVID-19 pandemic, demands the evaluation of several different strategies to compare and define the most suitable solution for each necessity of prediction. Analyzing the epidemic time series, for example, the number of new confirmed cases of COVID-19 per day, classic compartmental models or linear regressions may not provide results with enough precision to support managerial or clinical decisions. The application of nonlinear models is an alternative to improve the performance of these models. The Kalman Filter (KF) is a state-space model that is used in several applications as a predictor. The filter algorithm requires low computational power and provides estimates of some unknown variables given the measurements observed over time. In this chapter, the KF predictor is considered in the analysis of five countries (China, United States, Brazil, Italy, and Singapore). Similarly to the ARIMA methodology, the results are evaluated based on three criteria: R2 Score, MAE (Mean Absolute Error), and MSE (Mean Square Error). It is important to notice that the definition of a predictor for epidemiological time series shall be carefully evaluated and more complex implementations do not always represent a better prediction on average. For the proposed KF predictor, there were specific time-series samples with no satisfactory result, achieving a negative R2 Score, for example, while, on the other, other samples achieved higher R2 Score and lower MAE and MSE, when compared to other linear predictors. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

19.
SpringerBriefs in Applied Sciences and Technology ; : 41-54, 2021.
Article in English | Scopus | ID: covidwho-968060

ABSTRACT

When considering time-series forecasting, the application of autoregressive models is a popular and simple technique that is usually considered. In this chapter, we present the basic theoretical aspects and assumptions of the ARIMA—Autoregressive Integrated Moving Average model. It is considered for the prediction of the COVID-19 epidemiological data series of five different countries (China, United States, Brazil, Italy, and Singapore), each of them with specific curves, which are results of the virus reproduction itself but also of policies and government decisions during the pandemic spread. The discussion about the results is performed with the focus on the three evaluation criteria of the model: R2 Score, MAE, and MSE. Higher R2 Score was obtained when the sample time series was smoothly increasing or decreasing. The error metrics were higher when the prediction was performed for oscillating data series. This may indicate that the use of ARIMA models may be suitable as a prediction tool for the COVID-19 when the country is not facing severe oscillations in the number of infections. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

20.
SpringerBriefs in Applied Sciences and Technology ; : 15-39, 2021.
Article in English | Scopus | ID: covidwho-968059

ABSTRACT

The process of decision-making when dealing with infectious diseases is firmly based on mathematical modeling nowadays. One usual approach is to consider the adoption of compartmental methods such as SIR and SEIR and a large number of corresponding variations for modeling and prediction epidemic time series. Nevertheless, the COVID-19 epidemic characteristics and curves are apparently challenging the results obtained by these models. This chapter presents the results of two traditional compartmental models, SIR (Susceptible—Infected–Recovered) and SEIR (Susceptible–Exposed–Infected–Recovered), and an adapted version of the SEIR, called SEIR with Intervention, which captures the impact of containment measures for the dynamics of the infection rate. The analysis is performed for five countries: China, United States, Brazil, Italy, and Singapore, each of them with specific characteristics of dealing with the pandemic. A sequence of results is presented, considering different parameters, in order to understand the feasibility of application for each model. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

SELECTION OF CITATIONS
SEARCH DETAIL