COVID-19 in Northeast Brazil: achievements and limitations in the responses of the state governments

Ligia Kerr Carl Kendall Antônio Augusto Moura da Silva Estela Maria L Aquino Julia M Pescarini Rosa Lívia Freitas de Almeida Maria Yury Ichihara Juliane F Oliveira Thália Velho Barreto de Araújo Carlos Teles Santos Daniel Cardoso Pereira Jorge Demócrito de Barros Miranda Filho Guilherme Santana Ligia Gabrielli Maria de Fatima Pessoa Militão de Albuquerque Naomar Almeida-Filho Natanael de Jesus Silva Rafael Souza Ricardo Arraes de Alencar Ximenes Celina Maria Turchi Martelli Sinval Pinto Brandão Filho Wayner Vieira de Souza Maurício Lima Barreto About the authors

Resumo

No Brasil, a pandemia da COVID-19 tem sido severa nos estados das regiões mais pobres, como o Nordeste. A falta de políticas nacionais para controle da pandemia levou as autoridades estaduais e municipais a implementarem medidas de saúde pública. O objetivo deste estudo é mostrar o efeito dessas medidas na epidemia. A maior incidência da COVID-19 entre os nove estados do Nordeste foi registrada em Sergipe, Paraíba e Ceará. O Piauí, a Paraíba e Ceará foram os que mais testaram. Muitos estados apresentavam alta proporção de pessoas em trabalho informal. Estados com aeroportos internacionais tiveram importante papel na entrada e disseminação inicial do vírus, em especial o Ceará. Todos os estados aplicaram medidas de distanciamento social, proibição de eventos públicos e fechamento de unidades de ensino. As respostas foram o aumento significativo de distanciamento social, em especial Ceará e Pernambuco, a queda do número de reprodução (Rt) e a separação da curva dos casos observados da curva dos casos esperados sem as intervenções não medicamentosas em todos os estados. A pobreza, a desigualdade e as altas taxas de trabalho informal fornecem pistas do porquê da intensidade da COVID-19 na região. Por outro lado, as medidas de mitigação tomadas precocemente pelos governantes amenizaram os efeitos da pandemia.

Palavras-chave
COVID-19; Nordeste Brasileiro; Prevenção e Controle

Abstract

The COVID-19 pandemic has been most severe in the poorest regions of Brazil, such as the states of the Northeast Region. The lack of national policies for pandemic control forced state and municipal authorities to implement public health measures. The aim of this study is to show the effect of these measures on the epidemic. The highest incidence of COVID-19 among the nine states in the Northeast was recorded in Sergipe, Paraíba and Ceará. Piauí, Paraíba and Ceará were the states that most tested. Factors associated with transmission included the high proportion of people in informal work. States with international airports played an important role in the entry of the virus and the initial spread, especially Ceará. All states applied social distancing measures, banned public events and closed schools. The response was a significant increase in social distancing, especially in Ceará and Pernambuco, a decline in the reproduction rate (Rt), and a separation of the curve of observed cases versus expected cases if the non-pharmacological interventions had not been implemented in all states. Poverty, inequality, and the high rates of informal work provide clues to the intensity of COVID-19 in the region. On the other hand, the measures taken early by the governments mitigated the effects of the pandemic.

Key words
COVID-19; Northeast Brazil; Prevention and Control

Introduction

Brazil reached the mark of 2,488,807 cases of COVID-19 on July 29, 202011 Centro de Integração de Dados e Conhecimentos para Saúde (CIDACS). Fiocruz. UFBA. Painel Rede CoVida 2020 [Internet]. [acessado 2020 Jun 20]. Disponível em: https://painel.covid19br.org/
https://painel.covid19br.org/...
. Brazil is among the most unequal countries in the world, and the COVID-19 pandemic has deepened these inequalities, not only producing a greater impact on the number of cases and deaths in the poorest regions, such as the North and Northeast, but also increasing poverty22 Ahmed F, Ahmed NE, Pissarides C, Stiglitz J. Why inequality could spread COVID-19. Lancet Public Health 2020; 5(5):e240.

3 Dolce J, Fonseca B. Desigualdade social é fator de risco para mortes de crianças e adolescentes por Covid-19 no país 2020 [Internet]. Pública Agência de Jornalismo Investigativo; 2020 [acessado 2020 Jun 20]. Disponível em: https://apublica.org/2020/06/desigualdade-social -e-fator-de-risco-para-mortes-de-criancas-e-adolescentes-por-covid-19-no-pais/#.XuiTr59onPV.face book
https://apublica.org/2020/06/desigualdad...

4 Sumner A, Hoy C, Ortiz-Juarez E. Estimates of the Impact of COVID-19 on Global Poverty. UNUWIDER 2020; 800-809.
-55 Mahler DG, Lakner C, Aguilar RAC, Wu H. Updated Estimates of the Impact of COVID-19 on Global Poverty [Internet]. World Bank; 2020 [acessado 2020 Jun 20].Disponível em: https://blogs.worldbank.org/opendata/updated-estimates-impact-covid-19-global-poverty
https://blogs.worldbank.org/opendata/upd...
, racial and ethnic disparities66 Goes EF, Ramos DO, Ferreira AJF. Desigualdades raciais em saúde e a pandemia da Covid-19. Trab Educ Saude 2020; 18(3):e00278110. and strongly affecting women77 Alon TM, Doepke M, Olmstead-Rumsey J, Tertilt M. The impact of COVID-19 on gender equality. NBER Working Papers 2020; 26947.,88 Wenham C, Smith J, Morgan R. COVID-19: the gendered impacts of the outbreak. Lancet 2020; 395(10227):846-848..

The epidemic, while not sparing the wealthiest regions, has had a deeper impact on less favored states, such as those in the North and Northeast. The Northeast, one of the poorest regions of the country, constitutes 27% of the Brazilian population but has about a third ( 34% ) of all cases and deaths (32%)11 Centro de Integração de Dados e Conhecimentos para Saúde (CIDACS). Fiocruz. UFBA. Painel Rede CoVida 2020 [Internet]. [acessado 2020 Jun 20]. Disponível em: https://painel.covid19br.org/
https://painel.covid19br.org/...
. A nationwide serological survey showed a remarkable variability in the prevalence of SARS coronavirus-2 antibodies in different Brazilian regions99 Hallal P, Hartwig F, Horta B, Victora GD, Silveira M, Struchiner C, Vidaletti LP, Neumann N, Pellanda LC, Dellagostin OA, Burattini MN, Menezes AM, Barros FC, Barros AJ, Victoria CG. Remarkable variability in SARS-CoV-2 antibodies across Brazilian regions: nationwide serological household survey in 27 states. medRxiv 2020; [preprint]., with extremely high rates in cities in the poorer regions. The new coronavirus pandemic has been a challenge for Brazil due to these deep internal inequalities, known to be associated with the pandemic both nationally and internationally.

In the absence of treatment and vaccines, the only effective strategy against COVID-19 is to reduce contact between susceptible and infected individuals through early identification of cases and reducing contact1010 Lewnard JA, Lo NC. Scientific and ethical basis for social-distancing interventions against COVID-19. Lancet Infect Dis 2020; 20(6):631., cleaning surfaces and potentially contaminated hands1111 World Health Organization (WHO). Water, sanitation, hygiene and waste management for COVID-19: technical brief, 03 March 2020. Geneva: WHO; 2020. and the use of masks1212 Eikenberry SE, Mancuso M, Iboi E, Phan T, Eikenberry K, Kuang Y, Kostelich E, Gumel AB. To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic. Infect Dis Model 2020; 5:293-308.,1313 Stutt RO, Retkute R, Bradley M, Gilligan CA, Colvin J. A modelling framework to assess the likely effectiveness of facemasks in combination with 'lock-down'in managing the COVID-19 pandemic. Proceedings Royal Society A 2020; 476(2238):20200376.. The universally recommended strategy of testing cases and contacts and isolating the infected was practiced only timidly in Brazil. In the absence of national policies to control the pandemic, state and municipal authorities were responsible for implementing public health measures to reduce transmission for COVID-19, including declaring a state of emergency, closing retail and service companies, restricting transportation, and closing schools and universities1414 Aquino EM, Silveira IH, Pescarini JM, Aquino R, Souza-Filho JA. Medidas de distanciamento social no controle da pandemia de COVID-19: potenciais impactos e desafios no Brasil. Cien Saude Colet 2020; 25:2423-2446.. The interventions varied substantially among states and municipalities, including both the measures that were adopted, and when they were instituted.

The objectives of this study are to explore the experience of the COVID-19 epidemic in the Northeast Region and to discuss the differences and similarities across the states of this region with respect to COVID-19, the social distance measures implemented, and how these differences can explain the epidemiological indicators of the epidemic in this region.

Methodology

Type and place of study

This is a mixed, descriptive and analytical study conducted in the Northeast Region of Brazil. This region is composed of nine states (Alagoas, Bahia, Ceará, Maranhão, Paraíba, Pernambuco, Piauí, Rio Grande do Norte and Sergipe) and 1,793 municipalities. It covers an area of approximately 1.5 million km2 and has 53 million inhabitants.

Data collection

The data used were extracted for each state from the following sources: 1) demographic and socioeconomic data from the Brazilian Institute of Geography and Statistics (IBGE)1515 Brasil. Instituto Brasileiro de Geografia e Estatística (IBGE). Painel de Indicadores [Internet]. 2020 [acessado 2020 Jun 20]. Disponível em: https://www.ibge.gov.br/indicadores.
https://www.ibge.gov.br/indicadores...
; 2) federal and state decrees concerning specific control measures for COVID-19, with the effective date being considered the publication of the decree; 3) number of cases and deaths due to COVID-19 and for Serious Acute Respiratory Syndrome (SRAG) from the Ministry of Health and State Secretariats1616 Cotta W. COVID-19 in Brazil 2020 [Internet]. [acessado 2020 Jun 20]. Disponível em: https://wesleycota.com/
https://wesleycota.com/...
,1717 Centro de Integração de Dados e Conhecimentos para Saúde (CIDACS). Fiocruz. UFBA. CoVida: Ciência, Informação e Solidariedade [Internet]. 2020 [acessado 2020 Jun 20]. Disponível em: https://covid19br.org/
https://covid19br.org/...
and the Infogripe System1818 Fundação Oswaldo Cruz (Fiocruz). Painel Infogripe: Monitoramento de casos reportados de síndrome respiratória aguda grave (SRAG) hospitalizados. Rio de Janeiro: Fiocruz; 2020.; 4) information on arrival and departure of national and international flights from the National Civil Aviation Agency (ANAC)1919 Brasil. Agência Nacional de Aviação Civil (ANAC). Setor Regulado. Microdados [Internet]. 2020 [acessado 2020 Jun 7]. Disponível em: https://www.anac.gov.br/assuntos/setor-regulado/empresas/envio-de-informacoes/microdados
https://www.anac.gov.br/assuntos/setor-r...
; and 5) mobility data from the Google Mobility Report2020 Google LLC. Google COVID-19 Community Mobility Reports 2020 [Internet]. [acessado 2020 Jun 7]. Disponível em: https://www.google.com/covid19/mobility/
https://www.google.com/covid19/mobility/...
and the social isolation index developed by the Brazilian company Inloco2121 Inloco. Mapa brasileiro da COVID-19 2020 [Internet]. [acessado 2020 Jun 7]. Disponível em: https://www.inloco.com.br/
https://www.inloco.com.br/...
, using cellular phone data to help confront the pandemic.

Initiatives to develop measures of social mobility have been implemented by Google and the company In Loco, based on data captured by cell phones, with the objective of helping to face the COVID-19 pandemic. The social isolation index developed by Inloco shows the daily percentage of the population that are conforming to the social distancing guidelines in Brazilian states and municipalities since 02/01/20202121 Inloco. Mapa brasileiro da COVID-19 2020 [Internet]. [acessado 2020 Jun 7]. Disponível em: https://www.inloco.com.br/
https://www.inloco.com.br/...
. The Google Community Mobility Report contains data on daily visits to the following locations: a) work, b) parks, beaches, squares and public gardens, c) supermarkets, drugstores and pharmacies, d) restaurants, shopping malls, museums, libraries and cinemas, e) subway stations, trains and buses, and f) residential areas. The movement of people (visits and length of stay) in different locations is calculated daily and compared to a baseline, which corresponds to the median movement during the period January 3 to February 6, 20202020 Google LLC. Google COVID-19 Community Mobility Reports 2020 [Internet]. [acessado 2020 Jun 7]. Disponível em: https://www.google.com/covid19/mobility/
https://www.google.com/covid19/mobility/...
. Mobility changes are presented as a percentage and are calculated daily until May 6, 2020 for the six categories. The Mobility Report covers 132 countries, including Brazil, and data are available from February 26th. For this study, the average movement in non-residential (areas a-e, above) and residential areas until June 13, 2020 was calculated. The Inloco social isolation index shows the daily percentage of the population that is meeting the recommendation for social distancing in Brazilian states and municipalities since 02/01/20202121 Inloco. Mapa brasileiro da COVID-19 2020 [Internet]. [acessado 2020 Jun 7]. Disponível em: https://www.inloco.com.br/
https://www.inloco.com.br/...
.

The data on flights and passengers were collected from the Brazilian aviation authority ANAC and include the three state capitals with international airports (Fortaleza, Recife and Salvador). Data included: 1) number of landings (arriving or departing) of planes arriving from São Paulo, Rio de Janeiro or internationally per month, regardless of the airline; 2) number of passengers on these flights; and 3) ratio between flights and passengers per month between the routes mentioned above.

Analysis

The data were summarized in the form of tables and graphs. Analysis of the data used the qualitative method of controlled comparison2222 Elliot M, Fairweather I, Olsen W, Pampaka M. A dictionary of social research methods. Oxford: Oxford University Press; 2016.,2323 Eggan F. Social Anthropology and the Method of Controlled Comparison. American Anthropologist 1954; 56(5):743-763. to compare the differences and similarities among the Northeastern states.

The effective reproduction number over time (Rt), the transmission parameter Beta and the observed and predicted curves for the number of cases were calculated using a compartmental model which divides the population among susceptible, exposed (individuals in incubation), asymptomatic infected, symptomatic infected and recovered2424 Jorge DC, Rodrigues MS, Silva MS, Cardim LL, Silva NB, Silveira IH, Silva VAF, Pereira FAC, Pinho STR, Andrade RFS, Ramos PIP, Oliveira JF. Assessing the nationwide impact of COVID-19 mitigation policies on the transmission rate of SARS-CoV-2 in Brazil. medRxiv 2020; [preprint].,2525 Oliveira JF, Jorge DC, Veiga RV, Rodrigues MS, Torquato MF, Silva NB, Fiaconne RL, Castro CP, Paiva ASS, Cardim LL, Amad AAS, Lima EABF, Souza DS, Pinho STR, Ramos PIP, Andrade RFS, Rede CoVida Modelling Task-force. Evaluating the burden of COVID-19 on hospital resources in Bahia, Brazil: A modelling-based analysis of 14.8 million individuals. medRxiv 2020; [preprint].. The daily Rt value is defined as the average number of secondary cases infected by a case with onset of symptoms on day “t”, which in the present methodology also takes into account the secondary cases caused by asymptomatic infected individuals. The indicator can be interpreted as the higher the Rt value for a given location, the more uncontrolled the epidemic11 Centro de Integração de Dados e Conhecimentos para Saúde (CIDACS). Fiocruz. UFBA. Painel Rede CoVida 2020 [Internet]. [acessado 2020 Jun 20]. Disponível em: https://painel.covid19br.org/
https://painel.covid19br.org/...
.

Results

Table 1 shows characteristics of the COVID-19 epidemic in the nine states of the region up to 7/21/2020. The state with the smallest difference between the first and the fiftieth and between the first and the hundredth case was Maranhão. The highest incidence of COVID-19 among the nine states was recorded in Sergipe (2049/100,000 pop.), followed by Paraíba (1713/100,000 pop.) and Ceará (1677/100,000 pop.). The region’s first official death attributed to COVID-19 occurred in Pernambuco, on March 25. Until July 21, the ratio between the number of deaths and the number of confirmed COVID-19 cases notified was highest in Pernambuco (7.6%). The mortality rate for COVID-19 was highest in Ceará (80.1/100,000 pop.) followed by Pernambuco (64.4/100,000 pop.). In contrast, in Bahia, the ratio between the number of deaths and the number of confirmed COVID-19 cases was 2.2% and the mortality rate for COVID-19 was 20.1/100,000 inhabitants. The incidence of SRAG in Ceará reached 161.1/100,000, followed by 112.5/100,000 in Pernambuco and 97.8/100,000 in Piauí, indicating that there are a large number of cases of COVID-19 that are not being registered as such in these states, even considering the underreporting and delayed notification of SRAG cases2626 Bastos LS, Niquini RP, Lana RM, Villela DA, Cruz OG, Coelho FC, Codeço CT, Gomes MFC. COVID-19 e hospitalizações por SRAG no Brasil: uma comparação até a 12ª semana epidemiológica de 2020. Cad Saúde Pública 2020; 36:e00070120.. The epidemic had already reached 100% of the municipalities in most states, with the exception of Bahia and Piauí.

Table 1
Epidemiology of SRAG and COVID-19 in Northeastern States as of 7/21/2020.

Regarding testing for the identification of active cases, Piauí was the northeastern state that conducted the most tests (5122/100,000), followed by Paraíba (4954/100,000) and Ceará (4219/100,000). The relationship between the number of tests performed and the number of positive cases was higher in Piauí (4.0), Bahia (2.9) and Paraíba (2.9), and lower in Alagoas (2.0) and in Sergipe (1.7). These low numbers indicate that only people with a high probability of COVID-19 are being tested, i.e., the very symptomatic and the most severe cases (Table 1).

Table 2 presents some demographic indicators for the nine states. The population of each state ranges from 2.3 to 14.9 million inhabitants, from Sergipe to Bahia, respectively. Sergipe and Maranhão have the lowest percentages of elderly aged 60 and over. Demographic density also shows important differences, ranging from 12.4/km2 in Piauí to 112.3/km2 in Alagoas. Maranhão reports almost 10% excessive household occupancy by room, almost 2.5 times more than Bahia and Paraíba.

Table 2
Demographic indicators in the Northeastern States.

Using the method of controlled comparison2222 Elliot M, Fairweather I, Olsen W, Pampaka M. A dictionary of social research methods. Oxford: Oxford University Press; 2016., the states were ranked by the main result variable: the aggregate number of notified cases of COVID-19 per 100,000 inhabitants of each state. Then, exploring other columns in the data, this ranking was compared with the ranking of other indicators that corresponded or approached correspondence with this main variable. Some of the indicators, such as the percentage of households with excessive occupancy rates have a direct association with transmission and their selection is logical. Some of the other indicators such as proportion of people with formal jobs, do not have this direct association. These indicators represent a variety of different potential factors in transmission: not only issues of potential exposure, but also indirect and contextual factors, such as wealth distribution or a state history of supportive policies for workers’ rights, including health. This pattern of classification of indicators is complemented by a review of all the indicators provided for each state. Maranhão, for example, is the state that reports the highest levels of values negatively associated with the health of the population. Ceará has an important percentage of the population below the poverty line and in extreme poverty, and has fewer households with piped water, indicators that are important in determining the distribution of infectious diseases. The highest incidence for COVID-19 was recorded in Ceará, followed by Maranhão and Pernambuco. The highest mortality rates were also observed in these three states.

Table 3 shows some well-known socioeconomic indicators for measuring the degree of poverty and inequality. There is an important similarity between the Human Development Index (HDI) of the Northeastern states, with Rio Grande do Norte, Ceará and Pernambuco standing out as those with slightly higher levels. The comparative analysis of income inequalities, measured through the Gini Index, also shows that they are shared at similar levels by all states, being slightly more accentuated in Sergipe, Alagoas, Bahia and Ceará (these last two states with very similar values), and especially in Maranhão. In fact, the state of Maranhão has the worst indicators, presenting the second worst HDI, the lowest per capita household monthly income, the highest percentages of the population below the poverty line and in extreme poverty and the highest proportion of people in informal work. Ceará has the best HDI, but has the fourth worst household income per capita, 42% of its population lives below the poverty line; almost ⅔ of its population was employed in the reference week, and close to 30% had informal work. Pernambuco ranks third in HDI and per capita monthly household income. However, 41% of its population is below the poverty line, almost ⅔ of its population was employed at the end of the fourth quarter of 2019, and almost 30% of the population aged 14 and over had informal work (Table 3).

Table 3
Socioeconomic indicators of the Northeastern States.

Although all states in the region have many unfavorable socioeconomic indicators and share common social, political and cultural characteristics, they have differences that can contribute to understanding the epidemiology of COVID-19 in the region. This can be seen in Table 3, which brings together some well-known socioeconomic indicators of poverty and inequality. The Human Development Index (HDI), for example, is an indicator designed by the United Nations (UN) to measure the quality of life and economic development of a population, composed of education indicators (literacy and enrollment rate), life expectancy at birth, and income (GDP per capita). There are important similarities between the HDIs of the northeastern states, with Ceará, Rio Grande do Norte, and Pernambuco standing out as those with slightly higher levels. The comparative analysis of income inequalities, measured by the Gini Index, also allows us to see that they are shared at similar levels by all states, with the situation of inequality being slightly higher in Pernambuco, Sergipe, Alagoas, Bahia and Ceará (these last two states with very similar values). The state of Maranhão, in general, has the worst indicators, with the worst HDI, the lowest per capita monthly household income, the highest percentages of the population below the poverty line and in extreme poverty and the lowest proportion of people in informal work (Table 3).

The initial port of entry of cases in the country was in São Paulo, whose main airport (Guarulhos) has the largest number of international flights, with passengers arriving principally from Europe, and frequently embarking for other destinations. Three state capitals in the Northeast region – Fortaleza, Recife and Salvador – have international airports. In January, about half of the 15,107 flights and 1,312,000 passengers that arrived in the Northeast region came directly from São Paulo and Rio de Janeiro, where the pandemic was already spreading, due to infected individuals arriving from abroad (Figure 1).

Figure 1
Number of landings, number of passengers, and passengers per landing in the international airports in the Northeast states arriving from the exterior and other Brazilian cities: São Paulo, Rio de Janeiro, destined for Salvador, Fortaleza and Recife from January to March 2020.

Other forms of COVID-19 entry were recorded in a non-systematic manner. For example, social events, such as a wedding that was widely reported in the media that took place at a seaside resort in the south of the state of Bahia2727 Trindade E. Os circuitos dos ricos e famosos que disseminaram coronavírus no Brasil. Folha de São Paulo; 2020.. There, people from different cities, including some with a history of recent trips to Europe, several of whom were infected with SARS-CoV-2, were responsible for infecting other participants and workers at the event. When they returned home they brought the virus with them.

The first cases of COVID-19 identified in the region were, in general, people who had arrived from trips abroad or from the Southeast of the country. Control measures started with the isolation of the first imported cases and the quarantine of contacts and were followed by the prohibition of public events and the closure of schools, adopted by all the state governments. The social distance rules adopted in the states of the Northeast were, for the most part, implemented on very similar dates, in mid-March1414 Aquino EM, Silveira IH, Pescarini JM, Aquino R, Souza-Filho JA. Medidas de distanciamento social no controle da pandemia de COVID-19: potenciais impactos e desafios no Brasil. Cien Saude Colet 2020; 25:2423-2446.. In Paraíba, decrees implementing social distance were promulgated the day after notification of the first case of the disease (Chart 1). In Bahia, eleven days passed until the enactment of such actions at the state level. Three states – Maranhão, Rio Grande do Norte and Piauí – implemented measures of social distance even before the confirmation of the first case. Maranhão was also the first state in the Northeast to decree total lockdown, for 13 days, from May 5, 2020, followed by the states of Ceará and Pernambuco.

Chart 1
Measures of social distancing implemented in the Northeast states, date of the first case and days until social distancing until 13 May 2020.

Most states adopted measures encompassing different spheres of social life, including downsizing or banning events, closing schools and universities, restrictions on the functioning of retail businesses, services and industry, closing and banning visits to parks, swimming pools and beaches, reduced transportation (municipal, intercity and interstate) and changes in the work schedules of public employees. The lack of specific legislation for social distancing at the state level for each measure considered is highlighted in gray in the Chart 1. However, it is important to note that in the state capitals and many municipalities, measures were also adopted in response to the epidemiology and political context of each location.

Figure 2 shows the evolution of the average mobility outside the home (column 1), the daily value of the number of effective reproduction over time – Rt (column 2) and the evolution curves of the accumulated number of cases observed and estimated using a SEIR model (column 3) for the nine states in the Northeast. All states show significant reductions in mobility outside the home as of the second half of March.

Figure 2
Mobility Index, Reproduction Number (Rt), and effect of social distancing measures on transmission dynamics of COVID-19, from March to June 2020.

Most states maintained the social isolation index (Inloco) above 40%, but registered reductions by the 2nd week of May. Ceará, followed by Pernambuco, were the states that featured the highest levels of social distancing during the period of this study. Maranhão, which implemented the lockdown at the beginning of May, kept the social distancing rates at a level close to that of the beginning of the implementation, but showed a marked reduction after suspending the lockdown (Figure 2, column 1).

In all the states during the study there is great variability in the effective reproduction number (Rt) over time. However, the number remained above 1 most of the time, except for some rare moments when it was below 1 (Figure 2, column 2). However, since the beginning of June, the states of Alagoas, Maranhão and Pernambuco had a lower Rt. It is noteworthy that as of the date of submission of this article, in August 2020, Ceará still has an average Rt value above 1.

In Figure 2, column 3, the curve of the cumulative number of cases recorded and the curve of the numbers obtained from the model also allows us to observe the effect of social distance measures on the curves. Thus, the state of Bahia started to distance themselves from the curve expected without interventions in the beginning of April, when the beta decreased from 1.40 to 0.87. Ceará also saw important impact from social distancing measures, visible at the end of March 2020, with an increasing separation between the curves of expected and observed cases. At the same time the beta dropped from 1.76 to 0.71. Maranhão could only reduce the cases observed in relation to the expected number of cases in mid-April, about 15 days after implementing the lockdown (on 05/05/2020), when the beta moved from 1.45 to 0.87. In a further sharp reduction, 15 days after the lockdown was declared, the beta fell from 0.87 to 0.50. Pernambuco separated from the expected curve without intervention around April 15, when the observed cases proved to be fewer than expected, and the beta fell from 1.30 to 0.68. In general, the SEIR models of all the states converged, showing a reduction in the slope of the epidemic curves due to the decrease in contact and transmission of COVID-19, in parallel to the social distancing strategies and mask use implemented in these states.

Discussion

This study shows that COVID-19 has had a very severe impact on the Northeast region. The government’s lack of leadership in formulating a consistent national response has led state governments and municipal administrations to take decisions to respond to the epidemic and to mitigate it. This study focusing on the states of the Northeast region, one of the poorest regions of the country, but heterogeneous in some socioeconomic and demographic indicators, allows to verify the variations in the magnitude of the epidemic and explore the effectiveness of the social distance measures implemented in a socioeconomic and political context unfavorable.

The interaction between poverty, unemployment, education level, race, and gender and has been documented as determinants of COVID-192828 Burström B, Tao W. Social determinants of health and inequalities in COVID-19. Eur J Public Health 2020; ckaa095.,2929 Patel AP, Paranjpe MD, Kathiresan NP, Rivas MA, Khera AV. Race, Socioeconomic Deprivation, and Hospitalization for COVID-19 in English participants of a National Biobank. medRxiv 2020; [preprint].. People with low incomes are more exposed to infectious diseases, including SARS-CoV-2, due to the lack of access to basic sanitation and treated water, the lack of access to education and health services, the use of public transport, subject to crowding and the difficulty of self-isolating, living in precarious housing and/or with an excessive number of residents, or also because they are more often unemployed or engaged in informal jobs. Brazil began the epidemic with 41% of workers listed as informal workers, that is, without social or labor rights, such as unemployment benefits, vacations, maternity leave or social security. It is not by chance that the nine states in the Northeast are among the 12 Brazilian states with the lowest reported income in the PNAD (National Household Survey) in May 20203030 Brasil. Instituto Brasileiro de Geografia e Estatística (IBGE). Pesquisa Nacional por Amostra de Domicílios Contínua COVID-19. Rio de Janeiro: IBGE; 2020.. This same survey showed that unemployment rates increased in 12 Brazilian states, half of which are in the Northeast Region, and four of these six are among the states with the highest unemployment rates in the country. In two of the states with increasing unemployment (Maranhão and Ceará), and in Pernambuco, which already reported an unemployment rate higher than Ceará, the highest case rates for COVID-19 were reported. High rates of unemployment or informal work, without government assistance for income support, lead to a daily search for an income and, consequently, to low adherence to social distance measures, and high rates of disease transmission1414 Aquino EM, Silveira IH, Pescarini JM, Aquino R, Souza-Filho JA. Medidas de distanciamento social no controle da pandemia de COVID-19: potenciais impactos e desafios no Brasil. Cien Saude Colet 2020; 25:2423-2446.. Social distancing not only takes longer to reach the poorest peripheral areas and regions, but it is targeted at formal and salaried workers. In Medellin, the second largest city in Colombia, the emergency provision of minimum income for people in need, registered in advance via an app, enabled both timely mapping and delivery of income, ensuring the successful implementation of physical distancing and the mitigation of COVID-193131 UOL. Medellín controla vírus com arriscada coleta de dados em massa [Internet]. 2020 [acessado 2020 Jul 10]. Disponível em: encurta-dor.com.br/mwzEY
encurta-dor.com.br/mwzEY...
. In Brazil, the existence of the Single Registry (Cadastro Único) accelerated the delivery of the emergency aid approved by Congress, which theoretically could arrive more quickly to needy segments of the population3232 Brasil. Governo do Brasil. Assistência Social. Solicitar Auxílio Emergencial (Coronavírus - COVID 19) [Internet]. 2020 [acessado 2020 Jul 10]. Disponível em: https://www.gov.br/pt-br/servicos/solicitar-auxilio-emergencial-de-r-600-covid-19
https://www.gov.br/pt-br/servicos/solici...
. However, due to a series of operational failures, a large portion of this population had to expose themselves to large gatherings while waiting at banks and national lottery stations to receive the aid3333 Globo. Agências da Caixa voltam a ter longas filas por problemas com auxílio emergencial [Internet]. Rio de Janeiro: O Globo; 2020 [acessado 2020 Jul 10]. Disponível em: https://g1.globo.com/jornal-nacional/noticia/2020/07/22/agencias-da-caixa-voltam-a-ter-longas-filas-por-problemas-com-auxilio-emergencial.ghtml
https://g1.globo.com/jornal-nacional/not...
.

The comparative analysis conducted among the states showed that several northeastern states share factors that could amplify the epidemic, such as high urban density, high levels of inequality, high rates of informal work, presence of an international airport and being internal domestic tourism destinations. Although these characteristics help to explain a part of the distribution pattern of COVID-19, it can not provide a more definitive explanation of the data provided. Possibly, one of the problems that aggravated COVID-19 in the Northeast was the early entry of the virus in the region, in this case, in the state of Ceará. Despite the fact that the authorities in Ceará launched a technically solid and effective set of preventive actions dated from the detection of the first official case, this state had the highest number of reported cases of COVID-19. However, retrospective investigation of cases of Severe Acute Respiratory Syndrome, conducted by the epidemiological surveillance service of the state and the Fortaleza Department of Health, showed that 50 cases of COVID-19 were confirmed in Fortaleza one day after Wuhan’s notification to WHO in January3434 Ceará. Secretaria de Saúde. IntegraSUS: transparência da saúde do Ceará Fortaleza [Internet]. 2020 [acessado 2020 Jul 10]. Disponível em: https://integrasus.saude.ce.gov.br/
https://integrasus.saude.ce.gov.br/...
. Another study of the evolution and spread of the SARS-CoV-2 epidemic in the country showed that three different strains were circulating in Brazil, and that they were transmitted before the confirmation of the first official case3535 Candido DS, Claro IM, Jesus JG, Souza WM, Moreira FRR, Dellicour S, Mellan TA, Plessis L, Pereira RHM, Sales FCS, Manuli ER, Thézé J, Almeida L, Menezes MT, Voloch CM, Fumagalli MJ, Coletti TM, Silva CAM, Ramundo MS, Amorim MR, Hoeltgebaum HH, Mishra S, Gill MS, Carvalho LM, Buss LF, Prete Jr CA, Ashworth J, Nakaya HI, Peixoto PS, Brady OJ, Nicholls SM, Tanuri A, Rossi ÁD, Braga CKV, Gerber AL, Guimarães APC, Gaburo Jr N, Alencar CS, Ferreira ACS, Lima CX, Levi JE, Granato C, Ferreira GM, Francisco Jr RS, Granja F, Garcia MT, Moretti ML, Perroud Jr MW, Castiñeiras TMPP, Lazari CS, Hill SC, Santos AAS, Simeoni CL, Forato J, Sposito AC, Schreiber AZ, Santos MNN, Sá CZ, Souza RP, Resende-Moreira LC, Teixeira MM, Hubner J, Leme PAF, Moreira RG, Nogueira ML, Brazil-UK Centre for Arbovirus Discovery, Diagnosis, Genomics and Epidemiology (CADDE) Genomic Network, Ferguson NM, Costa SF, Proenca-Modena JL, Vasconcelos ATR, Bhatt S, Lemey P, Wu C, Rambaut A, Loman NJ, Aguiar RS, Pybus OG, Sabino EC, Faria NR. Evolution and epidemic spread of SARS-CoV-2 in Brazil. Science 2020; eabd2161.. One of the strains reached a higher prevalence in the Southeast Region, including São Paulo and Rio de Janeiro; the other two entered the country through the state of Ceará, probably from Europe and the United States3535 Candido DS, Claro IM, Jesus JG, Souza WM, Moreira FRR, Dellicour S, Mellan TA, Plessis L, Pereira RHM, Sales FCS, Manuli ER, Thézé J, Almeida L, Menezes MT, Voloch CM, Fumagalli MJ, Coletti TM, Silva CAM, Ramundo MS, Amorim MR, Hoeltgebaum HH, Mishra S, Gill MS, Carvalho LM, Buss LF, Prete Jr CA, Ashworth J, Nakaya HI, Peixoto PS, Brady OJ, Nicholls SM, Tanuri A, Rossi ÁD, Braga CKV, Gerber AL, Guimarães APC, Gaburo Jr N, Alencar CS, Ferreira ACS, Lima CX, Levi JE, Granato C, Ferreira GM, Francisco Jr RS, Granja F, Garcia MT, Moretti ML, Perroud Jr MW, Castiñeiras TMPP, Lazari CS, Hill SC, Santos AAS, Simeoni CL, Forato J, Sposito AC, Schreiber AZ, Santos MNN, Sá CZ, Souza RP, Resende-Moreira LC, Teixeira MM, Hubner J, Leme PAF, Moreira RG, Nogueira ML, Brazil-UK Centre for Arbovirus Discovery, Diagnosis, Genomics and Epidemiology (CADDE) Genomic Network, Ferguson NM, Costa SF, Proenca-Modena JL, Vasconcelos ATR, Bhatt S, Lemey P, Wu C, Rambaut A, Loman NJ, Aguiar RS, Pybus OG, Sabino EC, Faria NR. Evolution and epidemic spread of SARS-CoV-2 in Brazil. Science 2020; eabd2161.. The consequences of this process, unknown at the time, was the wide and silent spread of SARS-CoV-2 in the country, without mitigation measures being adopted3636 Souza WM, Buss LF, Candido DS, Carrera JP, Li S, Zarebski A, Pereira RHM, Prete Jr CA, Souza-Santos AA, Parag KV, Belotti MCTD, Vincenti-Gonzalez MF, Messina J, Sales FCS, Andrade PS, Nascimento VH, Ghilardi F, Abade L, Gutierrez B, Kraemer MUG, Braga CKV, Aguiar RS, Alexander N, Mayaud P, Brady OJ, Marcilio I, Gouveia N, Li G, Tami A, Oliveira SB, Porto VBG, Ganem F, Almeida WAF, Fantinato FFST, Macário EM, Oliveira WK, Nogueira ML, Pybus OG, Wu C, Croda J, Sabino EC, Faria NR. Epidemiological and clinical characteristics of the early phase of the COVID-19 epidemic in Brazil. Nature Humam Behav 2020; 4:856-865.. When the governments of the states of the Northeast region took the first measures for social distancing in mid-March, when the first cases were officially notified, Ceará, for example, already had 1,160 confirmed COVID-19 cases, and about 1/3 of the cities in the interior of the state already had cases3434 Ceará. Secretaria de Saúde. IntegraSUS: transparência da saúde do Ceará Fortaleza [Internet]. 2020 [acessado 2020 Jul 10]. Disponível em: https://integrasus.saude.ce.gov.br/
https://integrasus.saude.ce.gov.br/...
. The situation in Ceará does not represent the failure of measures adopted for social distancing, as there is evidence that without these measures the situation would be even more serious, with more cases and deaths. Rather, it may be related to weaknesses in the epidemiological surveillance system that did not have access to the first cases of COVID-19 that occurred in Fortaleza and in other parts of the State. It seems that the information about wealthy patients, entering the private health care system, wasn’t promptly reported to the surveillance system2424 Jorge DC, Rodrigues MS, Silva MS, Cardim LL, Silva NB, Silveira IH, Silva VAF, Pereira FAC, Pinho STR, Andrade RFS, Ramos PIP, Oliveira JF. Assessing the nationwide impact of COVID-19 mitigation policies on the transmission rate of SARS-CoV-2 in Brazil. medRxiv 2020; [preprint]..

An important set of measures to restrict the movement of people and, consequently, reduce the spread of the epidemic, concerns measures to close businesses and non-essential services, close schools, as well as limit the movement of people within neighborhoods and between municipalities. We observed to a greater or lesser degree a reduction in social mobility and a slowdown in the progression of the epidemic in all states in the Northeast. However, as of the data of this submission, the reproductive number remained above 1 in all states, indicating that the epidemic is still growing.

At the same time that the epidemic entered, in most states, through its capitals and the more affluent population, and after community transmission was established in these cities, it headed for the interior of the states, much less well prepared than the capitals. The numbers of cases reported in all the capitals of the region, as of the date this paper was finished, are fewer than the total for the interior of the states. The Rt calculated for each state is now strongly influenced by the growth in the number of cases in the interior. The lack of tertiary care in these municipalities, especially with respect to ICU beds per capita, or the availability of health professionals with skills in the management of hospitalized patients in these units, combined with the difficulty of access of the population to these units constitutes greater fragility for those communities in the face of COVID-19 in the interior of the states. At the same time, in the absence of other measures to be carried out at this time, social distancing and social isolation measures, such as lockdown, may be the only way to actually reduce the transmission of COVID-191414 Aquino EM, Silveira IH, Pescarini JM, Aquino R, Souza-Filho JA. Medidas de distanciamento social no controle da pandemia de COVID-19: potenciais impactos e desafios no Brasil. Cien Saude Colet 2020; 25:2423-2446..

The available data do not allow a more detailed breakdown of the social distancing measures in regions or neighborhoods of greater and lesser purchasing power, or of areas closer and farther from the capitals of the Northeast. However, a national serological study shows cities in the North Region with more than 20% prevalence, and the Northeast with about 15%, while most other developed regions have rates around 1%99 Hallal P, Hartwig F, Horta B, Victora GD, Silveira M, Struchiner C, Vidaletti LP, Neumann N, Pellanda LC, Dellagostin OA, Burattini MN, Menezes AM, Barros FC, Barros AJ, Victoria CG. Remarkable variability in SARS-CoV-2 antibodies across Brazilian regions: nationwide serological household survey in 27 states. medRxiv 2020; [preprint].. A survey carried out in the capital of Ceará, Fortaleza, demonstrates the great inequality experienced in large urban centers. While the poorest neighborhoods had almost a quarter of its population affected by COVID-19, the richest neighborhoods, producing the first cases, had six times less, staying in the 4% range3737 Ceará. Secretaria da Saúde. Secretaria Municipal de Saúde. Inquérito soroprevalência coronavírus em Fortaleza. Fortaleza: Secretaria da Saúde do Estado do Ceará e Secretaria Municipal de Saúde de Fortaleza; 2020..

Poverty and striking inequality in the Northeast, associated with other socioeconomic and cultural conditions of the region, provide the clues for why COVID-19 is so intense and widespread. On the other hand, the fact that mitigation actions were taken early, as soon as the first cases were officially recognized, mitigated the effects of the pandemic. While the responses have been different by state – given the absence of centralized measures issued by the federal government – there was an important reduction in the number of deaths by COVID-19 in the region as a result of physical distancing measures and actions on health taken by the state governments.

The COVID-19 epidemic hit one of the most unequal countries in the world, aggravating an unprecedented political, economic and social crisis that preceded the outbreak of the epidemic. The recent picture of COVID-19 in Northeast Brazil demonstrates that various epidemics are occurring simultaneously. It is likely that there will be new waves both in the capitals and in the interior. This prognosis must remain until either a medical intervention, such as a vaccine, or the population reaches, after a few waves, levels of “herd immunity,” or more radical measures are implemented to support physical distancing, such as: income extended to vulnerable families, mass testing, isolation of confirmed cases with locations for isolation provided when it is not possible (hotels, schools, etc. ), and contact tracing; measures at this point in time have not been implemented at levels necessary to end or significantly reduce the disease. The COVID-19 epidemic in northeastern Brazil, which encompasses the region and the country in one of the most difficult periods of its political, economic and social history, will leave its marks forever in future generations.

References

  • 1
    Centro de Integração de Dados e Conhecimentos para Saúde (CIDACS). Fiocruz. UFBA. Painel Rede CoVida 2020 [Internet]. [acessado 2020 Jun 20]. Disponível em: https://painel.covid19br.org/
    » https://painel.covid19br.org/
  • 2
    Ahmed F, Ahmed NE, Pissarides C, Stiglitz J. Why inequality could spread COVID-19. Lancet Public Health 2020; 5(5):e240.
  • 3
    Dolce J, Fonseca B. Desigualdade social é fator de risco para mortes de crianças e adolescentes por Covid-19 no país 2020 [Internet]. Pública Agência de Jornalismo Investigativo; 2020 [acessado 2020 Jun 20]. Disponível em: https://apublica.org/2020/06/desigualdade-social -e-fator-de-risco-para-mortes-de-criancas-e-adolescentes-por-covid-19-no-pais/#.XuiTr59onPV.face book
    » https://apublica.org/2020/06/desigualdade-social -e-fator-de-risco-para-mortes-de-criancas-e-adolescentes-por-covid-19-no-pais/#.XuiTr59onPV.face book
  • 4
    Sumner A, Hoy C, Ortiz-Juarez E. Estimates of the Impact of COVID-19 on Global Poverty. UNUWIDER 2020; 800-809.
  • 5
    Mahler DG, Lakner C, Aguilar RAC, Wu H. Updated Estimates of the Impact of COVID-19 on Global Poverty [Internet]. World Bank; 2020 [acessado 2020 Jun 20].Disponível em: https://blogs.worldbank.org/opendata/updated-estimates-impact-covid-19-global-poverty
    » https://blogs.worldbank.org/opendata/updated-estimates-impact-covid-19-global-poverty
  • 6
    Goes EF, Ramos DO, Ferreira AJF. Desigualdades raciais em saúde e a pandemia da Covid-19. Trab Educ Saude 2020; 18(3):e00278110.
  • 7
    Alon TM, Doepke M, Olmstead-Rumsey J, Tertilt M. The impact of COVID-19 on gender equality. NBER Working Papers 2020; 26947.
  • 8
    Wenham C, Smith J, Morgan R. COVID-19: the gendered impacts of the outbreak. Lancet 2020; 395(10227):846-848.
  • 9
    Hallal P, Hartwig F, Horta B, Victora GD, Silveira M, Struchiner C, Vidaletti LP, Neumann N, Pellanda LC, Dellagostin OA, Burattini MN, Menezes AM, Barros FC, Barros AJ, Victoria CG. Remarkable variability in SARS-CoV-2 antibodies across Brazilian regions: nationwide serological household survey in 27 states. medRxiv 2020; [preprint].
  • 10
    Lewnard JA, Lo NC. Scientific and ethical basis for social-distancing interventions against COVID-19. Lancet Infect Dis 2020; 20(6):631.
  • 11
    World Health Organization (WHO). Water, sanitation, hygiene and waste management for COVID-19: technical brief, 03 March 2020 Geneva: WHO; 2020.
  • 12
    Eikenberry SE, Mancuso M, Iboi E, Phan T, Eikenberry K, Kuang Y, Kostelich E, Gumel AB. To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic. Infect Dis Model 2020; 5:293-308.
  • 13
    Stutt RO, Retkute R, Bradley M, Gilligan CA, Colvin J. A modelling framework to assess the likely effectiveness of facemasks in combination with 'lock-down'in managing the COVID-19 pandemic. Proceedings Royal Society A 2020; 476(2238):20200376.
  • 14
    Aquino EM, Silveira IH, Pescarini JM, Aquino R, Souza-Filho JA. Medidas de distanciamento social no controle da pandemia de COVID-19: potenciais impactos e desafios no Brasil. Cien Saude Colet 2020; 25:2423-2446.
  • 15
    Brasil. Instituto Brasileiro de Geografia e Estatística (IBGE). Painel de Indicadores [Internet]. 2020 [acessado 2020 Jun 20]. Disponível em: https://www.ibge.gov.br/indicadores
    » https://www.ibge.gov.br/indicadores
  • 16
    Cotta W. COVID-19 in Brazil 2020 [Internet]. [acessado 2020 Jun 20]. Disponível em: https://wesleycota.com/
    » https://wesleycota.com/
  • 17
    Centro de Integração de Dados e Conhecimentos para Saúde (CIDACS). Fiocruz. UFBA. CoVida: Ciência, Informação e Solidariedade [Internet]. 2020 [acessado 2020 Jun 20]. Disponível em: https://covid19br.org/
    » https://covid19br.org/
  • 18
    Fundação Oswaldo Cruz (Fiocruz). Painel Infogripe: Monitoramento de casos reportados de síndrome respiratória aguda grave (SRAG) hospitalizados Rio de Janeiro: Fiocruz; 2020.
  • 19
    Brasil. Agência Nacional de Aviação Civil (ANAC). Setor Regulado. Microdados [Internet]. 2020 [acessado 2020 Jun 7]. Disponível em: https://www.anac.gov.br/assuntos/setor-regulado/empresas/envio-de-informacoes/microdados
    » https://www.anac.gov.br/assuntos/setor-regulado/empresas/envio-de-informacoes/microdados
  • 20
    Google LLC. Google COVID-19 Community Mobility Reports 2020 [Internet]. [acessado 2020 Jun 7]. Disponível em: https://www.google.com/covid19/mobility/
    » https://www.google.com/covid19/mobility/
  • 21
    Inloco. Mapa brasileiro da COVID-19 2020 [Internet] [acessado 2020 Jun 7]. Disponível em: https://www.inloco.com.br/
    » https://www.inloco.com.br/
  • 22
    Elliot M, Fairweather I, Olsen W, Pampaka M. A dictionary of social research methods Oxford: Oxford University Press; 2016.
  • 23
    Eggan F. Social Anthropology and the Method of Controlled Comparison. American Anthropologist 1954; 56(5):743-763.
  • 24
    Jorge DC, Rodrigues MS, Silva MS, Cardim LL, Silva NB, Silveira IH, Silva VAF, Pereira FAC, Pinho STR, Andrade RFS, Ramos PIP, Oliveira JF. Assessing the nationwide impact of COVID-19 mitigation policies on the transmission rate of SARS-CoV-2 in Brazil. medRxiv 2020; [preprint].
  • 25
    Oliveira JF, Jorge DC, Veiga RV, Rodrigues MS, Torquato MF, Silva NB, Fiaconne RL, Castro CP, Paiva ASS, Cardim LL, Amad AAS, Lima EABF, Souza DS, Pinho STR, Ramos PIP, Andrade RFS, Rede CoVida Modelling Task-force. Evaluating the burden of COVID-19 on hospital resources in Bahia, Brazil: A modelling-based analysis of 14.8 million individuals. medRxiv 2020; [preprint].
  • 26
    Bastos LS, Niquini RP, Lana RM, Villela DA, Cruz OG, Coelho FC, Codeço CT, Gomes MFC. COVID-19 e hospitalizações por SRAG no Brasil: uma comparação até a 12ª semana epidemiológica de 2020. Cad Saúde Pública 2020; 36:e00070120.
  • 27
    Trindade E. Os circuitos dos ricos e famosos que disseminaram coronavírus no Brasil Folha de São Paulo; 2020.
  • 28
    Burström B, Tao W. Social determinants of health and inequalities in COVID-19. Eur J Public Health 2020; ckaa095.
  • 29
    Patel AP, Paranjpe MD, Kathiresan NP, Rivas MA, Khera AV. Race, Socioeconomic Deprivation, and Hospitalization for COVID-19 in English participants of a National Biobank. medRxiv 2020; [preprint].
  • 30
    Brasil. Instituto Brasileiro de Geografia e Estatística (IBGE). Pesquisa Nacional por Amostra de Domicílios Contínua COVID-19 Rio de Janeiro: IBGE; 2020.
  • 31
    UOL. Medellín controla vírus com arriscada coleta de dados em massa [Internet]. 2020 [acessado 2020 Jul 10]. Disponível em: encurta-dor.com.br/mwzEY
    » encurta-dor.com.br/mwzEY
  • 32
    Brasil. Governo do Brasil. Assistência Social. Solicitar Auxílio Emergencial (Coronavírus - COVID 19) [Internet]. 2020 [acessado 2020 Jul 10]. Disponível em: https://www.gov.br/pt-br/servicos/solicitar-auxilio-emergencial-de-r-600-covid-19
    » https://www.gov.br/pt-br/servicos/solicitar-auxilio-emergencial-de-r-600-covid-19
  • 33
    Globo. Agências da Caixa voltam a ter longas filas por problemas com auxílio emergencial [Internet]. Rio de Janeiro: O Globo; 2020 [acessado 2020 Jul 10]. Disponível em: https://g1.globo.com/jornal-nacional/noticia/2020/07/22/agencias-da-caixa-voltam-a-ter-longas-filas-por-problemas-com-auxilio-emergencial.ghtml
    » https://g1.globo.com/jornal-nacional/noticia/2020/07/22/agencias-da-caixa-voltam-a-ter-longas-filas-por-problemas-com-auxilio-emergencial.ghtml
  • 34
    Ceará. Secretaria de Saúde. IntegraSUS: transparência da saúde do Ceará Fortaleza [Internet]. 2020 [acessado 2020 Jul 10]. Disponível em: https://integrasus.saude.ce.gov.br/
    » https://integrasus.saude.ce.gov.br/
  • 35
    Candido DS, Claro IM, Jesus JG, Souza WM, Moreira FRR, Dellicour S, Mellan TA, Plessis L, Pereira RHM, Sales FCS, Manuli ER, Thézé J, Almeida L, Menezes MT, Voloch CM, Fumagalli MJ, Coletti TM, Silva CAM, Ramundo MS, Amorim MR, Hoeltgebaum HH, Mishra S, Gill MS, Carvalho LM, Buss LF, Prete Jr CA, Ashworth J, Nakaya HI, Peixoto PS, Brady OJ, Nicholls SM, Tanuri A, Rossi ÁD, Braga CKV, Gerber AL, Guimarães APC, Gaburo Jr N, Alencar CS, Ferreira ACS, Lima CX, Levi JE, Granato C, Ferreira GM, Francisco Jr RS, Granja F, Garcia MT, Moretti ML, Perroud Jr MW, Castiñeiras TMPP, Lazari CS, Hill SC, Santos AAS, Simeoni CL, Forato J, Sposito AC, Schreiber AZ, Santos MNN, Sá CZ, Souza RP, Resende-Moreira LC, Teixeira MM, Hubner J, Leme PAF, Moreira RG, Nogueira ML, Brazil-UK Centre for Arbovirus Discovery, Diagnosis, Genomics and Epidemiology (CADDE) Genomic Network, Ferguson NM, Costa SF, Proenca-Modena JL, Vasconcelos ATR, Bhatt S, Lemey P, Wu C, Rambaut A, Loman NJ, Aguiar RS, Pybus OG, Sabino EC, Faria NR. Evolution and epidemic spread of SARS-CoV-2 in Brazil. Science 2020; eabd2161.
  • 36
    Souza WM, Buss LF, Candido DS, Carrera JP, Li S, Zarebski A, Pereira RHM, Prete Jr CA, Souza-Santos AA, Parag KV, Belotti MCTD, Vincenti-Gonzalez MF, Messina J, Sales FCS, Andrade PS, Nascimento VH, Ghilardi F, Abade L, Gutierrez B, Kraemer MUG, Braga CKV, Aguiar RS, Alexander N, Mayaud P, Brady OJ, Marcilio I, Gouveia N, Li G, Tami A, Oliveira SB, Porto VBG, Ganem F, Almeida WAF, Fantinato FFST, Macário EM, Oliveira WK, Nogueira ML, Pybus OG, Wu C, Croda J, Sabino EC, Faria NR. Epidemiological and clinical characteristics of the early phase of the COVID-19 epidemic in Brazil. Nature Humam Behav 2020; 4:856-865.
  • 37
    Ceará. Secretaria da Saúde. Secretaria Municipal de Saúde. Inquérito soroprevalência coronavírus em Fortaleza Fortaleza: Secretaria da Saúde do Estado do Ceará e Secretaria Municipal de Saúde de Fortaleza; 2020.

Publication Dates

  • Publication in this collection
    30 Sept 2020
  • Date of issue
    Oct 2020

History

  • Received
    31 July 2020
  • Accepted
    10 Aug 2020
  • Published
    12 Aug 2020
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