Novo coronavírus (Sars-CoV-2): difusão espacial e outro patamar para a socialização dos investimentos no Brasil

Silveira, Márcio Rogério; Felipe, Nelson Fernandes; Cocco, Rodrigo Giraldi; Felácio, Rafael Matos; Rodrigues, Lucas Azeredo

doi:10.22296/2317-1529.rbeur.202024pt

Cited by 3 publications

(4 citation statements)

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“…21 A study showed that the age group of people affected by the Influenza A/H1N1pdm09 virus in Brazil, in 2009, was predominant in children under 2 years old and adults aged between 20 and 29 years old. 6 On the other hand, data on the age group of people affected by COVID-19, in the literature investigated in this study, are in accordance with the main international statistics, which indicate the age above 60 years old. 22 On the incidence and mortality rates: the result of the disparity in the Brazilian regions may be related to the non-compliance with the isolation measures and the use of masks with the increase of cases in people of different ages.…”

Section: Discussionsupporting

confidence: 88%

“…The main Brazilian regions that had the highest incidence were the South and Southeast. 6 In December 2019, a series of cases of pneumonia caused by a β-coronavirus identified in Hubei province, Wuhan, China, were reported. 9 On January 12, 2020, the disease was named COVID-19 and on March 11 this year a pandemic state was declared.…”

Section: Discussionmentioning

confidence: 99%

“…4 On February 26, 2020, the first case of COVID-19 was identified in Brazil, according to data from the Ministry of Health. 5 It is a disease initially identified in China, which spread rapidly among the world population and generated negative impacts on people's health and the countries' economy 6 . The spread of the virus that causes COVID-19 and the ability to control it is still a challenge for health agencies and researchers 7 .…”

Section: Introductionmentioning

confidence: 99%

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Influenza A/H1N1 e COVID-19 no Brasil: impactos e diferenças epidemiológicas

Cândido¹,

Costa²,

Gonçalves³

et al. 2020

RECI

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Justificative and Objectives: In 2009, Brazil faced the influenza A/H1N1pdm09 pandemic that infected at least 50 thousand people. In 2020, it faces another pandemic caused by the SARS-Cov-2 virus . Because it is a new disease, there is much speculation about it and comparison with other scenarios, often based on fake news. This study compares the impacts and epidemiological differences of Influenza A/H1N1 and COVID-19 in Brazil. Methods: Quantitative, descriptive, epidemiological study, based on documents, whose data were collected on the information platforms of the Brazilian Ministry of Health and the World Health Organization, in addition to scientific articles. The data on Influenza A/H1N1 refer to 2009 and the data on COVID-19 to the period from March to April 30, 2020. Results: It appears that in Brazil, in just two months, the number of cases of COVID-19 (85380) has already exceeded the total cases of influenza A/H1N1pdm09 (50482) that occurred in the whole year of 2009 and caused a triple of deaths. Therefore, COVID-19 is more severe, given the proportions reached in lethality, due to the lack of vaccine and specific treatment of cases. Conclusion:The COVID-19 pandemic is more impactful for Brazil than the influenza A/H1N1pdm09 pandemic. Descriptors: Brazil. Coronavirus Infections. Epidemiology. Influenza A Virus. Public Health.RESUMO Justificativa e Objetivos: Em 2009, o Brasil enfrentou a pandemia de influenza A/H1N1pdm09 que infectou, pelo menos, 50 mil pessoas. Em 2020, enfrenta outra pandemia causada pelo vírus SARS-Cov-2 (COVID-19). Por se tratar de uma doença nova, há muita especulação sobre a mesma, assim como comparação com outros cenários, muitas vezes com base em informações falsas. Este estudo compara os impactos e diferenças epidemiológicas da Influenza A/H1N1 e COVID-19 no Brasil. Métodos: Estudo quantitativo, descritivo,

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Section: Discussionsupporting

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Influenza A/H1N1 e COVID-19 no Brasil: impactos e diferenças epidemiológicas

Cândido¹,

Costa²,

Gonçalves³

et al. 2020

RECI

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“…The spatial analysis of the coronavirus spread can provide an important tool for the management of public health, showing the routes of disease dispersion and the fragility of the municipalities concerning their sociodemographic characteristics (GUIMARÃES et al, 2020). In the case of COVID-19, Silveira et al (2020) have highlighted that in countries like Brazil, its dissemination occurred mainly through commercial passenger aviation, following primarily the configuration and logic of the urban hierarchy networks, transport and the population arrangement of cities.…”

Section: Risk and Social Vulnerabilities In The Brazilian Context Of mentioning

confidence: 99%

Influence of socio-economic indicators and territorial networks at the spatiotemporal spread dynamics of Covid-19 in Brazil

Albuquerque

Santos

Silveira

et al. 2021

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This work aims to provide an overview of the territorial evolution of COVID-19 (SARS-CoV-2) in Brazil using socio-demographic variables, for the time span between February 26, 2020 until January 24, 2021. Socio-demographic indicators, basic sanitation infrastructure data, and epidemiological bulletins were integrated using Principal Components Analysis (PCA) to develop a social vulnerability index (SVI), to estimate the degree of exposure risk of the Brazilian population to COVID-19. The results indicate that the majority of confirmed cases were reported from the main Brazilian capitals, linked to well-developed port and airport modes. In terms of deaths, the states of São Paulo, Rio de Janeiro, Ceará and Pernambuco were at the top of the ranking. On the contrary, there were some states of the mid-west (Mato Grosso do Sul) and the north (Acre, Amapá, Roraima, Rondônia and Tocantins), that recorded low mortality indexes. The SVI reveals that the states of the north and north-east are the most vulnerable. Regarding the metropolitan areas, it was observed that the main capitals of the north and north-east, with the exception of Salvador, present significantly more critical numbers in terms of dissemination and deaths by COVID-19 than the capitals of the south-southeast, where the SVI is lower. The comparative exception was Santa Catarina state metropolitan areas. Finally, as the virus does not strike everyone in the same way, one of the great challenges is to search for solutions to cope with COVID-19 in the face of very unequal realities. Thus, a reflection on the strategies adopted by the Brazilian government is relevant, while considering the continental dimensions and the diversity of the Brazilian regions, to obtain a better analysis of the more vulnerable populations and social groups.

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Can the Content of Social Networks Explain Epidemic Outbreaks?

Maia

Martínez

Marteleto

et al. 2023

Popul Res Policy Rev

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People share and seek information online that reflects a variety of social phenomena, including concerns about health conditions. We analyze how the contents of social networks provide real-time information to monitor and anticipate policies aimed at controlling or mitigating public health outbreaks. In November 2020, we collected tweets on the COVID-19 pandemic with content ranging from safety measures, vaccination, health, to politics. We then tested different specifications of spatial econometrics models to relate the frequency of selected keywords with administrative data on COVID-19 cases and deaths. Our results highlight how mentions of selected keywords can significantly explain future COVID-19 cases and deaths in one locality. We discuss two main mechanisms potentially explaining the links we find between Twitter contents and COVID-19 diffusion: risk perception and health behavior.

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Novo coronavírus (Sars-CoV-2): difusão espacial e outro patamar para a socialização dos investimentos no Brasil

Cited by 3 publications

References 4 publications

Influenza A/H1N1 e COVID-19 no Brasil: impactos e diferenças epidemiológicas

Influenza A/H1N1 e COVID-19 no Brasil: impactos e diferenças epidemiológicas

Influence of socio-economic indicators and territorial networks at the spatiotemporal spread dynamics of Covid-19 in Brazil

Can the Content of Social Networks Explain Epidemic Outbreaks?

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