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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.
. 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 (which was not certified by peer review)
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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:[email protected]
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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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14
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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16
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.
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