COVID-19 denialism in Brazil a multifactor study Tarc ısio M. Rocha Filho1 Magda L. Lucio2 Fulvio A. Scorza3 and Marcelo A. Moret4 1ICP-IF Universidade de Bras ılia Campus Universit ario Darcy Ribeiro Asa Norte Bras ılia DF Brazil

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COVID-19 denialism in Brazil: a multifactor study

Tarc´ısio M. Rocha Filho1,*, Magda L. Lucio2, Fulvio A. Scorza3, and Marcelo A. Moret4

1ICP-IF, Universidade de Bras´

ılia, Campus Universit´

ario Darcy Ribeiro, Asa Norte, Bras´

ılia, DF, Brazil

2FACE, Universidade de Bras´

ılia, Campus Universit´

ario Darcy Ribeiro, Asa Norte, Bras´

ılia, DF, Brazil

3Escola Paulista de Medicina, Universidade Federal de S˜

ao Paulo, S˜

ao Paulo – Brazil

4SENAI-CIMATEC, Salvador, BA, Brazil and UNEB, Salvador, BA, Brazil

*marciano@ﬁs.unb.br

ABSTRACT

We discuss the relationships between the outcome of the COVID-19 pandemic in Brazil at the municipal level and different

health, social, demographic, and economic indices. We obtain signiﬁcant correlations between the data gathered for each

municipalitiy and the proportion of cases and deaths by COVID-19 and the results by municipality of the 2018 Brazilian

presidential election. We obtain different estimates for the number of deaths caused by central government denialism of

scientiﬁc facts and measures for mitigation of the pandemic and its the historical, economic, and social roots.

1 Introduction

Amidst the different issues resulting from the policies implemented by different governments the capacity to deal with crisis is

certainly the one with the most overreaching consequences. This was staunchly demonstrated with the advent of the COVID-19

pandemic, caused by the SARS-CoV-2 virus. According to ofﬁcial reports

, since its ﬁrst detection in Wuhan (China) in

December 2019 to the present date, the virus has been responsible for six and a half million deaths and 615 million cases.

Although these ﬁgures are sadly impressive in and of themselves, the real numbers are much worse, with cases signiﬁcantly

under-reported

and under-reported deaths proportionately smaller but still signiﬁcant

. The estimated real number of deaths in

the world is approximately 18 million

, with under-reporting varying signiﬁcantly among countries and regions. This only

shows that the burden of COVID-19 is by far the worst planetary health crisis in more than a century.

A recent report by the Lancet Commission on lessons for the future from the COVID-19 pandemic

addresses both the

correct measures and failures in the mitigation of the COVID-19 pandemic and its social and economic consequences. Among

different analyses, the report points out that most governments and the World Health Organization were too slow to react to the

new pandemic; that many mitigation measures were hindered by wide sectors of the population; and that trust in communities

acted as an important ﬁghting tool against the pandemic. Some authors have addressed the consequences of pandemic mitigation

policies according to government choices and public discourse. Based on surveys from the initial stages of the pandemic,

Pickup and collaborators discussed the effects of political partisanship on attitudes and perceptions of government policies in

mitigating the pandemic in the US and Canadai

, with evidence that partisanship guided the assessment of central government

policies against COVID-19 in both countries. Bennouna et al. studied the great variations of COVID-19 mitigation policies in

Brazil, Mexico, and the US and the importance of central (presidential) and state (governors) coordination, or its absence, in the

adoption of public policies

. The effect of political partisanship on adherence to social distancing in each US state was studied

using mobility statistics and found to be closely related to the respective governor’s political party, with recommendations being

more effective in Democratic than in Republican counties

. A comparison of public health emergency measures and social

policiesD in demonstrated that their simultaneous implementation succeeded in confronting the pandemic, as was the case in

Germany; that social policies without associated health interventions failed to result in an effective mitigation, as occurred in

Brazil and the US; while in India, public health policies simply failed due to the absence of social interventions

. Today, among

different public policies, the most effective against COVID-19 is mass vaccination, which, if properly planned, can signiﬁcantly

reduce the burden of lost lives

. Among the many difﬁculties faced in ﬁghting the pandemic, denial of disease severity and of

scientiﬁc knowledge have a signiﬁcant impact on the burden of health services and on the outcomes of the pandemic11

Denialism in Brazil has been a major issue in mitigating the pandemic, with frequent questioning of mask wearing and

social distancing efﬁcacy, the proposal of early treatment without any scientiﬁc evidence to corroborate its use

and even

backed by a number of physicians

, and denial of vaccine efﬁcacy and safety

. Its impacts in numbers are difﬁcult to estimate,

but its effects are quite visible. For example, the number of COVID-19 deaths in the Brazilian public health system from

January 1, 2021, to March 23, 2022, was signiﬁcantly higher in non-fully vaccinated individuals than in fully vaccinated

individuals

, a consequence of ideological components of COVID-19 denial resulting from erratic and often misleading

arXiv:2210.10840v2 [physics.soc-ph] 20 Nov 2022

speeches from the far-right Brazilian President and central government authorities

16,17

. Indeed, the central government never

issued mask mandates or any form of lockdown in order to prevent the virus’ spread, and there was no systematic testing policy.

Instead, state and municipal authorities implemented these measures, with varying degrees of success

18,19

. These studies put

forward the need for strong coordinated actions between different levels of government for the simultaneous adoption of social

and health policies for an efﬁcient mitigation of the current and future pandemics20.

Here we investigate the relations between series of social, economic, and demographic indicators with the electoral results

for the 2018 second round of the Brazilian presidential election, and how they determined the outcomes of the COVID-19

pandemic in Brazil, and obtain estimates for the number of deaths related to COVID-19 denialism. The current study relies

on a large and representative set of data, signiﬁcantly expands on previous studies focusing on the COVID-19 and election

relationships, and discusses the picture that emerges from such analysis as well as its social and historical roots.

2 Data

The Federate Republic of Brazil is divided into 26 states plus the Federal District, and each state is divided into 5570

municipalities, with a population ranging from

10004

12228 009

(2022 estimates) and an area from

3,656,km2

for Santa

Cruz de Minas in the state of Minas Gerais up to

159533km2

for Altamira in the state of Pará (northern region). A municipality

usually, but not always, corresponds to a city or a small conglomerate of cities. The following data and respective sources were

used in our analysis:

• Total number of COVID-19 cases and deaths by Brazilian municipality21.

• Number of COVID-19 vaccine by dose and type as a function of time for each municipality22.

• Number of votes received by each candidate at the second round of the 2018 Brazilian election23.

• Budget from the federal government for the public health structure received during the year of 202122.

The following social, economic and demographic data for the year of 2010 from the last ofﬁcial completed Census in Brazil:

• Illiteracy rate by municipality, for individual with 18 years of age or more24.

• Proportion of individuals in each municipality with 25 years or more having completed their primary education24.

• GINI index by municipality24.

• Percentage of extremely poor individuals24.

• Average income in each municipality24.

• Human Development Index in each municipality24.

• Population by self-declared race (skin color) in each municipality25.

Although the census data is a decade old as the 2020 census was postponed due to the pandemic, we do not expect a signiﬁcant

change in the rates and proportions used here, but we recognize it as a source of error in the analysis to be presented below. The

recent estimates for the population in each municipality:

• Estimated population in 2022 in each municipality26.

The list of all variable considered in the correlation analysis is summarized in Table 1.

3 Methods

In order to determine the intrinsic relations between each pair of variables listed in Table 1, we use the Spearman rank-order

correlation

rs(A,B)

between two ordered series

A= (A1,

. . .

,ANdata )

and

B= (B1,...,BNdata )

each composed by

Ndata

values. It

is given by the Pearson correlation between their rank values, and for the special case that all ranks are distinct by27:

rs[A,B] = 1−6∑Ndata

i=1d2

Ndata(N2

d−1),(1)

with

the difference in paired ranks of the two data sets

and

given by the difference in position of the

-th data point in the

two data sets in ascending order. The Spearman correlation satisﬁes

−1≥rs≥1

and is a measure for how two variables are

monotonically related, by an increasing or decreasing relation if rs>0 or rs<0, respectively.

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COVID-19denialisminBrazil:amultifactorstudyTarc´sioM.RochaFilho1,*,MagdaL.Lucio2,FulvioA.Scorza3,andMarceloA.Moret41ICP-IF,UniversidadedeBras´lia,CampusUniversit´arioDarcyRibeiro,AsaNorte,Bras´lia,DF,Brazil2FACE,UniversidadedeBras´lia,CampusUniversit´arioDarcyRibeiro,AsaNorte,Bras´lia,DF,Brazil...

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COVID-19 denialism in Brazil a multifactor study Tarc ısio M. Rocha Filho1 Magda L. Lucio2 Fulvio A. Scorza3 and Marcelo A. Moret4 1ICP-IF Universidade de Bras ılia Campus Universit ario Darcy Ribeiro Asa Norte Bras ılia DF Brazil

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