1

EpidemiologicalandclinicalcharacteristicsoftheearlyphaseoftheCOVID-19

epidemicinBrazil

WilliamMarcieldeSouza,PhD1*,LewisFletcherBuss,MD2*,DarlandaSilvaCandido,MSc3*,Jean-

Paul Carrera,MSc3,4*, Sabrina Li,MSc5*, Alexander E. Zarebski3, PhD,Maria F. Vincenti-Gonzalez,

PhD6, JaneyMessina,PhD5,7,FlaviaCristinadaSilvaSales,BSc2,PameladosSantosAndrade,MSc2,

CarlosA.PreteJr,MSc8,VítorHeloizNascimento,PhD8,FabioGhilardi,MD2,RafaelHenriqueMoraes

Pereira,DPhil9,AndrezaAruskadeSouzaSantos,PhD10,LeandroAbade,DPhil3,BernardoGutierrez,

MSc3,11, Moritz U. G. Kraemer, DPhil3,12,13, Renato Santana Aguiar, PhD14, Neal Alexander, PhD15,

Philippe Mayaud, MD16, Oliver J. Brady, DPhil17, Izabel Oliva Marcilio de Souza, MD18, Nelson

Gouveia, PhD19, Guangdi Li, PhD20, Adriana Tami, PhD6, Silvano Barbosa deOliveira,MSc21, Victor

Bertollo Gomes Porto, MD21, Fabiana Ganem, PhD21, Walquiria Aparecida Ferreira de Almeida,

MSc22, Francieli Fontana Sutile Tardetti Fantinato, BSc22, Eduardo Marques Macário, PhD22,

Wanderson Kleber de Oliveira, PhD22, Oliver G. Pybus, DPhil3, Chieh-HsiWu, PhD23*, Julio Croda,

MD22,24,25*#,EsterC.Sabino,MD3*,NunoRodriguesFaria,PhD2,3,26*#

*Theseauthorscontributedequally

#Correspondingauthor

1.VirologyResearchCenter,UniversityofSãoPaulo,RibeirãoPreto,Brazil.

2.InstitutodeMedicinaTropicaldaFaculdadedeMedicinadaUniversidadedeSãoPaulo,Brazil.

3.DepartmentofZoology,UniversityofOxford,Oxford,UK.

4. Department of Research in Virology and Biotechnology, Gorgas Memorial Institute of Health

Studies,PanamaCity,Panama.

5.SchoolofGeographyandtheEnvironment,UniversityofOxford,UK.

6. University of Groningen, University Medical Center Groningen, Department of Medical

MicrobiologyandInfectionPrevention,Groningen,Netherlands.

7.OxfordSchoolofGlobalandAreaStudies,UniversityofOxford,UK.

8.DepartmentofElectronicSystemsEngineering,UniversityofSãoPaulo,Brazil

9.InstituteforAppliedEconomicResearch(IPEA),Brasilia,Brazil

10.BrazilianStudiesProgramme,LatinAmericanCentre,UniversityofOxford,UK

11.SchoolofBiologicalandEnvironmentalSciences,UniversidadSanFranciscodeQuitoUSFQ,

Quito,Ecuador.

12.HarvardMedicalSchool,HarvardUniversity,Boston,MA,USA.

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NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

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13.BostonChildren’sHospital,Boston,MA,USA.

14.DepartamentodeGenética, EcologiaeEvolução, InstitutodeCiênciasBiológicas,Universidade

FederaldeMinasGerais,BeloHorizonte,Brazil.

15.MRC Tropical EpidemiologyGroup, Department of InfectiousDisease Epidemiology, Faculty of

EpidemiologyandPopulationHealth,LondonSchoolofHygiene&TropicalMedicine,London,UK.

16.DepartmentofClinicalResearch,FacultyofEpidemiologyandPopulationHealth,LondonSchool

ofHygiene&TropicalMedicine,London,UK.

17.CentrefortheMathematicalModellingofInfectiousDiseases,DepartmentofInfectiousDisease

Epidemiology,LondonSchoolofHygiene&TropicalMedicine,London,UK.

18. Núcleo de Vigilância Epidemiológica do Hospital das Clínicas da Faculdade de Medicina,

UniversidadedeSãoPaulo,SãoPaulo,Brazil.

19.DepartamentoMedicinaPreventiva,FaculdadedeMedicinadaUniversidadedeSãoPaulo,São

Paulo,Brazil.

20.DepartmentofEpidemiologyandHealthStatistics,XiangyaSchoolofPublicHealth,CentralSouth

University,Changsha410078,China.

21.SecretariatofHealthSurveillance,DepartmentofImmunizationandCommunicableDiseases,

BrazilianMinistryofHealth,Brasília,Brazil.

22.SecretariatofHealthSurveillance,BrazilianMinistryofHealth,Brasília,Brazil.

23.MathematicalSciences,UniversityofSouthampton,Southampton,UK.

24. Laboratório de Pesquisa em Ciências da Saúde, Universidade Federal da Grande Dourados,

Dourados,MatoGrossodoSul,Brazil.

25.FundaçãoOswaldoCruz,CampoGrande,MatoGrossodoSul,CampoGrande,Brazil.

26.DepartmentofInfectiousDiseaseEpidemiology,ImperialCollegeLondon,London,UK.

Correspondingauthor:

NunoRodriguesFaria,PhD

DepartmentofInfectiousDiseaseEpidemiologySchoolofPublicHealthImperialCollegeLondonMedicalSchoolBuildingStMary’sCampusNortfolkPlaceLondonW21PGEmail:nfaria@ic.ac.uk

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Summary

Background

The first case of COVID-19 was detected in Brazil on February 25, 2020. We report the

epidemiological, demographic, and clinical findings for confirmed COVID-19 cases during the first

monthoftheepidemicinBrazil.

Methods

Individual-level andaggregatedCOVID-19datawereanalysed to investigatedemographicprofiles,

socioeconomicdriversandage-sexstructureofCOVID-19testedcases.Basicreproductionnumbers

(R0) were investigated for São Paulo and Rio de Janeiro. Multivariate logistic regression analyses

were used to identify symptoms associatedwith confirmed cases and risk factors associatedwith

hospitalization.Laboratorydiagnosisforeightrespiratoryviruseswereobtainedfor2,429cases.

Findings

By March 25, 1,468 confirmed cases were notified in Brazil, of whom 10% (147 of 1,468) were

hospitalised.Of the cases acquired locally (77·8%), two thirds (66·9%of 5,746)were confirmed in

private laboratories.Overall,positiveassociationbetweenhigherpercapita incomeandCOVID-19

diagnosiswasidentified.Themedianageofdetectedcaseswas39years(IQR30-53).ThemedianR0

was2·9forSãoPauloandRiodeJaneiro.Cardiovasculardisease/hypertensionwereassociatedwith

hospitalization. Co-circulation of six respiratory viruses, including influenza A and B and human

rhinoviruswasdetectedinlowlevels.

Interpretation

SocioeconomicdisparitydeterminesaccesstoSARS-CoV-2testinginBrazil.Thelowermedianageof

infectionandhospitalizationcomparedtoothercountries isexpectedduetoayoungerpopulation

structure.Enhancedsurveillanceofrespiratorypathogensacrosssocioeconomicstatusesisessential

tobetterunderstandandhaltSARS-CoV-2transmission.

Funding

SãoPauloResearchFoundation,MedicalResearchCouncil,WellcomeTrustandRoyalSociety.

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Introduction

Coronavirusdisease2019(COVID-19) isasevereacuterespiratoryinfectionthatemergedinearlyDecember

2019 inWuhan,China1.COVID-19 is causedby the severeacute respiratory syndromecoronavirus2 (SARS-

CoV-2), an enveloped, single-stranded positive-sense RNA virus that belongs to theBetacoronavirus genus,

Coronaviridaefamily2.SARS-CoV-2isphylogeneticallysimilartobatderivedSARS-likecoronaviruses3.Human-

to-humantransmissionoccursprimarilyviarespiratorydropletsanddirectcontact,similartoinfluenzaviruses,

SARS-CoVandMiddleEastRespiratorySyndromevirus(MERS-CoV)4.

Themostcommonlyreportedclinicalsymptomsarefever,drycough,fatigue,dyspnoea,anosmia,ageusiaor

somecombinationofthesesymptoms1,4-6.SARS-CoV-2spreadrapidlyandasofApril23,2020,morethan2.7

millioncaseshavebeenconfirmedacrosstheglobe,resultinginatleast187,330deaths7.

Brazil identified its first case on February 25, 2020. Despite a prompt public health response, Brazil now

accountsforathirdofallcasesreportedinLatinAmerica(46,701confirmedcases,including2,940deaths,as

ofApril23,2020)7. In this study,wedescribe theepidemiological,demographicalandclinical characteristics

fromtheearlyphaseoftheCOVID-19epidemicinBrazil.

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Methods

Ethicalapproval

ThestudywassupportedbytheBrazilianMinistryofHealthandethicalapprovalwasprovidedby

the national ethical review board (Comissão Nacional de Ética em Pesquisa, CONEP), protocol

numberCAAE30127020.0.0000.0068.

Individual-leveldataonnotifiedcasesfromBrazil

To investigate individual-level diagnostic, demographic, self-reported travel history, place of

residenceandlikelyplaceofinfection,differentialdiagnosisforotherrespiratorypathogens,aswell

asclinicaldetails, includingcomorbidities,wecollectedcasedatanotifiedtotheREDCapdatabase8

fromFebruary25toMarch25,2020.Datawascontributedbypublichealthandprivatelaboratories.

Diagnosis and case definitions (see Appendix, pp.1) were based on World Health Organization

(WHO) interim guidance. To explore the time-lag between the number of imported cases and of

localcasesweusedtheGrangercausalitytest9.

GeospatialanalysisofCOVID-19cases,demographicandsocio-economicdata

Basedondata fromthe firstCOVID-19 reports inBrazil10,wehypothesized that ratesof incidence

and testing for COVID-19 are higher in areas of higher per capita income. For the Greater

MetropolitanRegionofSãoPaulo (GMRSP),percapita incomeat theGMRSPneighbourhood level

(517 zones) were retrieved from the 2017 Pesquisa Origem e Destino survey

(www.metro.sp.gov.br/pesquisa-od/). 13,913 notified cases (COVID-19 confirmed, ruled out, and

withoutfinaldiagnosis)residentintheGMRSPweregeocodedbasedonself-reportedaddressusing

theGalileoalgorithmandverifiedusingGoogleAPI.Percapita incomeforeachzonewaslinkedto

eachnotifiedcasebasedonresidentialaddress.Wecomparepercapitaincomeforallnotifiedcases

betweenthosetested(positiveandnegative)anduntested,andforconfirmedcasesbyRT-PCR.Full

detailsonthestatisticalanalysiscanbefoundintheAppendix,pp.1.

Basicreproductionnumber(R0)estimation

To quantify transmission potential of COVID-19 in Brazil, an exponential model was used to

representtheincidenceofCOVID-19atthenationallevelandinSãoPauloandRiodeJaneirostates.

Timeseriesofconfirmedcasesweremodelledassamplesfromanegativebinomialdistributionwith

a mean equal to a fixed portion of the incidence. The analysis was carried out in a Bayesian

framework with uninformative priors on all parameters apart from the removal rate, which was

given an informative prior. The informative prior ensured that the average duration forwhich an

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individual is infectious is5to14days11 (Appendix,Figs.S1-S2).Standarddiagnosticswereusedto

checkwhethertheMarkovChainMonteCarlo(MCMC)samplesweresatisfactory.Fulldetailsofthe

modelused, theestimationprocessandconvergenceofMarkovChainMonteCarlochainscanbe

foundintheAppendix,pp.2.

Univariateandmultivariateanalysis

To investigate which factors are associated with a confirmed COVID-19 result and with

hospitalization summary statistics were calculated for continuous variables and for categorical

variablesandsummarizedasmedians(rangeandinterquartilerange, IQR),asappropriate.Missing

datawere removed (assumedmissing at random) (seeAppendix Table S1 and Fig. S3). Uni- and

multivariateanalysisincludedonlycaseswithcompleteinformationfortherelevantvariables.These

analyses compared demographics, symptoms, clinical signs and comorbidities between confirmed

COVID-19 cases (RT-PCR positive) and ruled-out COVID-19 cases (RT-PCR negative). Additionally,

separatemultivariatelogisticregressionmodelswerebuilttopredicthospitalisation(binaryvariable:

hospitalised vs. not hospitalised) based on symptoms, clinical signals and comorbidities, and to

predict testing status (positive or negative for RT-PCR SARS-CoV-2). The associations between the

outcome and independent variables were reported as adjusted odds ratios (AOR) with 95%

confidence intervals and likelihood ratio test (LRT) using the univariate and multivariate logistic

regression models. Model diagnostics were performed to check for model specification errors,

multicollinearityandinfluentialobservations.A0·05significancelevelwasapplied.

Roleofthefundingsource

Thefunderhadnoroleinstudydesign,datacollection,dataanalysis,datainterpretation,orwriting

of the report. The corresponding author had full access to all data in the study and had final

responsibilityforthedecisiontosubmitforpublication.

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Results

ByMarch25,2020, fourweeksafter the first reportofCOVID-19 inBrazil,67,344COVID-19cases

had been notified as COVID-19 suspected infections from 172 cities across all five administrative

regions of Brazil. Of these, 1,468 cases were confirmed (2·18% of all notified cases) and notified

throughtheREDCapsystem(Fig.1A), including1,144cases (77.9%of1,468)diagnosedbyRT-PCR

and324(22.1%of1,468)onclinicallyepidemiologicalgrounds.Duringthisperiod,anadditional965

aggregatedcaseswerenotifiedtotheMinistryofHealth,totalling2,433confirmedcasesduringthe

first month. Based on notifications via REDCap, 35% (517 of 1,468) of confirmed cases were

imported. Of these 326 had the country of travel recorded. TheUSA and Italy accounted for the

majority of the reported imported cases (USA: 82 [25·2%] and Italy 71 [21·7%] of 326 imported

cases) (Fig. 1B). The epidemic curves of locally-acquired cases followed the curves from imported

caseswithalagoftwodays(Grangercausalitytest)(Fig.1A).

Figure1.Epidemiological anddemographic characteristicsof the first confirmedCOVID-19 cases

withinthefirst4weeksoftheepidemicinBrazil.A.Numberofconfirmedcasesintravellers(blue)

andlocaltransmission(orange)fromREDCapdatabase.Greybarsshownumberofaggregatedcases

reportedtoMinistryofHealth(covid.saude.gov.br/).B. Importedcasesbyself-reportedcountryof

infectionfromREDCapdatabase.C.AggregatedcasesreportedtoMinistryofHealthbygeographic

region. GRU = Guarulhos São Paulo International Airport, MXP = Malpensa Milan International

Airport,MoH =Ministry of Health. L = Local, I = Imported, and SRAG = Severe acute respiratory

syndrome.

According to aggregated data,most confirmed caseswere reported in São Paulo (862 [35·4%] of

2,433)followedbyRiodeJaneiro(370[15·2%]).Toestimatethebasicreproductionnumber(R0)in

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these locations used a Bayesian approach to fit an exponential growth model to COVID-19

aggregated incidencedata. Consistentwithprevious studies in China andoverseas12,we find that

epidemicspread inSãoPauloandRiode Janeirostates ischaracterizedbysimilarR0valuesof2·9

(95%CI 2·1-4·4) and 2·9 (95%CI 2·2-4·5). TheR0 for Brazilwas slightly higherwithmedianof 3·2

(95%CI2·4-5·4)(Fig.2).

Figure2.R0 inearlyphaseof theCOVID-19epidemic inBrazil.A.Violinplots showing theR0 for

COVID-19estimatedusingtheMoHpublicdatabytheMarch25,2020(n=2,433cases).B.Modelfit

frompointestimatetoconfirmedcasesacrossallofBrazil,C.SãoPaulostate,D.RiodeJaneirostate

(seeAppendixfordetails).

Analysis of the age-sex structure of confirmed and notified cases compared to the Brazilian

demographic structure revealed a disproportionately lower proportion of confirmed COVID-19

infectionsreportedinyoungercategories(0–9,10–19yearsofage)andaslightlyhigherproportion

in middle-age categories (20–29 and 30–39 years of age) (Fig. 3). Specifically, compared to the

proportionofthetotalBrazilianpopulationperagecategory,theproportionofconfirmedCOVID-19

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infections in the0–9and10–19yearsofagecategoriesare16·4-and5·3-fold lowercompared to

Braziliandemographicstructure(Fig.3A).

Wefoundthatmostconfirmedcaseswereinmales(776[54·7%]of1,420–46confirmedcaseshad

missinginformationforsexand/orage)(Fig.3A).Themedianageofcaseswas39years(IQR,30–53,

range:newborn–93years).Nearlyhalf(695[48·9%]of1,420)oftheconfirmedcaseswereintheage

rangeof20to39yearsofage(Fig.3A).Similarly,51·6%(2,288of4,438)ofcasestestedforSARS-

CoV-2 belonged to this age-group (Fig. 3B), which is substantially higher than the corresponding

fraction of the Brazilian population (68,451,093 [32%] of 211,755,692). 9·5% (133) of cases were

healthcareworkers.Overall,onlyfournewborns,threeinfants(6to8month-old),tenchildren(1to

12 years old), and twelve adolescents (12 to 17 years old) were diagnosed with COVID-19. In

addition,ninepatientswerepregnant,oneinthefirsttrimester,oneinthesecondtrimester,fourin

thethirdtrimesterand3hadmissinginformation).SixcaseswereHIV-positive.

Figure 3. Demographic profile of confirmed COVID-19 (A) and notified (B) cases and total

population in Brazil. Age classes are shown on the left. Proportion of confirmed COVID19 and

notified cases from the REDCap database for each age-class category are shown as filled bars.

Proportion(%)ofthecountry’spopulationineachage-sexclassisshownasfadedbars.

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Nearly a third of confirmed cases (462 [36·2%] of 1,277 - 191 confirmed cases had missing

information forcontactwithconfirmedcase) reportedclosecontactwithanotherconfirmedcase.

After categorizing exposure in travel (international), home (household), work (including schools),

andhealthfacility(healthcareworkers),wefoundthatoverhalfreportedhavinghadcontactwitha

suspectedcaseattheirworkplace(120[33·2%]of385)orathome(88[22·9%]of385)(Fig.4A).

Figure4BshowschangesinnotifiedconfirmedandnotifieduntestedCOVID-19casesinBrazilover

the study period. Two thirds (586 [66·9%] of 876) of diagnostic tests were performed in private

medicallaboratorieswherecostsvariedtypicallybetween300-690BrazilianReais(BRL)(forcontext,

currentminimummonthlysalaryis1,045BRL).

Figure4.COVID-19diagnosisandsocioeconomicfactorsintheGMRSP.A.Self-reportedsourceof

exposure. B. Total number of cases notified according to classification status from February 25

through March 25, 2020 (bars), and the proportion of notified cases being tested (line). C.

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Distribution of per capita income based on neighbourhood of residence for all notified COVID-19

cases grouped according to testing status (tested vs. untested), and for RT-PCR confirmed, in the

early-phaseoftheepidemic.Theoveralldistributionofaveragepercapita incomefor517zonesin

theGMRSPweightedbypopulationsizeisshownontheleftofpanel3C.

Totestwhethernotifiedtestedcaseswereassociatedwithsocioeconomicstatus,weevaluatedthe

association between COVID-19 diagnosis and socioeconomic status in the subset of cases in the

GreaterMetropolitan Region of São Paulo (GMRSP) regionwith geocoded residential information

usinganordinalprobitmodel.WefoundthattheproportionoftestedcasesinGMRSPincreasedas

incomepercapita increases (z-score=0.19, likelihoodratio testP-value<0.01) (Fig.4C,TableS2).

Moreover, the increase in theproportionof testedcases foraunit-increase in income ishigher in

weeks2,3and4comparedtoweek1.Fortherangeofincomepercapitaobserved,giventhesame

amountofincomepercapita,theproportionsoftestedcaseswerelowerinweeks2,3and4than

week1.Overall,therewasanoticeableupwardstrendintheassociationbetweentestingrateand

percapitaincomeuncoveringawideningsocioeconomicdisparityintestingpracticeasthenumber

of cases expands. The income distribution of the untested fraction increasingly approximates the

average for GMSP, whereas the tested and confirmed cases (both laboratory and clinical

epidemiological)areconsistentlyhigheroverthestudyperiod.

We also analysed the results for other respiratory pathogens tested in Brazil as part of the

differentialdiagnosisbyCentralPublicHealthLaboratoriesandNationalInfluenzaCentres(Brazilian

Ministry ofHealth). Respiratory virusesmost frequently identified in patientswith suspected, but

negativediagnosisofCOVID-19wereinfluenzaAvirus(347[14·3%]of2,429),influenzaBvirus(251

[10·3%]of2,429)andhumanrhinovirus(136[5·6%]of2,429).Wefoundco-detectionofSARS-CoV-2

withsixotherrespiratoryviruses,themostfrequentlywerewithinfluenzaA(11[0·5%]of2,429)and

humanrhinovirus(6[0·2%]of2,429)(Appendix,Fig.S4).

We next analysed most common symptoms for confirmed COVID-19 cases in Brazil and which

symptomswere linked tohospitalization.Mostpatientswith confirmedCOVID-19 (1,351 [92%]of

1,468)reportedatleastonesymptom,themostcommonbeingcough(1,040[70·8%]of1,468)and

fever(982[66·9%]of1,468).Otherfrequentsymptomsreportedwerecoryza(495of[33·7%]1,468),

sorethroat(483[32·9%]of1,468)andmyalgia(450[30·7%]of1,468).Thecharacteristics,symptoms

andclinicalsignsofCOVID-19casesconfirmedbyRT-PCRpositive,ruledoutbyRT-PCR,andnotified

COVID-19cases,butnotestedaresummarisedinAppendixTableS3.

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In a univariate analysis of4,387 caseswith a final classification as confirmed (n=1,101) or

discarded(COVID-19ruledout)(n=3,286),wefoundthatincreasingage,symptoms(cough,difficulty

breathing, dyspnoea/tachypnea, sputum production, nasal congestion, nasal flaring,

nausea/vomiting, headache, irritability/confusion, difficulty swallowing, intercostal retraction and

Alterationonchestauscultation)andclinical signs (feverandconjunctivalcongestion)werehigher

associatedwith a negative SARS-CoV-2 results (seeAppendix Table S4). Overall, a total of 12·5%

(184/1,468)ofconfirmedCOVID-19caseshadatleastonecomorbidity.Mostcommoncomorbidities

wereheartdisease,hypertension,diabetes,andchronicrespiratorydisease.

ByMarch25,10%(147of1,468)ofpatientswithCOVID-19hadbeenhospitalized,ofwhom

15·6%(23of147)requiredmechanicalventilation.Thedateofhospitalizationwasavailablefor140

patients. Themedian time from symptomonset tohospital admissionwas fourdays (IQR=2 to6

days,range0to26days).ThemostfrequentsymptomsinhospitalizedpatientswithCOVID-19were

fever (122 [83%]of147)andcough (118 [80·3%]of147).Multivariable logistic regressionshowed

that chest X-ray abnormalities (AOR: 55·1 [18.88-121.24], p<0·001) andO2 saturation <95% (AOR:

14·80 [4·33-55], p<0·001) were strongly associated with hospitalization (Figure 5A). Most

hospitalizedpatientsweremale(87[59·2%]of147).ThemedianageofhospitalizedCOVID-19cases

was55yearsofage(IQR=40-68),rangingfromnewbornto93yearsofage;24·25%(36of147)ofthe

hospitalized cases were aged≤39 years. One of the four newborns was hospitalized with fever,cough, dyspnoea/tachypnoea, altered chest radiology, and abnormal findings on auscultation, but

didnot requiremechanicalventilation.Also,oneoutof sixHIVpatients thatwerediagnosedwith

COVID-19was hospitalized and underwentmechanical ventilation. None of the reported cases in

babies,children,orpregnantwomenrequiredhospitalization.

Alargeproportion(59[40·1%]of147)ofhospitalizedpatientshadatleastonecomorbidity,

andthemostcommonwerecardiovasculardisease/hypertension(47[29·9%]of147),diabetes(14

[9·5%]of147),otherrespiratorydiseases(11[7·5%]of147)andsolidorhaematologicalneoplasm(7

[4·8%] of 147). Proportions in general population for cardiovascular disease and diabetes are

respectively 4·2%, and6·2%13.Multivariable logistic regressionanalysis showed increasingoddsof

hospitalization in patients with cardiovascular diseases/hypertension (AOR: 3·41 [1·97-5·87],

p<0·001) (Figure 5B). Interestingly, age was not significantly associated with hospitalization after

accountingforco-morbidities.

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Figure 5.Multivariable logistic regression predicting hospitalisation among RT-PCR SARS-CoV-2

positivecases.A.Analysisofsymptomsandclinicalsigns.B.Analysisofreportedcomorbidities.

FourdeathsduetoCOVID-19wererecorded,and48patientsremainedhospitalizedduring

the studyperiod.Basedon theavailabledischargedates, 3·5% (51of 1,468) confirmed caseshad

recoveredfromtheCOVID-19infectionbyMarch25,2020.Themedianageamongthefourdeaths

was60years (IQR=56 -66), ranging from49 to74-yearsofage)andwitha sex ratioof1:1.The

mediantimefromthesymptomonsettodeathwassevendays(IQR=4-9·5,range,3to14days).

Of the four fatal cases,onehadcardiovasculardisease/hypertension,onehadbothcardiovascular

disease/hypertension and renal disease, and two fatal cases had no reported comorbidities. Only

one case had reported close contact with a confirmed COVID-19 case reinforcing that local

transmissionwasalreadywellestablishedinBrazilbyMarch25,2020.

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Discussion

These findings provide evidence that SARS-CoV-2 transmission in Brazil shifted rapidly from a

scenarioofimportedtolocaltransmission.Wefoundthattheproportionoftestedcasesishigherin

zoneswithhigherpercapita income.WeshowedthatduringthefirstmonthofCOVID-19inBrazil,

only 33·1% of the reported confirmed cases were conducted in public health laboratories. Our

results support similar transmission potential (R0) of SARS-CoV-2 in Brazil to other geographic

regions.Overall,ourclinical findingsdemonstratethatchestX-rayabnormalitiesandO2saturation

<95%arestronglyassociatedwithhospitalization.Thecombinationofuniversalaccesstodiagnostic

and the successof interventionswill dictate the fateofCOVID-19 inBrazil.Overall, these findings

filledinagapinourunderstandingofCOVID-19earlyestablishmentinLatinAmerica.

Weidentifiedseverallimitationsinourstudy.First,detailedindividual-leveldatawasonlyavailable

for the first month of the epidemic in Brazil. Moreover, several cases had incomplete

documentation, such as hospitalization date,mechanical ventilation, and travel history. Real-time

aggregateddataandopen-accessopenlinelistshavethepotentialtoprovidereal-timeinsightsinto

transmissibility14. Secondly, our retrospective study has focused predominantly on symptomatic

patients (92%) that presented themselves to health services for testing. Therefore, we cannot

describe the full spectrum of disease. Population-based serologic surveys are urgently needed to

properly determine the asymptomatic and oligosymptomatic fraction. Finally, many patients

remained hospitalizedwhen the dataset was extracted, and, wewere unable to estimate clinical

outcomesgiventhelongdurationofinfection.

Together with changes in surveillance guidelines, socioeconomic bias in testing suggests that the

numberofconfirmedcasecountsmaysubstantiallyunderestimatethetruenumberofcasesinthe

population. Additional reasons for underreporting include (i) a significant proportion of

asymptomatic infections15,(ii)peoplewithmildandevenmoderatediseaseareunlikelytopresent

tohealthservicesfortesting,(iii)limitedtestingcapacityinpublichealthserviceinBrazilinfaceof

the largenumberof casesdue todelays in importing reagentsandkitsused inmolecular testing.

Close monitoring of state- and municipality-level data will further help to inform mitigation

strategies.

Our results suggest thatapproximately50%of theCOVID-19cases inBrazilwere skewed towards

age groups between 20 to 39 years with substantially fewer cases in younger age groups. This

pattern could be explained by (i) a higher risk of exposure of this group due to more frequent

international travel (travelbanswereonly implementedonMarch23,2020), and (ii) youngerage

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15

groups being less likely to acquire an infection and/or less likely to acquire significant symptoms

uponbeinginfected16.

COVID-19 infections were reported in paediatric and pregnant patients17-19. Paediatric infection

appears to typically be of mild or moderate severity; we observed a similar proportion of

asymptomaticinfectionscomparedtoreportsin36childreninChina(24%vs.28%)19.Also,theonset

symptomsofpregnantwomenweresimilartothosereportedinnon-pregnantadultswithCOVID-19

infection.Ontheotherhand,proportionofhospitalisationofpaediatricpatientsinBrazilwaslower

thanthoseobservedforchildreninChina(3.3%vs.38.9%)19.Also,incontrasttoChina,noneofthe

pregnantwomenthattestedpositiveforCOVID-19inBrazilhadpneumoniaorwerehospitalized17,18.

However,theabsence/lowernumberofhospitalisationscouldbeexplainedbyresourceavailability

and local clinical practice guidelines. Despite the small sample size, our findings in pregnant and

paediatricpatientsintheearly-phaseCOVID-19pandemicinBrazilrequirefurtherunderstandingof

SARS-CoV-2infectioninthesegroups.

Althoughclinicalfeatures inBrazilaresimilartothoserecentlyreportedinothercountries1,4,5,we

observed that8%ofconfirmedcases reportednosymptoms.This shouldnotbeconsideredasan

estimate of the asymptomatic fraction. Firstly, it is not possible to distinguish true asymptomatic

infectionsfromcasesinthepre-symptomaticphase.Secondly,routinelycollecteddatatendstobe

incomplete.Thirdly,thesecasesweretestedbecausetheywereincontactwithaknownconfirmed

case. Lastly, there is an ascertainment bias towards symptomatic infections due to the case

definition used for notification (Appendix). Other estimates of the asymptomatic fraction have

varied widely, including 18% on the Diamond Princess ship15, 50-75% in the Italian village of

Vo’Euganeo20and31%basedonrepatriationflightscreening21.

Overall, 10% of COVID-19 cases in Brazil were hospitalized compared to 19% in the USA22. As

mentioned above, these differencesmay reflect factors other than disease severity, for example,

resourceavailability,localclinicalpracticeguidelinesandtestingavailability.Ontheotherhand,they

may also reflect right censoring,whereby cases thatwerenotified towards the endof theperiod

studiedhadnot yet beenhospitalized. Thiswouldbe expected given themedian lagof four days

betweensymptomonsetandhospitalizationobserved inBrazil.Althoughagewasnotarisk factor

for hospitalization after controlling for comorbidities, is should be noted that the age distribution

among patients who were hospitalized differed from that reported in China, with a higher

proportionofyounger(<39years:Brazil,24.5%vs.China,10%)andolderpatients(>70years:Brazil,

21.8%vs.China,15%)18.However, such comparisonsneed tobe taken cautiouslydue todifferent

testingandnotificationpractisesinthetwocountries.

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Weshowedthatpatientswithpre-existingcardiovasculardiseases/hypertensionwereat increased

riskofhospitalization.Theprevalenceofatleastonecomorbidconditionamonginfectedindividuals

in Brazil was similar to that reported in China (12.5% vs. 10.5%)23. Previous studies suggest that

persons with underlying health conditions, such as cardiovascular, diabetes and chronic lung

diseases, appear to be at higher risk for severe COVID-19 infection than persons without these

conditions22,24.Pre-existingcardiovasculardiseaseappears tobeparticularly important,potentially

duetotheinvolvementofthereninangiotensinsystemsignallingpathway25.

This study provides new information on co-circulation and co-detection of other respiratory

pathogens in the early phase of the COVID-19 epidemic in Brazil. Particularly, we found co-

circulationofeightotherrespiratoryviruses,themostcommonrespiratoryinfectionswereinfluenza

A and B, and human rhinovirus (HRV). Co-detection of SARS-CoV-2 with influenza A and human

metapneumovirus (hMPV) have also been reported in China26,27. Here we found co-detection of

SARS-CoV-2withinfluenzaAandhMPV,andweexpandedthedescriptionoftheothermultipleco-

detectionscenariosofSARS-CoV-2withotherrespiratoryviruses,includingHRV,influenzaB,human

respiratorysyncytialvirus,andothercoronaviruses(i.e.coronavirus229E/NL63,hCoVOC43/HKU1).

Although,viralco-infectionhasbeenreportedwithmanyotherrespiratoryviruses,nodifferencein

clinicaldiseaseseveritybetweenviralco-infectionandsingleinfectionhasbeenreported28.

In conclusion, we provide the first description of COVID-19 in Brazil. Our study provides crucial

information fordiagnostic screeningandhealth-careplanning, and for future studies investigating

the impact of non-pharmaceutical and pharmaceutical interventions in mitigating COVID-19

transmission.

Contributors

JC, JPC, LFB, CAP, VHN,WMS,DSC, AEZ, JM, FCSS, PSA, FG, AASS, BG, CHW, RHMP, SL, NG, SBO,

VBGP,FG,WAFA,FFSTF,EMMandWKOcollectedtheepidemiological,spatialandclinicaldataand

processedstatisticaldata.NRF,WMS,LFB,CHW,JPC,DCS,JM,ECS,PM,SL,RHMP,LA,AASS,GL,AT,

MFVG,MUGK,RSA,NA,PM,OJB,IOMS,NG,GL,OGP,AEZ,andJCinterpretedtheresultsandwrote

themanuscript.Allauthorsreadandrevisedthefinalmanuscript.JC,WMS,LFB,MFVG,andNGJare

responsibleforsummarisingepidemiologicalandclinicaldata.

Declarationofinterests

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17

Wedeclarenocompetinginterests.

Acknowledgments

Theauthorsthankthecliniciansandepidemiologistfortechnicalsupport.Thisworkwassupported

by a FAPESP (2018/14389-0) and Medical Research Council and CADDE partnership award

(MR/S0195/1).NRFissupportedbyaSirHenryDaleFellowship(204311/Z/16/Z).WMSissupported

by the SãoPauloResearch Foundation,Brazil (No. 2017/13981-0).OJBwas fundedbya SirHenry

WellcomeFellowshipfundedbytheWellcomeTrust(206471/Z/17/Z).VHNandCAPweresupported

byFAPESP(2018/12579-7).AEZandBGaresupportedbyOxfordMartinSchool.

References1. Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel

coronavirusinWuhan,China.Lancet2020;395(10223):497-506.

2. CoronaviridaeStudyGroupofthe InternationalCommitteeonTaxonomyofV.Thespecies

Severeacuterespiratorysyndrome-relatedcoronavirus:classifying2019-nCoVandnaming itSARS-

CoV-2.Naturemicrobiology2020.

3. Lu R, Zhao X, Li J, et al. Genomic characterisation and epidemiology of 2019 novel

coronavirus:implicationsforvirusoriginsandreceptorbinding.Lancet2020;395(10224):565-74.

4. GuanWJ,NiZY,HuY,etal.ClinicalCharacteristicsofCoronavirusDisease2019inChina.The

NewEnglandjournalofmedicine2020.

5. HolshueML,DeBoltC,LindquistS,etal.FirstCaseof2019NovelCoronavirusintheUnited

States.TheNewEnglandjournalofmedicine2020;382(10):929-36.

6. LivingstonE,BucherK.CoronavirusDisease2019(COVID-19)inItaly.Jama2020.

7. World-Health-Organization.Coronavirusdisease(COVID-2019)situationreports.2020.

8. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data

capture(REDCap)--ametadata-drivenmethodologyandworkflowprocessforprovidingtranslational

researchinformaticssupport.Journalofbiomedicalinformatics2009;42(2):377-81.

9. Granger CWJ. Investigating Causal Relations by Econometric Models and Cross-spectral

Methods.Econometrica1969;37(3):424-38.

10. CandidoDS,Watts,A.,Abade,L.,Kraemer,M.U.G.,Pybus,O.G.,Croda,J.,deOliveira,W.,

Khan, K., Sabino, E. C., Faria, N. R. Routes for COVID-19 importation in Brazil. Journal of Travel

Medicine2020;inpress.

. CC-BY-NC 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted April 29, 2020. .https://doi.org/10.1101/2020.04.25.20077396doi: medRxiv preprint

18

11. Wolfel R, CormanVM,GuggemosW, et al. Virological assessmentof hospitalizedpatients

withCOVID-2019.Nature2020.

12. Liu Y, Gayle, A. A.,Wilder-Smith, A., Rocklov, J. The reproductive number of COVID-19 is

highercomparedtoSARScoronavirus.JournalofTravelMedicine2020;27(2):taaa021.

13. IBGE. Pesquisa Nacional de Saúde 2013, Percepção do estado de saúde, estilos de vida e

doençascrônicas.RiodeJaneiro,2014.

14. Xu B, KraemerMUG, Open C-DCG. Open access epidemiological data from the COVID-19

outbreak.TheLancetInfectiousdiseases2020.

15. MizumotoK, KagayaK, ZarebskiA, ChowellG. Estimating the asymptomatic proportionof

coronavirusdisease2019(COVID-19)casesonboardtheDiamondPrincesscruiseship,Yokohama,

Japan, 2020. Euro surveillance : bulletin Europeen sur les maladies transmissibles = European

communicablediseasebulletin2020;25(10).

16. Li Q, Guan X, Wu P, et al. Early Transmission Dynamics in Wuhan, China, of Novel

Coronavirus-InfectedPneumonia.TheNewEnglandjournalofmedicine2020;382(13):1199-207.

17. YuN,LiW,KangQ,etal.Clinicalfeaturesandobstetricandneonataloutcomesofpregnant

patients with COVID-19 in Wuhan, China: a retrospective, single-centre, descriptive study. The

LancetInfectiousdiseases2020.

18. ChenH,Guo J,WangC,etal.Clinical characteristicsand intrauterinevertical transmission

potentialofCOVID-19infectioninninepregnantwomen:aretrospectivereviewofmedicalrecords.

Lancet2020;395(10226):809-15.

19. Qiu H,Wu J, Hong L, Luo Y, Song Q, Chen D. Clinical and epidemiological features of 36

childrenwithcoronavirusdisease2019(COVID-19)inZhejiang,China:anobservationalcohortstudy.

TheLancetinfectiousdiseases2020.

20. DayM. Covid-19: identifying and isolating asymptomatic people helped eliminate virus in

Italianvillage.Bmj2020;368:m1165.

21. Nishiura H, LintonNM, Akhmetzhanov AR. Serial interval of novel coronavirus (COVID-19)

infections. Internationaljournalofinfectiousdiseases:IJID:officialpublicationoftheInternational

SocietyforInfectiousDiseases2020;93:284-6.

22. TeamCC-R.SevereOutcomesAmongPatientswithCoronavirusDisease2019 (COVID-19) -

United States, February 12-March 16, 2020.MMWRMorbidity andmortalityweekly report 2020;

69(12):343-6.

23. Wu Z, McGoogan JM. Characteristics of and Important Lessons From the Coronavirus

Disease2019(COVID-19)OutbreakinChina:SummaryofaReportof72314CasesFromtheChinese

CenterforDiseaseControlandPrevention.Jama2020.

. CC-BY-NC 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted April 29, 2020. .https://doi.org/10.1101/2020.04.25.20077396doi: medRxiv preprint

19

24. MadjidM,Safavi-NaeiniP,SolomonSD,VardenyO.PotentialEffectsofCoronavirusesonthe

CardiovascularSystem:AReview.JAMAcardiology2020.

25. Bavishi C, Maddox TM, Messerli FH. Coronavirus Disease 2019 (COVID-19) Infection and

ReninAngiotensinSystemBlockers.JAMAcardiology2020.

26. WuX,CaiY,HuangX,etal.Co-infectionwithSARS-CoV-2and InfluenzaAVirus inPatient

withPneumonia,China.Emerginginfectiousdiseases2020;26(6).

27. Touzard-Romo F, Tape C, Lonks JR. Co-infection with SARS-CoV-2 and Human

Metapneumovirus.RhodeIslandmedicaljournal2020;103(2):75-6.

28. Asner SA, ScienceME, Tran D, SmiejaM,Merglen A,Mertz D. Clinical disease severity of

respiratoryviralco-infectionversussingleviralinfection:asystematicreviewandmeta-analysis.PloS

one2014;9(6):e99392.

. CC-BY-NC 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted April 29, 2020. .https://doi.org/10.1101/2020.04.25.20077396doi: medRxiv preprint