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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 Bolsonaro administration and its class allies used the pandemic as a political opportunity and the state as a tool to advance dispossessions. • Dispossessions such as debt interest payments or privatizations simply redistribute surpluses or means of production, with no impact on capital expansion. • Dispossessions of peasants or indigenous people from lands and forests expand capital by turning their means of subsistence into means of capitalist production. • The transfer to for-profit companies of public services, facilities, or spaces turns means of subsistence into commodities and thus expands capital. • Capitalization, commodification, or the termination of contemporary commons reduce means of subsistence and thus increase the availability of exploitable labor-power. In 2020–2021 Brazil simultaneously experienced the far-right presidency of Jair Bolsonaro and the world's largest health crisis in a century. The Covid-19 pandemic struck the country deeply, killing about 690 thousand people by late 2022. They were also years of increased pressure by capital on peasants and indigenous people, targets of the violence with which capital, ever since its dawn, has wielded to advance over spaces that serve the subsistence of immediate producers. In this period, the Brazilian state continued to comply with decades-old demands from neoliberal ideology for privatizations and the dismantling of protections for workers and the poor in general. These phenomena, when articulated by theory inspired by the Marxian concept of primitive accumulation, suggest that the Bolsonaro administration and its class allies used the pandemic as a political opportunity for dispossessing policies. The article discusses this based on concepts that distinguish dispossessions that serve capital expansion from those that do not. The first group includes processes that lead to proletarianization of immediate producers in addition to the capitalization or commodification of hitherto means of subsistence. Among dispossessions that do not expand capital are those that involve the simple redistribution of surpluses or means of production. The paper contributes to the literature on dispossession by analyzing concrete manifestations of it, drawing on a conceptual framework that distinguishes dispossession types that have been conflated in much current research. [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]

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]

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]

This paper formulates a Model Predictive Control (MPC) policy to mitigate the COVID-19 contagion in Brazil, designed as optimal On-Off social isolation strategy. The proposed optimization algorithm is able to determine the time and duration of social distancing policies in the country. The achieved results are based on data from the period between March and May of 2020, regarding the cumulative number of infections and deaths due to the SARS-CoV-2 virus. This dataset is assumably largely sub-notified due to the absence of mass testing in Brazil. Thus, the MPC is based on a SIR model which is identified using an uncertainty-weighted Least-Squares criterion. Furthermore, this model includes an additional dynamic variable that mimics the response of the population to the social distancing policies determined by the government, which affect the COVID-19 transmission rate. The proposed control method is set within a mixed-logical formalism, since the decision variable is forcefully binary (existence or the absence of social distance policy). A dwell-time constraint is included to avoid too frequent shifts between these two inputs. The achieved simulation results illustrate how such optimal control method would operate in practice, pointing out that no social distancing should be relaxed before mid August 2020. If relaxations are necessary, they should not be performed before this date and should be in small periods, no longer than 25 days. This paradigm would proceed roughly until January/2021. The results also indicate a possible second peak of infections, which has a forecast to the beginning of October. This peak can be reduced if the periods of days with relaxed social isolation measures are shortened. [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]

This paper compares and contrasts the spread and impact of COVID-19 in the three countries most heavily impacted by the pandemic: the United States (US), India and Brazil. All three of these countries have a federal structure, in which the individual states have largely determined the response to the pandemic. Thus, we perform an extensive analysis of the individual states of these three countries to determine patterns of similarity within each. First, we analyse structural similarity and anomalies in the trajectories of cases and deaths as multivariate time series. Next, we study the lengths of the different waves of the virus outbreaks across the three countries and their states. Finally, we investigate suitable time offsets between cases and deaths as a function of the distinct outbreak waves. In all these analyses, we consistently reveal more characteristically distinct behaviour between US and Indian states, while Brazilian states exhibit less structure in their wave behaviour and changing progression between cases and deaths. • We compare and contrast the natural history of COVID-19 in the US, India and Brazil. • Case, death and mortality rate trajectories are analysed on a state-by-state basis. • We quantify heterogeneity and identify anomalous states therein within each country. • Wave behaviours are explored and clustered across the three countries states. • New mathematical methods quantify the lag in the progression from cases and deaths. [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]

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]

The presence of a large number of infected individuals with few or no symptoms is an important epidemiological difficulty and the main mathematical feature of COVID-19. The A-SIR model, i.e. a SIR (Susceptible–Infected–Removed) model with a compartment for infected individuals with no symptoms or few symptoms was proposed by Gaeta (2020). In this paper we investigate a slightly generalized version of the same model and propose a scheme for fitting the parameters of the model to real data using the time series only of the deceased individuals. The scheme is applied to the concrete cases of Lombardy, Italy and São Paulo state, Brazil, showing different aspects of the epidemic. In both cases we see strong evidence that the adoption of social distancing measures contributed to a slower increase in the number of deceased individuals when compared to the baseline of no reduction in the infection rate. Both for Lombardy and São Paulo we show that we may have good fits to the data up to the present, but with very large differences in the future behavior. The reasons behind such disparate outcomes are the uncertainty on the value of a key parameter, the probability that an infected individual is fully symptomatic, and on the intensity of the social distancing measures adopted. This conclusion enforces the necessity of trying to determine the real number of infected individuals in a population, symptomatic or asymptomatic. • COVID-19 epidemic previsions must take into account a large class of asymptomatic cases. • Disparate outcomes in previsions are consequence of the uncertainty in the number of asymptomatic individuals. • Necessity of determining the real number of infected individuals, symptomatic or asymptomatic. • Parameters of the model fitted to real data using only time series of deceased individuals. • Social distancing measures contributed to slower increase in the number of deceased individuals. [ABSTRACT FROM AUTHOR]