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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]

The first reported case of coronavirus disease (COVID-19) in Brazil was confirmed on 25 February 2020 and then the number of symptomatic cases produced day by day. In this manuscript, we studied the epidemic peaks of the novel coronavirus (COVID-19) in Brazil by the successful application of Predictor–Corrector (P-C) scheme. For the proposed model of COVID-19, the numerical solutions are performed by a model framework of the recent generalized Caputo type non-classical derivative. Existence of unique solution of the given non-linear problem is presented in terms of theorems. A new analysis of epidemic peaks in Brazil with the help of parameter values cited from a real data is effectuated. Graphical simulations show the obtained results to classify the importance of the classes of projected model. We observed that the proposed fractional technique is smoothly work in the coding and very easy to implement for the model of non-linear equations. By this study we tried to exemplify the roll of newly proposed fractional derivatives in mathematical epidemiology. The main purpose of this paper is to predict the epidemic peak of COVID-19 in Brazil at different transmission rates. We have also attempted to give the stability analysis of the proposed numerical technique by the reminder of some important lemmas. At last we concluded that when the infection rate increases then the nature of the diseases changes by becoming more deathly to the population. [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]

Fake news has now grown into a big problem for societies and also a major challenge for people fighting disinformation. This phenomenon plagues democratic elections, reputations of individual persons or organizations, and has negatively impacted citizens, (e.g., during the COVID-19 pandemic in the US or Brazil). Hence, developing effective tools to fight this phenomenon by employing advanced Machine Learning (ML) methods poses a significant challenge. The following paper displays the present body of knowledge on the application of such intelligent tools in the fight against disinformation. It starts by showing the historical perspective and the current role of fake news in the information war. Proposed solutions based solely on the work of experts are analysed and the most important directions of the application of intelligent systems in the detection of misinformation sources are pointed out. Additionally, the paper presents some useful resources (mainly datasets useful when assessing ML solutions for fake news detection) and provides a short overview of the most important R&D projects related to this subject. The main purpose of this work is to analyse the current state of knowledge in detecting fake news; on the one hand to show possible solutions, and on the other hand to identify the main challenges and methodological gaps to motivate future research. [ABSTRACT FROM AUTHOR]

• The Brazilian population is modelled by cellular automata. • The model is tuned for the COVID-19 evolution. • Social isolation has three variables: start, duration and percentage of reduced contact. • Simulations are carried out to analyze different demands on the healthcare system. • COVID-19 cases evolution and hospitalization cannot be used alone to end social campaigns. Background and objective Many countries around the world experienced a high increase in the number of COVID-19 cases after a few weeks of the first case, and along with it, excessive pressure on the healthcare systems. While medicines, drugs, and vaccines against the COVID-19 are being developed, social isolation has become the most used method for controlling the virus spreading. With the social isolation, authorities aimed to slow down the spreading, avoiding saturation of the healthcare system, and allowing that all critical COVID-19 cases could be appropriately treated. By tuning the proposed model to fit Brazil's initial COVID-19 data, the objectives of the paper are to analyze the impact of the social isolation features on the population dynamics; simulate the number of deaths due to COVID-19 and due to the lack of healthcare infrastructure; study combinations of the features for the healthcare system does not collapse; and analyze healthcare system responses for the crisis. Methods In this paper, a Susceptible-Exposed-Infected-Removed model is described in terms of probabilistic cellular automata and ordinary differential equations for the transmission of COVID-19, flexible enough for simulating different scenarios of social isolation according to the following features: the start day for the social isolation after the first death, the period for the social isolation campaign, and the percentage of the population committed to the campaign. Results Results showed that efforts in the social isolation campaign must be concentrated both on the isolation percentage and campaign duration to delay the healthcare system failure. For the hospital situation in Brazil at the beginning of the pandemic outbreak, a rate of 200 purchases per day of intensive care units and mechanical ventilators is the minimum rate to prevent the collapse of the healthcare system. Conclusions By using the model for different scenarios, it is possible to estimate the impact of social isolation campaign adhesion. For instance, if the social isolation percentage increased from 40% to 50% in Brazil, the purchase rate of 150 intensive care units and mechanical ventilators per day would be enough to prevent the healthcare system to collapse. Moreover, results showed that a premature relaxation of the social isolation campaign can lead to subsequent waves of contamination. [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]

The rapid spread of COVID-19 across the world has raised concerns about the responsiveness of cities and healthcare systems during pandemics. Recent studies try to model how the number of COVID-19 infections will likely grow and impact the demand for hospitalization services at national and regional levels. However, less attention has been paid to the geographic access to COVID-19 healthcare services and to hospitals' response capacity at the local level, particularly in urban areas in the Global South. This paper shows how transport accessibility analysis can provide actionable information to help improve healthcare coverage and responsiveness. It analyzes accessibility to COVID-19 healthcare at high spatial resolution in the 20 largest cities of Brazil. Using network-distance metrics, we estimate the vulnerable population living in areas with poor access to healthcare facilities that could either screen or hospitalize COVID-19 patients. We then use a new balanced floating catchment area (BFCA) indicator to estimate spatial, income, and racial inequalities in access to hospitals with intensive care unit (ICU) beds and mechanical ventilators while taking into account congestion effects. Based on this analysis, we identify substantial social and spatial inequalities in access to health services during the pandemic. The availability of ICU equipment varies considerably between cities, and it is substantially lower among black and poor communities. The study maps territorial inequalities in healthcare access and reflects on different policy lessons that can be learned for other countries based on the Brazilian case. • Use of a Balanced Floating Catchment Area to calculate accessibility to healthcare. • Estimates access to healthcare in the 20 largest Brazilian cities during COVID-19. • Over 1.6 million people have poor access to healthcare for severe cases of COVID-19. • 41% of vulnerable population have poor access to ICU beds and mechanical ventilators. • Low income and black communities in urban periphery have less healthcare access. [ABSTRACT FROM AUTHOR]

• We propose two indexes to account for the vulnerability related to COVID-19. • The northern and northeastern regions of Brazil have the highest degree of vulnerability from exposure to COVID-19. • The Indigenous population is the most vulnerable to infection with COVID-19 and is the most impoverished monetarily. • The Brown and Black populations are also at a disadvantage compared to the White and Asian groups. • Most states with the highest vulnerability from exposure to COVID-19 have also had the largest increase in death rates. This paper aims to show how much and in which way people in Brazil are deprived in terms of indicators directly related to the capacity to prevent and recover from infection with COVID-19. We use the Alkire-Foster (AF) method and a fuzzy-set approach as complements to measure multidimensional poverty within the context of the coronavirus pandemic. We propose two pandemic-specific indexes to account for the vulnerability related to the capacity to prevent infection with and to recover from the disease. The outcomes reveal structural deprivations in the country and considerable inequality among regions and ethnic groups. Rank correlation analyses suggest that the proposed indexes can trace the trends in increasing infection and a higher mortality rate in vulnerable regions. Compared to headcount ratio results, the fuzzy measures have more precise outcomes and are better able to capture the evolution in mortality patterns. Our empirical evidence offers an additional warning that the pandemic responses need to prioritize the most vulnerable groups and reinforces the need for coordinated national action. [ABSTRACT FROM AUTHOR]

This paper examines the effects of the COVID-19 pandemic on environmental protection and legislation in Brazil. We evaluate major legislative actions, environmental fines and deforestation since January 2019. We show that 57 legislative acts aimed at weakening environmental protection in Brazil during the current administration, almost half of which in the seven-month period of the pandemic in Brazil, with September 2020 as the month with the most legislative acts (n = 16). These acts either deregulated or weakened current environmental legislation, with a number of them aimed at dismantling the main federal institutions in charge of environmental protection. We also found a 72% reduction in environmental fines during the pandemic, despite an increase in Amazonian deforestation during this period. We conclude that the current administration is taking advantage of the COVID-19 pandemic to intensify a pattern of weakening environmental protection in Brazil. This has the potential to intensify ongoing loss of biodiversity, greenhouse gas emissions, and the likelihood of other zoonotic disease outbreaks, and inflict substantial harm to traditional and indigenous peoples. We highlight the key role of the scientific community, media and civil society, national and international levels, in order to reverse these harmful actions. • 57 legislative acts have aimed to weaken the environmental legislation during the current administration. • 49% of changes in environmental legislation occurred during the 7-months period of the pandemic. • A reduction of about 70% of environmental fines occurred from March to August 2020. • Protected areas were weakened through exoneration of chief staff during the pandemic. • The administration is pushing rules and simplifying norms, especially since the pandemic began. [ABSTRACT FROM AUTHOR]

• Anonymized mobile phone users' location data to identify mobility among cities. • Clusters were identified based on mobility patterns amongst cities of the state. • A city cluster risk-based approach for Sars-CoV-2 and isolation barriers. • Non-infected and infected clusters are classified according to risk and criticality. • Isolation policies can be supported by analytics for economic recovery planning. Given the recent outbreak of Sars-CoV-2, several countries started to seek different strategies to control contamination and minimize fatalities, which are usually the primary objectives for all strategies. Secondary objectives are related to economic factors, therefore ensuring that society would be able is to keep its essential activities and avoid supply disruptions. This paper presents an application of anonymized mobile phone users' location data to estimate population flow amongst cities with an origin-destination matrix. The work includes a clustering analysis of cities, which may enable policymakers (and epidemiologists) to develop public policies giving the appropriate consideration for each set of cities within a Province or State. Risk measures are included to analyze the severity of the spread among the clusters, which can be ranked. Then, intelligence can be obtained from the analysis, and some clusters could be isolated to avoid contagion while keeping their economic activities. Therefore, this analysis is reproducible for other states of Brazil and other countries and can be adapted for districts within a city, especially considering the possibility of a second wave COVID-19 pandemic. [ABSTRACT FROM AUTHOR]