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The main objective of this paper is to propose a novel SEIR stochastic epidemic model. A distinguishing feature of this new model is that it allows us to consider a setup under general latency and infectious period distributions. To some extent, queuing systems with infinitely many servers and a Markov chain with time-varying transition rate comprise the very technical underpinning of the paper. Although more general, the Markov chain is as tractable as previous models for exponentially distributed latency and infection periods. It is also significantly more straightforward and tractable than semi-Markov models with a similar level of generality. Based on stochastic stability, we derive a sufficient condition for a shrinking epidemic regarding the queuing system's occupation rate that drives the dynamics. Relying on this condition, we propose a class of ad-hoc stabilising mitigation strategies that seek to keep a balanced occupation rate after a prescribed mitigation-free period. We validate the approach in the light of the COVID-19 epidemic in England and in the state of Amazonas, Brazil, and assess the effect of different stabilising strategies in the latter setting. Results suggest that the proposed approach can curb the epidemic with various occupation rate levels if the mitigation is timely. • We propose queue-based stochastic epidemic model for viral epidemics. • The model is tractable yet general and applicable to large populations. • It describes epidemics with general latency and infectious periods. • We derive a control rule that ensure a shrinking epidemic. • Our experiments illustrate the effectiveness of the proposed control rule. [ABSTRACT FROM AUTHOR]

This paper examines the impacts of emergency cash-transfers on individuals' social distancing behaviour and beliefs about COVID-19. We focus on the impacts of "Auxilio Emergencial" (AE): a large-scale cash-transfer in Brazil targeting low-income individuals who were unemployed or informally employed during the pandemic. To identify causal effects we exploit exogenous variation, arising from the AE design, in individuals' access to the cash-transfer programme. Using data from an online survey, our results suggest that eligibility to the emergency cash transfer led to a reduced likelihood of individuals contracting COVID-19, likely to have been driven by a reduction in working hours. Moreover, the cash transfer seems to have increased perceptions about the seriousness of coronavirus, while also exacerbating misconceptions about the pandemic. These findings indicate effects of emergency cash-transfers in determining individuals' narratives about a pandemic, in enabling social distancing and potentially in reducing the spread of the disease. [ABSTRACT FROM AUTHOR]

• The pandemic-crisis effects on Brazilian manufacturing industries are investigated. • An input-output model is employed to address its sectorial composition and linkages. • The pandemic-crisis has further harmful effects on knowledge-intensive industries. • EIHC promotes high-tech industries, while it showed higher degree of import dependence. • COVID-19 may be crucial to rethink industrial policy space within societal purposes. The COVID-19 crisis represents a turning point to rethink industrial policy spaces and countries' productive autonomy, especially for developing economies, such as Brazil, that call for new ways of thinking about manufacturing within societal purposes. Using an input-output model, the paper explores the pandemic-crisis-effects in the case of the Brazilian manufacturing sector. We find that the pandemic-crisis has harmful effects on the Brazilian productive structure, revealing the dependence on imports of the Brazilian Health System. Reductions in manufacturing gross output and value-added are mainly seen in knowledge-intensive subsectors, followed by job losses and tax revenue reduction. We suggest that the pandemic points to some roads to reindustrialization and resilience, given the reorganization of international production networks and the growing dependence on imports in key manufacturing sectors. Thus, the potential role of the manufacturing sector to achieve inclusive and sustainable growth reveals the importance of building productive capacity beyond the pandemic. [ABSTRACT FROM AUTHOR]

Novel coronavirus respiratory disease COVID-19 has caused havoc in many countries across the globe. In order to contain infection of this highly contagious disease, most of the world population is constrained to live in a complete or partial lockdown for months together with a minimal human-to-human interaction having far reaching consequences on countries' economy and mental well-being of their citizens. Hence, there is a need for a good predictive model for the health advisory bodies and decision makers for taking calculated proactive measures to contain the pandemic and maintain a healthy economy. This paper extends the mathematical theory of the classical Susceptible–Infected–Removed (SIR) epidemic model and proposes a Generalized SIR (GSIR) model that is an integrative model encompassing multiple waves of daily reported cases. Existing growth function models of epidemic have been shown as the special cases of the GSIR model. Dynamic modeling of the parameters reflect the impact of policy decisions, social awareness, and the availability of medication during the pandemic. GSIR framework can be utilized to find a good fit or predictive model for any pandemic. The study is performed on the COVID-19 data for various countries with detailed results for India, Brazil, United States of America (USA), and World. The peak infection, total expected number of COVID-19 cases and thereof deaths, time-varying reproduction number, and various other parameters are estimated from the available data using the proposed methodology. The proposed GSIR model advances the existing theory and yields promising results for continuous predictive monitoring of COVID-19 pandemic. • Proposed a generalized SIR (GSIR) framework for pandemic modeling and prediction. • An integrative model that captures the pandemic data via distinct waves emerging at different times. • Explicit expressions are derived for the susceptible, infected and removed populations. • Dynamic modeling of system parameters that can capture the impact of policy decisions. • GSIR model is tested on COVID-19 data and can serve as a good alternative to existing methods. [ABSTRACT FROM AUTHOR]

This paper provides an overview of the regulatory responses to Covid-19 from the regulatory authorities of Brazilian states. This paper aims to provide new insights into the operationalization of the human rights to water and sanitation in the Brazilian regulatory authorities' actions during a health emergency. We find that communities located in unserved areas were not mentioned in the regulatory responses, nor were people in vulnerable situations. Equity and non-discrimination principles were correlated more with economic measures. This study has also identified the absence of responses related to access to sanitation facilities, with normative contents related to the issue not appearing in the content analysis. • The presence of HRtWS is unequal in the regulatory responses to the Covid-19 pandemic. • Communities without piped network were ignored in the regulatory responses to the Covid-19 pandemic. • No mentions regarding vulnerable people were identified in the regulatory responses to the Covid-19 pandemic. • No responses were identified regarding access to sanitation facilities. • There is an absence of a plural understanding of the sustainability in the regulatory responses to the Covid-19 pandemic. [ABSTRACT FROM AUTHOR]

Extracting information and discovering patterns from a massive dataset is a hard task. In an epidemic scenario, this data has to be integrated providing organization, agility, transparency and, above all, it has to be free of any type of censorship or bias. The aim of this paper is to analyze how coronavirus contamination has evolved in Brazil applying unsupervised analysis algorithms to extract information and find characteristics between them. To achieve this goal we describe an implementation that uses data about Covid-19 spread in Brazilian states (26 states and the federal district), applying a Time Series Clustering technique based on a K-Means variation, using Dynamic Time Warping as a similarity metric. We used data reported by the Ministry of Health in Brazil, referring to deaths per 100k inhabitants, during 452 days from the first reported death in each state. Two analyzes were performed, one considering 3 clusters and the other with 6 clusters. Through these analysis, 3 patterns of responses to the pandemic can be observed, ranging from one of greater to lesser control of the pandemic, although in recent months all clusters showed a highly increase in the number of deaths. The identification of these patterns is important to highlight possible actions and events, as well as other characteristics that determine the correct or incorrect public decision-making in combating the Covid-19 pandemic. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic (SARS-CoV-2 virus) is the global crisis of our time. The absence of mass testing and the relevant presence of asymptomatic individuals causes the available data of the COVID-19 pandemic in Brazil to be largely under-reported regarding the number of infected individuals and deaths. We develop an adapted Susceptible-Infected-Recovered (SIR) model, which explicitly incorporates the under-reporting and the response of the population to public health policies (confinement measures, widespread use of masks, etc). Large amounts of uncertainty could provide misleading predictions of the COVID-19 spread. In this paper, we discuss the role of uncertainty in these model-based predictions, which is illustrated regarding three key aspects: (i) Assuming that the number of infected individuals is under-reported, we demonstrate anticipation regarding the infection peak. Furthermore, while a model with a single class of infected individuals yields forecasts with increased peaks, a model that considers both symptomatic and asymptomatic infected individuals suggests a decrease of the peak of symptomatic cases. (ii) Considering that the actual amount of deaths is larger than what is being registered, we demonstrate an increase of the mortality rates. (iii) When we consider generally under-reported data, we demonstrate how the transmission and recovery rate model parameters change qualitatively and quantitatively. We also investigate the "the uncertainty tripod": under-reporting level in terms of cases, deaths, and the true mortality rate of the disease. We demonstrate that if two of these factors are known, the remainder can be inferred, as long as proportions are kept constant. The proposed approach allows one to determine the margins of uncertainty by assessments on the observed and true mortality rates. [ABSTRACT FROM AUTHOR]

The outbreak of COVI-19 and the restrictive measures on the mobility of people in Brazil have raised serious concerns about the survival and recovery of passenger transport companies, especially those that generate public agglomerations. There are some policy proposals that aim to recover this set of sectors in the face of the adverse effects of the COVID-19 outbreak. This study contributes to this debate in course and analyzes the economic effects of two scenarios of recovery for this type of transport services in the Brazilian economy up to the end of 2022: (i) one with a 50% recovery until the end of 2021 and (ii) another with a 50% sectorial recovery until June 2022. This distinction allows us to assess the impact of the speed of recovery. In both scenarios, we also consider likely changes in the labor market, family preferences, and government spending. To accomplish this task, we developed a dynamic computable general equilibrium model that recognizes a Social Accounting Matrix (SAM) and has details of the transport sectors. The main findings suggest that the drop in these transport services is the main contributing factor to the decline in the Brazilian GDP growth (−2.2%) in the period of social distance measures. However, faster recovery of these sectors can generate a marginal effect of 0.5 percentage points on GDP at the end of 2021. In the recovery period, due to the redistributive effects of income, the family demand for public transport is expected to grow post- COVID-19 outbreak, while the demand for private transport is reduced, especially in the basket of goods of the poorest households. Vehicle, bus, and aircraft manufacture seems sensitive to the recovery time of the demand for transport services with public agglomerations. • The paper develops a SAM-based dynamic CGE models detailing the transport sectors. • With a faster recovery in the transport service, the endogenous response of the Brazilian economy is more intense. • The income effect of the emergency resource policy alters the modal distribution in the household budget. • Manufacture of vehicles, buses and aircraft are sensitive to the recovery time scenarios. [ABSTRACT FROM AUTHOR]

Social distancing has been adopted as a strategy to contain the advance of the pandemic until specific solutions to combat the virus are available. Many economic sectors and social activities need to continue despite the viruses, and most social distancing policies disregard individual choices, resulting in greater or lesser adherence to isolation. In this paper, we characterize the differentiation of access to essential activities within Brazilian cities during social distancing and the intention to maintain behavior changes concerning these activities in the post-pandemic period. The results showed that the behavior regarding essential activities during the pandemic is not independent of variables associated with pre-pandemic travel patterns, isolation conditions, and socioeconomic factors. Post-pandemic intention proved to be statistically dependent on factors associated with the activities' characteristics and experiences during the pandemic. Accessibility-oriented planning can guide urban and transportation policies more equitably through the identification of the most affected groups by activity disruption. At the same time, the incorporation of new routines less dependent on displacement offers opportunities to review public space. [Display omitted] • Less schooling, self-employed or less frequent commuters changed less work routine. • Young, less schooling or low-income people maintained educational and purchase trips. • Higher schooling people have more chances to keep essential tasks without traveling. • Public transport users had fewer opportunities to switch from routine to purchasing. • Post-pandemic intention is statistically dependent on activities' characteristics. [ABSTRACT FROM AUTHOR]

With the closure of schools during the COVID-19 pandemic, the education available to students depended heavily on home and family conditions and structure. This paper analyses the relationship between access to technology, demographic characteristics, and learning opportunities during the pandemic in Brazil in 2020. It uses logistic and ordinary least squares linear regression models to generate information based on two national household surveys in Brazil. Both demographics and technology access are related to learning during the pandemic, but demographics have a more constant relationship with learning opportunities than technology access throughout the studied period. • Inequalities in access to education during the pandemic in Latin America. • Use of PNAD-COVID19 to study educational access during the pandemic in Brazil. • ICT access is related to differences in educational access during the pandemic. • Gender and race inequalities not addressed by educational policies in the pandemic. [ABSTRACT FROM AUTHOR]

• We analyze the correlation of meteorological and human mobility in Brazilian capitals. • We propose the EEMD-ARIMAX method to predict time series with independent variables. • We introduce an anomaly detection algorithm to adjust the prediction. In 2020, Brazil was the leading country in COVID-19 cases in Latin America, and capital cities were the most severely affected by the outbreak. Climates vary in Brazil due to the territorial extension of the country, its relief, geography, and other factors. Since the most common COVID-19 symptoms are related to the respiratory system, many researchers have studied the correlation between the number of COVID-19 cases with meteorological variables like temperature, humidity, rainfall, etc. Also, due to its high transmission rate, some researchers have analyzed the impact of human mobility on the dynamics of COVID-19 transmission. There is a dearth of literature that considers these two variables when predicting the spread of COVID-19 cases. In this paper, we analyzed the correlation between the number of COVID-19 cases and human mobility, and meteorological data in Brazilian capitals. We found that the correlation between such variables depends on the regions where the cities are located. We employed the variables with a significant correlation with COVID-19 cases to predict the number of COVID-19 infections in all Brazilian capitals and proposed a prediction method combining the Ensemble Empirical Mode Decomposition (EEMD) method with the Autoregressive Integrated Moving Average Exogenous inputs (ARIMAX) method, which we called EEMD-ARIMAX. After analyzing the results poor predictions were further investigated using a signal processing-based anomaly detection method. Computational tests showed that EEMD-ARIMAX achieved a forecast 26.73% better than ARIMAX. Moreover, an improvement of 30.69% in the average root mean squared error (RMSE) was noticed when applying the EEMD-ARIMAX method to the data normalized after the anomaly detection. [ABSTRACT FROM AUTHOR]

• Analysis of the impact of the Covid-19 pandemic on the Brazilian electricity market. • Application of the Optimization Tariff Model in the context of the pandemic. • Analysis of the efficacy of the public policy designed by the Brazilian government. • Presentation of a new public policy that present advantages over the current one. This paper evaluates the impact of the COVID-19 pandemic on the Brazilian distribution electricity market by applying a socioeconomic regulatory model called Optimized Tariff Model. The model quantifies the impact of the pandemic on the consumer's quality of life and also on the economic performance of power distribution companies. Results indicate that both consumers and power distribution companies have been significantly affected in Brazil, especially companies that did not have access to the public policy proposed by the government, as they exhibited economic losses of more than 1 (GR$) in total. After analyzing the impact of the new coronavirus and the actions of the government in this context, an alternative mitigation measure (public policy) is proposed based on the Optimized Tariff Model and its feasibility examined. The alternative mitigation measure is also compared to the public policy proposed by the government and proved to be advantageous in some respects such as making bank loans unnecessary. [ABSTRACT FROM AUTHOR]