Search

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]

This paper presents a computational model based on interval type-2 fuzzy systems for analysis and forecasting of COVID-19 dynamic spreading behavior. The proposed methodology is related to interval type-2 fuzzy Kalman filters design from experimental data of daily deaths reports. Initially, a recursive spectral decomposition is performed on the experimental dataset to extract relevant unobservable components for parametric estimation of the interval type-2 fuzzy Kalman filter. The antecedent propositions of fuzzy rules are obtained by formulating a type-2 fuzzy clustering algorithm. The state space submodels and the interval Kalman gains in consequent propositions of fuzzy rules are recursively updated by a proposed interval type-2 fuzzy Observer/Kalman Filter Identification (OKID) algorithm, taking into account the unobservable components obtained by recursive spectral decomposition of epidemiological experimental data of COVID-19. For validation purposes, through a comparative analysis with relevant references of literature, the proposed methodology is evaluated from the adaptive tracking and forecasting of COVID-19 dynamic spreading behavior, in Brazil, with the better results for RMSE of 1. 24 × 1 0 − 5 , MAE of 2. 62 × 1 0 − 6 , R 2 of 0.99976, and MAPE of 6. 33 × 1 0 − 6 . [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]

The SARS-CoV-2 virus was first registered in Brazil by the end of February 2020. Since then, the country counts over 150000 deaths due to COVID-19 and faces a profound social and economic crisis; there is also an ongoing health catastrophe, with the majority of hospital beds in many Brazilian cities currently occupied with COVID-19 patients. Thus, a Nonlinear Model Predictive Control (NMPC) scheme used to plan appropriate social distancing measures (and relaxations) in order to mitigate the effects of this pandemic is formulated in this paper. The strategy is designed upon an adapted data-driven Susceptible–Infected–Recovered–Deceased (SIRD) model, which includes time-varying auto-regressive immunological parameters. A novel identification procedure is proposed, composed of analytical regressions, Least-Squares optimization and auto-regressive model fits. The adapted SIRD model is validated with real data and able to adequately represent the contagion curves over large forecast horizons. The NMPC strategy is designed to generate piecewise constant quarantine guidelines, which can be reassessed (relaxed/strengthened) each week. Simulation results show that the proposed NMPC technique is able to mitigate the number of infections and progressively loosen social distancing measures. With respect to a "no-control" condition, the number of deaths could be reduced in up to 30% if the proposed NMPC coordinated health policy measures are enacted. [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]

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]

Twitter is a social media platform with more than 500 million users worldwide. It has become a tool for spreading the news, discussing ideas and comments on world events. Twitter is also an important source of health-related information, given the amount of news, opinions and information that is shared by both citizens and official sources. It is a challenge identifying interesting and useful content from large text-streams in different languages, few works have explored languages other than English. In this paper, we use topic identification and sentiment analysis to explore a large number of tweets in both countries with a high number of spreading and deaths by COVID-19, Brazil, and the USA. We employ 3,332,565 tweets in English and 3,155,277 tweets in Portuguese to compare and discuss the effectiveness of topic identification and sentiment analysis in both languages. We ranked ten topics and analyzed the content discussed on Twitter for four months providing an assessment of the discourse evolution over time. The topics we identified were representative of the news outlets during April and August in both countries. We contribute to the study of the Portuguese language, to the analysis of sentiment trends over a long period and their relation to announced news, and the comparison of the human behavior in two different geographical locations affected by this pandemic. It is important to understand public reactions, information dissemination and consensus building in all major forms, including social media in different countries. • We compared topic modeling for English and Portuguese tweets related to COVID-19. • The topics we identified were representative from the news from April to August. • We ranked ten topics for both languages and notice most of them are similar. • We performed sentiment analysis and negative emotions were dominant in the pandemic. • For sentiment analysis we compare several classifiers and features (n-grams and embeddings). [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]

• 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]

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]