A systematic review and meta-analysis of published research data on COVID-19 infection fatality rates
Gideon Meyerowitz-Katz
a,b,*, Lea Merone
c,daWesternSydneyLocalHealthDistrict,Australia
bUniversityofWollongong,Australia
cJamesCookUniversity,Australia
dTropicalPublicHealthService,Cairns,Australia
ARTICLE INFO Articlehistory:
Received8July2020
Receivedinrevisedform20September2020 Accepted24September2020
Keywords:
COVID-19 SARS-CoV-2 Infection-fatalityrate Globalhealth Deathrate
ABSTRACT
An important unknown during the coronavirus disease-2019 (COVID-19) pandemic hasbeen the infectionfatalityrate(IFR).Thisdiffersfromthecasefatalityrate(CFR)asanestimateofthenumberof deathsandasaproportionofthetotalnumberofcases,includingthosewhoaremildandasymptomatic.
WhiletheCFRisextremelyvaluableforexperts,IFRisincreasinglybeingcalledforbypolicymakersand thelaypublicasanestimateoftheoverallmortalityfromCOVID-19.
Methods:Pubmed,Medline,SSRN,andMedrxivweresearchedusingasetoftermsandBooleanoperators on25/04/2020andre-searchedon14/05/2020,21/05/2020and16/06/2020.Articleswerescreenedfor inclusionbybothauthors.Meta-analysiswasperformedinStata15.1byusingthemetancommand, basedonIFRandconfidenceintervalsextractedfromeachstudy.Google/GoogleScholarwasusedto assessthegreyliteraturerelatingtogovernmentreports.
Results:Afterexclusions,therewere24estimatesofIFRincludedinthefinalmeta-analysis,fromawide rangeofcountries,publishedbetweenFebruaryandJune2020.
Themeta-analysisdemonstratedapointestimateofIFRof0.68%(0.53%–0.82%)withhighheterogeneity (p<0.001).
Conclusion:Basedonasystematicreviewandmeta-analysisofpublishedevidenceonCOVID-19untilJuly 2020, theIFRof thediseaseacrosspopulations is0.68%(0.53%–0.82%).However,due toveryhigh heterogeneityinthemeta-analysis,itisdifficulttoknowifthisrepresentsacompletelyunbiasedpoint estimate.Itislikelythat,duetoageandperhapsunderlyingcomorbiditiesinthepopulation,different placeswillexperiencedifferentIFRsduetothedisease.Givenissueswithmortalityrecording,itisalso likelythatthisrepresentsanunderestimateofthetrueIFRfigure.Moreresearchlookingatage-stratified IFRisurgentlyneededtoinformpolicymakingonthisfront.
©2020PublishedbyElsevierLtdonbehalfofInternationalSocietyforInfectiousDiseases.Thisisanopen accessarticleundertheCCBY-NC-NDlicense(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Introduction
Theyear2020sawtheemergenceofaglobalpandemic,coronavirus disease-2019 (COVID-19), caused by the SARS-CoV-2 virus, which began in China andhassincespread across the world.Oneof themost challengingquestionstoanswerduringtheCOVID-19pandemichas beenregardingthetrueinfectionfatalityrate(IFR)ofthedisease.While casefatalityrates(CFR)areeminently calculablefrom variouspublished datasources(Kahathuduwaetal.,2020)–CFRbeingthenumberof deathsdivided by the number ofconfirmed cases–it is far more difficult to extrapolate to the proportion of all infected individuals who have died
duetotheinfectionbecausethosewhohaveverymild,atypicalor asymptomatic diseaseare frequentlyleftundetected andtherefore omittedfrom fatalityratecalculations (RinaldiandParadisi,2020).
Giventheissueswithobtainingaccurateestimates,itisnotunexpected that thereare wide disparitiesin the publishedestimates ofcase numbers.Thisisanissueforseveralreasons,mostimportantlyinthat policyisdependent on modelling,andmodellingis dependenton assumptions.IfwedonothavearobustestimateofIFR,itischallenging tomakepredictionsaboutthetrueimpactofCOVID-19inanygiven susceptible population, which may stymie policy development and may haveseriousconsequencesfordecision-makingintothefuture.While CFRisamorecommonlyusedstatistic,andisverywidelyunderstood amongexperts,IFRprovidesimportantcontextforpolicymakersthatis hardtoconvey,particularlygiventhewidevariationinCFRestimates.
WhileCFRisnaturallyafunctionofthedenominator–i.e.howmany peoplehavebeentestedforthedisease–policymakersareoftenmost
*Correspondingauthorat:UniversityofWollongong,Australia E-mailaddress:[email protected] (G.Meyerowitz-Katz).
https://doi.org/10.1016/j.ijid.2020.09.1464
1201-9712/©2020PublishedbyElsevierLtdonbehalfofInternationalSocietyforInfectiousDiseases.ThisisanopenaccessarticleundertheCCBY-NC-NDlicense(http://
creativecommons.org/licenses/by-nc-nd/4.0/).
ContentslistsavailableatScienceDirect
International Journal of Infectious Diseases
j o u r n a l h o m ep a g e : w w w . e l s e v i e r . c o m / l o c a te / i j i d
interestedinthetotalburdeninthepopulationratherthanthebiased estimatesgivenfromtestingonlytheacutelyunwellpatients.
Thisisparticularlyimportantwhenconsideringthereopening of countries post‘lockdown’. Dependingon theseverity of the disease,itmaybereasonabletoreopenservicessuchasschools, bars,andclubs,atdifferenttimings.Anothersalientpointisthe expectedburdenofdiseaseinyoungeragegroups—whilethere arelikelylong-termimpactsotherthandeath,itwillbeimportant for future planning to know how many people in various age groupsarelikelytodieiftheinfectionbecomeswidespreadacross societies.Age-stratifiedestimatesarealsoimportantasitmaygive countries somewayto predictthe number of deathsexpected giventheirdemographicbreakdown.
ThereareanumberofmethodsforinvestigatingtheIFRina population. Retrospective modelling studies of influenza, as a commoncauseofglobalpandemics,havesuccessfully predicted thetruenumberofcasesand deathsfrominfluenza-like illness records and excess mortality estimates (Wong et al., 2013;
Thompsonetal.,2009).However,thesemaynotbeaccurate,in partduetothegeneraldifficultyinattributinginfluenzacasesto subsequentmortality,meaningthatCFRsmaybothoverestimate andequallyunderestimatethetruenumberofdeathsduetothe diseaseinapopulation(Spychalskietal.,2020).
The standard test for COVID-19 involves polymerase chain reaction (PCR) testing of nasopharyngeal swabs from patients suspectedofhavingcontractedthevirus.Thiscanproducesome false negatives (Anon, 2020a), with one study demonstrating almost a quarter of patients experiencing a positive result following uptotwoprevious falsenegatives(Xiaoetal.,2020).
ThesensitivityofPCRisbelievedtobearound70%,whichmaylead totheunderdiagnosisofCOVID-19(Fernández-Baratetal.,2020).
PCR is alsolimitedin thatit cannottest forpreviousinfection.
Serology testing is more invasive, requiring a blood sample.
However, it can determine whether there has been previous infectionandcanbeperformedrapidlyatthepointofcare(PoC).
SerologyPoCtestingcannotdetermineifapersonisinfectiousorif infectionisrecentandthereisariskofmisinterpretationofresults (Winter and Hegde, 2020). Generally, serology testing is more
sensitiveandspecificthanPCR,butwillstilllikelyoverestimate prevalencewhenfewpeoplehavebeeninfectedwithCOVID-19 and underestimate inpopulations withmore infections (Lisboa Bastosetal.,2020).Additionally,therehasbeengreatvariation noted in the sensitivity, the ability of the test to detect truly positivecases,ofCOVID-19serologytests(Ghaffarietal.,2020).
Serologicaltestsarereliantonseroconversion,whichinCOVID-19 occurs several days after the viral load has peaked, meaning serology is less effective in the earlier stages of the disease (Ghaffarietal.,2020).Somestudiessuggestthattherearethose whodo notseroconvert atall(Stainesetal.,2020).Thelackof reliabletestingmaybeproblematicforestimatingCFRsandIFRs.
Given the emergenceof COVID-19 asa global pandemic, it is somewhatunlikelythattheseissuesareentirelythesameforthenewer disease,buttherearelikelysimilaritiesbetweenthetwo.Someanalysis inmainstreammedia publications and pre-prints has implied that there isa largeburdenofdeathsthatremainsunattributedtoCOVID-19.
Similarly,serologicalsurveyshavedemonstratedthatthereisalarge proportionofcasesthathavenotbeencapturedinthecasenumbers reportedintheUS,Europeandpotentiallyworldwide(Bendavidetal., 2020;Erikstrupetal.,2020;Simon,2020).
Thispaperpresentsasystematicefforttocollateandaggregate thesedisparateestimatesofIFRusinganeasilyreplicablemethod.
While any meta-analysis is only as reliable as the quality of includedstudies,thiswillatleastputarealisticestimatetotheIFR givencurrentpublishedevidence.
Methods
Thisstudyusedasimplesystematicreviewprotocol.PubMed, MedLine,andMedrxivweresearchedonthe25/04/2020usingthe terms and Boolean operators: (infection fatalityrate ORifr OR seroprevalence)AND(COVID-19ORSARS-CoV-2).Thissearchwas repeated on 14/05/2020, 25/05/2020 and 16/06/2020. The pre-printserverSSRNwasalsosearchedon25/05/2020;however, asitdoesnotallowthisformat,theBooleanoperatorsandbrackets were removed. While Medrxiv and SSRN would usually be excludedfromsystematicreview,giventhatthepapersincluded
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Table1
ResultsofthesystematicreviewofpublishedresearchdataonCOVID-19infectionfatalityrates.
Study Location Studyperiod Methodandsamplesize Results
Bassett(2020) NewYork(NYC) (USA),Madrid, Lombardy
Until22ndApril2020 (commencedatenot provided)
UtilisedR0of2.4tocalculateapredictedinfection rateof81%(UKandUSA).
Overthe3regions,theIFR(usingpredictedtotal infectionrateof81%)wascalculatedat0.17%,for eachregionspecifically,usingthesamepredicted infectionrate:NYC0.22%,Lombardy0.15%and Madrid0.14%.
Bendavidetal.
(2020)
Santa-Clara Country
2days Serologicaltestingof3300localadultsand children.Volunteersampling.Bootstrap procedureusedforweightedandunweighted prevalenceestimates.
Crudeprevalencerate1.5%(95%CI1.1%–2.0%), unweightedpopulationprevalence1.2%
(bootstrap95%CI0.7%–1.8%),weighted populationprevalence2.8%(95%CI1.3%–4.7%).
Numberofinfectionsestimatedtobegreaterthan thenumberofrecordedcases.IFR0.17%.
Governmentof theCzech Republic
CzechRepublic Unspecifiedstartdate, concluded1stMay2020
Tested26,549peopleforantibodies(serology). Uncovered107newcases.
Governmentof Denmark
Denmark Reportedon20thMay2020 Tested1,071peopleoutofatotalsampleof2600 forantibodies.
Anestimated1.1%seroprevalence,witha confidenceintervalrangingfrom0.5%to1.8%.
Governmentof England (Officefor National Statistics)
England,UK Asof24thMay2020 Serologysamplesrandomlygatheredfrom885 peopleupuntil24/05/2020
6.78%(5.21%–8.64%)testedpositiveonserological testingregimen.
Government (State)of Indiana,USA
Indiana,USA 7days Tested>4600usingviralPCRandserumfor antibodies;3600randomlyselectedindividuals and900volunteers.
1.7%testedpositiveforCOVID-19onPCRplusan additional1.1%whotestedpositiveforantibodies.
EstimatedIFR0.1%.45%ofpositivecasesreportno symptoms.
Governmentof Finland
Finland Week22 RandomweeklysamplingoftheFinnish
population,week22included178samples
5positivesfrom178leadtoa2.81%positiverate, rangingfrom1.21%to6.41%.
Governmentof Slovenia
Slovenia Notspecified 1367swabsand1367bloodsamplescollected fromarepresentativesampleofthepopulation.
41people(3.1%)testedpositiveforCOVID-19 antibodies.
Governmentof Spain
Allprovincesof Spain
27/04/2020–11/05/2020 60,983participantsinvited,ofwhichsofar37,992 (62.3%)haveresponded
5%positiveonserology,withdeathratesvarying byregion.CalculatedIFRbetween1%–1.3%
Governmentof Sweden
Stockholm county
27/04/2020–3/05/2020 (week18)
1200weeklysamples.Initialanalysiswas reportedfrom1104samples.
7.3%testedpositiveonserologyinStockholm county.OfficialgovernmentreportestimatesIFR at0.6%(0.4%–1.1%)basedonmodellingand serologicaltesting.
Hallaletal.
(2020)
Brazil From14thMayto21stMay 2020
46,011attemptsleadtoatotalof25,025samples acrosseveryregionofBrazil.
Anoverallseroprevalenceof1.39%,withthe authorsreportingacalculatedIFRof1%,although itwasimpossibletoascertainwhetherthis accountedforrightcensoring.
Herzogetal.
(2020)
Belgium TwotimeperiodsinApril, withtheestimateusedin thispaperfromthe20–26 ofApril2020
Totalof7307samplesfromlocationsaround Belgium
193outof3397samplestestedpositive,witha weightedoverallseroprevalenceof3.1%.
Combinedwithdeathestimates,thisproducedan IFRof1.1%overall
Jungetal.
(2020)
Casesexported fromChinaand diagnosed outsideChina
16days Atotalof51casesdiagnosedbetween24/09/2020 and09/02/2020.Datacollectedfromgovernment websitesormediaquotinggovernment announcements.
Meantimefromillnessonsettodeathwas20.2 days.EstimatedincidenceinChinaon24/01/2020 was4718(95%CI3328–6278)andCFR5.3%(95%CI 3.5%–7.6%).IFR0.5%–0.8%.
Modietal.
(2020a, 2020b)
Italy(1688 towns)
Useddatafrom01/01/
2015–28/03/2020
UtiliseddatafromtheItalianInstituteof Statistics.Compareddeathratesduringthe COVID-19pandemictopreviousdeathratesby ageandregion.
Clearincreaseindeathswasnotedforearly2020.
IFRincreaseswithage.Range0.02%(40-49years old)to15.1%(>90yearsold).
Nishiuraetal.
(2020)
Japanese
‘evacuees’
returningto Japanfrom Wuhan
3days Atotalof565individualsscreenedforsymptoms andtestedforCOVID-19(PCR).
Atotalof8passengerstestedPCRpositivefor COVID-19(1.4%).Estimatedascertainmentrateof 9.2%.EstimatedIFR0.3%–0.6%.
Rinaldiand Paradisi (2020)
NorthernItaly (10
municipalitiesin Lombardy)
Utilised5-yeardeathdata untilApril2020
CollecteddatafromtheItalianInstituteof Statistics.Thetotalpopulationoftheincluded municipalitieswas50563.Bayesianmodelused toestimateIFR.
DeathsbetweenFebruaryandApril2020were5- foldthe2015–2019average(341versus70).IFR 1.29%(95%CI0.89–2.01),increasingto4.25%for those>60yearsold(95%CI3.01%–6.39%) Roquesetal.
(2020)
France 54days Obtaineddataonpositivecasesanddeathsfrom JohnsHopkinsUniversityCentreforSystems ScienceandEngineeringanddataontests performedfromSantéPubliqueFrance,deaths fromnursinghomeswereaddedtotheofficial count.
CalculatedIFR0.5%(95%CI0.3–0.8),whennursing homeresidentswereadjustedforestimatedIFR 0.8%(95%CI0.45–1.25).Estimatedratiobetween thoseactuallyinfectedandthoseobservedwas8 (95%CI5–12).
Rosenbergetal.
(2020)
NewYorkState, USA
9days Cross-sectionalSeroprevalencestudyof15,101 adults.UsedIgGimmunoassayapprovedfor COVID-19
12.5%ofspecimenswerereactive.Cumulative incidencewasnotedtobehigherinHispanic people,African-Americanpeopleandnon- HispanicAsianpeople.
Russeletal.
(2020)
Diamond PrincessCruise Ship
14–17days Atotalof3711passengersandstaffweretested (PCR)whilstinquarantine.Utiliseddatafromthe WorldHealthOrganisationsituationalreports.
Therewere619confirmedcases(17%),318of whomwereasymptomatic(51%).CorrectedCFR was2.6%(95%CI0.89%–6.7%).CorrectedIFRwas 1.3%(95%CI0.38%–3.6%).CFRincreasedwithage (3.6%forthoseaged60–69years,95%CI3.2–4.0) and14.8%forthose>80years,95%CI13.0–16.7).
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are not peer-reviewed, during the pandemic it has been an importantsourceofinformationandcontainsmanyofthemost recentestimatesforepidemiologicalinformationaboutCOVID-19.
Inclusioncriteriaforthestudieswere:
- RegardingCOVID-19/SARS-CoV-2(i.e.notSARS-CoV-1extrap- olations).
- PresentedanestimatedpopulationIFR(orallowedthecalcula- tionofsuchfrompubliclyavailabledata).
Titlesandabstractswerescreenedforeligibilityanddiscarded iftheydidnotmeettheinclusioncriteria.GMKthenconducteda simpleGoogleandGoogleScholarsearchusingthesametermsto assessthegreyliterature,inparticularpublishedestimatesfrom government agenciesthat maynot appear onformal academic databases.LMassessedthearticlestoensurecongruence.Ifthese mettheinclusioncriteria,theywereincludedinthesystematic review and meta-analysis. Similarly, Twitter searches were performed using similar search terms to assess the evidence available onsocial media.Estimatesfor IFRand theconfidence intervalwereextractedforeachstudy.
AllanalysisanddatatransformationwereperformedinStata 15.1.Themeta-analysiswasperformedusingthemetancommand forcontinuousestimates,withIFRandthelower/upperboundsof theconfidenceintervalasthevariablesentered.Thismodelused the DerSimonianand Laird random-effectsmethod. Themetan commandinStataautomaticallygeneratesanI2statisticthatwas used to investigate heterogeneity. Histograms were visually inspected to ensure that there was no significant positive or negativeskewtotheresultsthatwouldinvalidatethismethodol- ogy.Forthestudieswherenoconfidenceintervalwasprovided, onewascalculated.
APRISMAflowdiagramofthesearchmethods.
Sensitivityanalyseswereperformedstratifyingtheresultsinto thetypeofstudy–serologicalvsnon–bycountry,andbythe monthofcalculation.
Themetabiasandmetafunnelcommandswereusedtoexamine publicationbiasintheincludedresearch,withEgger’stestusedfor themetabiasestimation.It waschallengingtoformallyratethe riskofbiasoftheincludedmodellingstudies,astherewasvery significant heterogeneity in methodology and implementation, withtheresultthattheriskofbiasinthesestudieswasconsidered tobehighacrossallincludedresearch.Serologicalsurveyswere ratedusingtheriskofbiasintheprevalencetoolwitharesulting estimateinlinewithCochraneGRADEcriteriaoflow,moderateor high(Hoyetal.,2012).Thistoolasksaseriesof10questionsabout thesamplinganddatacollectionofprevalencestudies,withafinal ratingbasedonthepreviousquestions.Eachquestionisanswered yes/no,withalackofinformationpresumedtobeno/unclear.A separatesensitivityanalysiswasconductedusingonlyserological surveyresultsstratifiedbytheriskofbias.
Becauseofarecentsurgeinthenumberofserologicalsurveys being published, these were included in the infection fatality estimatedespitenotformallycalculatinganIFRinthestudytext itself. Regional deathrates were taken from the John Hopkins UniversityCSSEdashboard(Dongetal.,2020)10daysafterthe serosurveycompletionwherenoIFRwascalculatedtoaccountfor right-censoringoftheseestimates(GiorgiRossietal.,2020),and usedtoestimatetheIFRgiventhepopulation.
Allcodeanddatafilesareavailable(in.doand.csvformat)upon request.
Results
Initialsearchesidentified252studiesacrossalldatabases.Later searchesonGoogleand socialmedia,aswellasresamplingthe Table1(Continued)
Study Location Studyperiod Methodandsamplesize Results
Saljeetal.
(2020)
France(hospital data)and Diamond PrincessCruise Ship
Dataavailableupto7th May2020
ModellinganalysisofCOVID-19transmissionin France.Thisincluded95,210hospitalisationsand 719infectionsfromtheDiamondPrincessCruise Ship.
3.6%ofinfectedindividualswerehospitalised (95%CI2.1%-5.6%)andthispercentageincreased withageandgender(0.2%females<20years;
45.9%males>80years).IFR0.7%(95%CI0.4%–
1.0%)witharange0.001–10.1%,increasingwith age.
Shakibaetal.
(2020)
Iran 1month Clusterrandomisedsamplingutilisedtoobtain 551rapidantibodytestsforCOVID-19
22%antibodyseropositivity.18%(65subjects) wereasymptomatic.IFR0.08%–0.12%.
Snoecketal.
(2020)
Luxembourg Datacollection commencedon15April 2020
RecruitedvoluntaryresidentsofLuxembourg.
PerformedPCRforCOVID-19in1842participants andserologytestingin1820participants.
Lowprevalenceofcarriers(0.3%).Seroprevalence ofIgAwas11%,and2%forIgG.Of1842PCRtests, only6wereinconclusive(0.3%).Timeprevalence ofCOVID-19was0.32(95%CI0.02–0.63).
Streecketal.
(2020)
Germany 7days Asero-epidemiologicalCGPandGEP-compliant studyinatownexposedtoasuper-spreading event.UtilisedaquestionnaireandPCR/serology testing6weeksafteroutbreak.Asampleof919 individualshadevaluableinfectionstatus.
Infectionrateor15.5%(95%CI12.3%–19.0%);this was5-foldreportedcasesinthecommunity (3.1%).EstimatedIFR0.36%(95%CI0.29–0.45).
Stringhinietal.
(2020a)
Switzerland Threeserosurveysover severaltimeperiods,with thefinalresultsreportedon June2nd
Longitudinalserologicalsurvey,withan accompanyingpaperestimatinginfectionfatality rateaswell.
Infection-fatalityrateestimatedbyauthorswas 0.64(0.38%–0.98%)aftercorrectingand accountingfordemography.
Tianetal.
(2020)
Beijing,China 21days 262casesretrospectivelyenrolledand characteristicscomparedbetweensevere,mild andasymptomaticpatientsusingMann–Whitney UtestsandWilcoxontests.
Fivepatientsdiedand46wereclassifiedas severe.IFRinBeijingwaslowerthannationally;
0.9%versus2.4%(p<0.001).
Verityetal.
(2020)
MainlandChina and37countries outsideof mainlandChina
56days Age-stratifiedCFRestimateson1334cases outsidemainlandChina.Usedprevalencedata fromPCR-confirmedcasesininternational residentsrepatriatedfromChinatodetermine IFR.
Meantimefromillnessonsettodeath17.8days (95%CI16.9–19.2).CFRinChina1.38%(95%CI 1.23–1.53),increasingwithageto6.8%inthose
aged>65years(95%CI5.7%–7.2%)and13.4%in
thoseaged>80years(95%CI11.2%–15.9%).IFR 0.66%(95%CI0.39%–1.33%).
Villaetal.
(2020)
Italy 32days CollecteddatafromItaly’sCivilProtectionAgency fromeachofItaly’s20regions.
EstimatedanIFRof1.1%(95%CI0.2%–2.1%)anda CFRof12.7%.
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includeddatabasesrevealedafurther17estimatestoincludeinthe study.Thesecamefromavarietyofsources,withsomeappearing fromblogposts,otherspostedonTwitter,andsomegovernment documentsbeingfoundthroughGoogle.Therewerenoduplicates specifically,however,twopre-printshadbeenpublishedandso appearedinslightlydifferentformsinbothdatabases.Inthiscase, thepublishedstudywasusedratherthanthepre-print.Resultsare collatedinTable1.
Studieswereexcludedforavarietyofreasons.Somestudiesonly lookedatCOVID-19incidence,ratherthantheprevalenceofantibodies, and were thus considered potentially unreliable as population estimates (Gudbjartssonetal.,2020).Themostcommonreasonforexclusionwas selectionbias—manystudiesonlylookedattargetedpopulationsin theirseroprevalencedata,andthuscouldnotbeusedaspopulation estimatorsofIFR(Erikstrupetal.,2020;Doietal.,2020;Takitaetal., 2020;Jerkovicetal.,2020;Valentietal.,2020;Garcia-Basteiroetal., 2020;Fontanetetal.,2020;Thompsonetal.,2020;EdSlotandReusken, 2020).Forsomedata,itwasdifficulttodeterminethenumerator(i.e.
numberofdeaths)associatedwiththeseroprevalenceestimateorthe denominator(i.e.population)wasnotwelldefinedandthuswedidnot calculateanIFR(Silveiraetal.,2020;Bryanetal.,2020).Onestudy explicitlywarnedagainstusingitsdatatoobtainanIFR(Soodetal., 2020).Anotherstudy calculatedanIFR,butdidnotallowfor anestimate
ofconfidenceboundsandthuscouldnotbeincludedinthequantitative synthesis(Wilson,2020).
Afterscreeningtitlesandabstracts,227studieswereremoved.
ManyoftheselookedatcasefatalityestimatesordiscussedIFRasa concept and/or a model input, rather than estimatethe figure themselves.Fortypaperswereassessedforeligibilityforinclusion inthestudy, whichresultedin afinal25 tobeincludedin the qualitativesynthesis.
Studies varied widely in design, with 3 entirely modelled estimates(Nishiuraetal.,2020;Jungetal.,2020;Saljeetal.,2020), 4observationalstudies(Bendavidetal.,2020;Verityetal.,2020;
Tian et al., 2020; Russell et al., 2020), 5 pre-prints that were challenging to otherwise classify (Rinaldi and Paradisi, 2020;
Roquesetal.,2020;Villaetal.,2020;Modietal.,2020a;Streeck etal.,2020),andanumberofserologicalsurveysofvaryingtypes reportedbygovernmentagencies(Bassett,2020;Anon,2020b;IU, 2020;Snoecketal.,2020;SloveniaRO,2020;Anon,2020c;Shakiba etal.,2020;StatisticsOFN,2020;Hallaletal.,2020;InstitutSS, 2020; Folkhälsomyndigheten, 2020a; Anon, 2020e; Stringhini etal.,2020b).Forthepurposesofthisresearch,anestimatefor New York City was calculated from official statistics and the serosurvey; however, this was correlated with a published estimate(Wilson,2020)toensurevalidity.
Figure1.
142
The main result from the random effects meta-analysis is presentedinFigure1.Overall,theaggregatedestimateacrossall24 studiesindicatedanIFRof0.68%(95%CI0.53%–0.82%),or68deaths per10,000infections.Heterogeneitywasextremelyhigh,withthe overallI2exceeding99%(p<0.0001)(Figure2).
The monthlysensitivity analysis from Figure3 showed that earlierestimatesofIFRwerelower,withlaterestimatesshowinga higherfigure,althoughthisappearstohavestabilisedinMay.
Analysing bytheregion oforigin didnotappeartohavea substantialeffectonthefindings,althoughtherewasaslightly lower estimate seen in Asia. As the Middle East was only represented by onestudy, this regionwas excluded from the meta-synthesis by region.Two studies were also excluded as theydidnotpresentanIFR fora specificregion(i.e.Diamond Princess).
Of note, there was some difference in the estimates of IFR betweenestimatesbasedonserosurveysandthoseofmodelledor PCR-based estimates. The overall estimates from serosurvey studieswere0.60%(0.42%–0.77%),althoughagainwithveryhigh heterogeneity,ascanbeseeninFigure4.
Therewereinsufficientdataintheincludedresearchtoperform ameta-analysisofIFRbyage.However,qualitativelysynthesizing thedatathatwerepresentedindicatesthattheexpectedIFRbelow
theageof60yearsislikelytobereducedbyalargefactor.Thisis supportedbystudiesexaminingtheCFR,whichwerenotincluded inthequantitativesynthesisandstudiesexaminingIFRinselected populations younger than 70 years of age that demonstrate a strongage-relatedgradienttothedeathratefromCOVID-19.
Plottingthestudiesusingafunnelplotproducedsomevisual indication of publication bias, with more high estimates than would be expected; however, the Egger’s regression was not significant(p=0.74).
Riskofbias
As previously noted, all estimates obtained from modelling studies are considered tobe at a high risk of bias due to the heterogeneityanddifficultyinratingthesestudiesforaccuracy.
Afterusingtherating‘riskofbias’toolforprevalencestudies,6 studieswereconsideredtobeatalowriskofbias,4studiesata moderateriskofbias,andtheremaining5estimatesatahighrisk of bias. Thisis summarized in the tablebelow (full scoringin Supplementarymaterials)(Table2).
Ingeneral,theprimaryreasonfordown-ratingstudieswasnon- response bias, thelack of representativenessof the population sample,andalackofinformationacrossallfields.Somereports
Figure2.
143
were published withminimal information, which substantially increased the uncertainty and thus the risk of bias in these estimates.
Thesensitivityanalysisbystudyqualityresultsaregivenbelow.
Broadly,studyqualitywascorrelatedwithahigherinferredIFR, with lower-quality serosurveys reporting higher estimates of populationprevalencethan randomlysampledpopulation-wide prevalenceestimates.Restrictingtheanalysistoonlythosestudies atalowriskofbiasresultedinmodestlyreducedheterogeneity andanincreasedIFRof0.76%(0.37%–1.15%)(Figure5).
OtherestimatesofIFR
SeveralestimatesofIFRwereidentifiedbutnotincludedinthe meta-analysis as theydid not meet the inclusion criteria. The aggregated best estimate from the Centre for Evidence-Based MedicineatOxfordUniversityof0.1%–0.41%(JasonOke,2020),and the pre-printestimate reported by Grewelle and Leo of 1.04%
(0.77%–1.38%)(GrewelleandDeLeo,2020)werebothpertinent butcouldnotbeincludedbecauseofcollinearity.
Similarly,theestimateofsymptomaticIFRproducedbyBasuof 1.3% (0.6%–2.1%) (Basu, 2020) was excluded because of the
exclusion of asymptomatic cases. Using reported estimates of asymptomaticcases,thisestimatewouldlikelymatchthemeta- analyticIFR;however,thiscorrectioncouldnotbeappliedforthe estimates in this study as it couldeasily introduce bias in the results.
Discussion
AspandemicCOVID-19progresses,itisusefultousetheIFR when reportingfigures, particularlyas somecountriesbeginto engagein enhanced screeningandsurveillance,and observean increase in positive cases that are asymptomatic and/or mild enoughthattheyhavesofaravoidedtesting(Suttonetal.,2020).It has been acknowledged that COVID-19 is often spread from asymptomaticand/orverymildlysymptomaticcases–potentially upto50%ofallpatients–andthatasymptomatictransmission mayalsobepossiblewithCOVID-19(Nishiuraetal.,2020;Baietal., 2020)andtheuseofIFRwouldaidthecaptureoftheseindividuals in mortality figures. IFR modelling, calculation and figures, however,areinconsistent.
The main finding of this research is that there is very high heterogeneityamongestimatesofIFRforCOVID-19andtherefore, Figure3.
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it isdifficulttodrawasingleconclusionregardingthenumber.
Aggregatingtheresultstogetherprovidesapointestimateof0.68%
(0.53%–0.82%),butthereremainsconsiderableuncertaintyabout whether this is a reasonable figure or simply a best guess. It appearslikely,however,thatthetruepopulationIFRinmostplaces fromCOVID-19willliesomewherebetweenthelowerboundand upperboundsofthisestimate.
One reason for the very high heterogeneity is likely that different countries and regions will experience differentdeath rates due to the disease. One factor that may impact this is government response, with more prepared countries suffering lower death rates than those that have sufficient resourcesto combatalargeoutbreak(Scallyetal.,2020).Moreover,itisvery likely, given the evidence around age-related fatality, that a countrywithasignificantlyyoungerpopulationwouldseefewer deaths on averagethan one with a farolder population, given similar levels of healthcare provisions between the two. For example,Israel,witha medianageof 30years,would expecta lowerIFRthanItaly,withamuchhighermedianage(45.4years).
Someincludedstudies(RinaldiandParadisi,2020;Modietal., 2020a) compared fatality during COVID-19 pandemic with previous years’average fatality, determiningthat mortalityhas beenhigherduringthepandemicandwhilstcorrelationdoesnot
necessarilyequatetocausation,itisreasonabletolinktheevents ascausalgiventhehighCFRobservedacrosscountries.Itishighly likelyfromthedataanalysedthatIFRincreaseswithagegroup, withthoseagedover60yearsoldexperiencingthehighestIFR,in Figure4.
Table2
Riskofbiasinincludedserosurveys.
Study Overallriskofbias
NewYorkCity Moderate
Bendavidetal. High
Streecketal. Low
Spain Low
Indiana High
Shakibaetal. Moderate
Sweden(Stockholm) Moderate
Stringhinietal. Low
Wuetal. High
Snoecketal. Low
Slovenia High
CzechRepublic Low
Denmark Moderate
Hallaletal. Low
Herzogetal. Moderate
Finland Moderate
ONSEngland Moderate
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onecasecloseto15%(Modietal.,2020a).Giventheelderlyarethe mostvulnerableinsocietytoillnessandlikelytocarryahigher diseaseburdenowingtoincreasedsusceptibilityandcomorbidity (Liu et al., 2020; Rothanand Byrareddy, 2020), the lower IFRs observed in the younger populations may skew the figure somewhat. There are some reasonable estimates of fatality in younger age groups that were not included in the population estimates(Erikstrupetal.,2020;Valentietal.,2020;Thompson etal.,2020),whichimplyasubstantiallylowerrateofdeathinthe populationbelow70yearsofage.While thesestudieswerenot consideredapplicableforquantitativesynthesis,theydoimplya lowerIFRforthoseaged18–70years.Indeed,arecentlypublished estimatestratifiedinfectionfatalitybyageandfoundaverylow risk for under 50s thatincreasedexponentially withagefrom 0.0016%<50yearsto0.14%for50–64yearoldsandupto5.6%for those 65yearsandolder(Perez-Saezetal.,2020).Thishasalso beendemonstratedinapre-printmeta-analysisofage-stratified IFRthatfoundanexponentialincreaseinIFRbyage,from0.005%
forchildrento0.2%atage50,0.75%atage60,and27%forages85 andabove(Levinetal.,2020).
Whilenotincludedinthequantitativesynthesis,onepaperdid examinetheextremelowerboundofIFRofCOVID-19insituations wherethehealthcaresystemhasbeenoverwhelmed.Thisislikely to be higher than the IFR in a less problematic situation but demonstrates that the absolute minimum in such a situation
cannotbelowerthan 0.2%,andis likely muchhigher thanthis figureinmostscenariosinvolvingoverburdenedhospitals.
Ofnote,thereappearstobeadivergencebetweenestimates basedonserosurveysandthosethataremodelledorinferredfrom otherformsoftesting,withtheIFRbasedpurelyonserosurveil- lancebeing0.60%(0.43%–0.77%).Somehavearguedthatserologi- calsurveysaretheonlyproperwaytoestimateIFR,whichwould leadtotheacceptanceofthisslightlylowerIFRasthemostlikely estimate(Ioannidis,2020).However,eventheseestimatesarevery heterogeneousinquality,withsomeextremelyrobustdatasuchas that reported from the Spanish and Swedish health agencies (Anon,2020b;Folkhälsomyndigheten,2020b),andsomethathave clearandworryingflawssuchasastudyfromIranwheredeath estimatesarereportedlysubstantiallylowerthanthetruefigure (Shakibaetal.,2020).However,whentakingqualityintoaccount, andonlyanalysingthoseserosurveysthathadalowriskofbias,it isinterestingtonotethattheinferredIFRrisessubstantially to 0.76%(0.37%–1.15%).Thismaybeduetothebiasinlower-quality serosurveysbeingtowardsahigherprevalence(Soodetal.,2020), whichinturnlowerstheIFRsubstantially.
Anotherkeyissueisaccountingfordeaths.Whileofficialdeath countswereusedforallserosurveyestimates,andincludedinall modelled estimates,these counts are increasingly beingrecog- nizedasundercountsofthetruedeathfigure(Modietal.,2020a).
Published research is already estimating that, even in many Figure5.
146
wealthycountrieswithexcellentdeath-reportingsystems,more than50%ofCOVID-19deathsarelikelybeingmissed(Modietal., 2020b; Modigand Ebeling, 2020). It is not unlikely that, after correcting for excess mortality not captured in official death- reportingsystems,theIFRofCOVID-19inmostpopulationswould be substantially higher than our analysis suggests. It is also possible that the IFR of the disease will drop over time as treatments improve; however, our analysis at least does not demonstratethatthishasbeenthecaseinthefirsthalfof2020.
Conversely, there is evidence that the tests used in these serosurveys have drawbacks despite their high specificity and sensitivity.Forexample,inasymptomatic/mildcases,thetestsmay havereducedsensitivity,leadingtoabiasedoverestimationofthe IFR(Takahashietal.,2020).Arecentsystematicreviewandmeta- analysis of serological tests for COVID-19 found that even the betterserologytestswouldlikelyoverestimateprevalenceinan areawithfew casesand underestimate prevalencewhen many peoplehadalreadybeeninfected(LisboaBastosetal.,2020).In areas with a prevalence of 1%–2%, for example,the systematic review implies that a study employing an enzyme-linked immunosorbent assaytoexamineantibodieswouldproducean estimatedinfectionratealmostdoublethetrueprevalence.This would then cause the IFR to be underestimated by the same fraction.
Thereareanumberoflimitationstothisresearch.Importantly, theheterogeneityinthemeta-analysiswasveryhigh. Thismay mean that thepoint estimates are less reliablethan would be expected.Itisalsonotablethatanymeta-analysisisonlyasreliable asthedatacontainedwithin—thisresearchincludedaverybroad range ofstudiesthataddressslightlydifferentquestionswitha verywiderangeofmethodologicalrigor,andthuscannotrepresent the certainty of any kind. While modelling studies were not formallygraded,atleastonehasalreadybeencritiquedforsimple mathematical errors,andgiventhat manywerepre-prints,it is hardtoascertainiftheyhaveprovidedaccuraterepresentationsof the data. Serology studies were at a variable risk of bias, and analysingonlythehighestqualityserosurveysproducedahigher estimatethanrelyingonlowerqualitystudies.
Moreover, the quality of included serosurvey estimates was oftenquestionable.Manycountrieshaveaclearpoliticalmotiva- tiontopresentlowerestimates,makingitchallengingtoascertain whether these may have biased the reporting of results, particularly forthoseplacesthathaveonlypresentedresultsas pressreleasesthusfar.Somehavealsobeencriticizedforsampling issuesthatwouldlikelyleadtoabiasedoverestimateofpopulation infectionrates(Bendavidetal.,2020).
Accountingforright-censoringintheseestimateswas alsoa challenge. Using a 10-day cut-offfor deaths is fartoo crude a methodtocreateareliableestimate.Insomecases,thiscouldbean overestimate,duetotheseroconversionprocesstakingalmostas muchtimeasthemediantimeuntildeath.Conversely,thereisa long tail for COVID-19 deaths (Giorgi Rossi et al., 2020), and thereforeit isalmost certainthatsomeproportionof the‘true’ numberofdeathswillbemissedbyusinga10-daycut-off,biasing theestimatedIFRsdown.Thismaybewhyserosurveyestimatesat firstappeartoresultinsomewhatlowerIFRsthanmodelledand observationaldatasuggest.
Itisalsoimportanttorecognizethatthisisalivingestimate.
With new data being published every single day during this pandemic, in a wide variety of languages and in innumerable formats,itisimpossibletocollateeverysinglepieceofinformation into one document no matter how rigorous. Moreover, this aggregatedestimateisonlyascorrectasthemostrecentsearch
—thepointestimatehasnotshiftedsubstantiallybecauseofthe inclusionofnewresearch,buttheconfidenceintervalhaschanged.
It isalmostcertainthat,overthecourseofcomingmonthsand
years,theIFRwillberevisedanumberoftimes.Inparticular,itis vital thatfuture researchstratifies this estimate byage,asthis appearstobethemostsignificantfactorintheriskofdeathfrom COVID-19.
Thisresearchhasarangeofveryimportantimplications.Some countrieshave announced the aimof pursuing herd immunity with regard to COVID-19 in the absence of a vaccination. The aggregated IFR would suggest that, at a minimum, you would expect 0.45%–0.53% of a population to die before the herd immunitythresholdofthedisease(basedonR0of2.5–3(Russell etal.,2020))wasreached(Mahase,2020).Asanexample,intheUS thiswouldimplymorethan1milliondeathsatthelowerendof thescale.Evenwithalowerherdimmunitythresholdsuggestedby morerecentmodelling(Aguasetal.,2020),thiswouldimplyan unmanageablenumberofdeathstoreachthethresholdacrossa country.
Thisalso hasimplicationsfor future planning.Governments lookingtoexitlockdownsshouldbepreparedtoseearelatively highIFRwithin thepopulationthat isinfected if COVID-19re- emerges. This should inform thedecision to relax restrictions, giventhattheIFRforpeopleinfectedwithCOVID-19appearstobe not insignificant even in places with very robust healthcare systems.
Conclusions
Basedonasystematicreviewandmeta-analysisofpublished evidence on COVID-19 until July 2020, the IFR of the disease acrosspopulationsis0.68%(0.53%–0.82%).However,becauseof very high heterogeneity in the meta-analysis, it is difficult to know if thisrepresentsthe ‘true’ pointestimate. Inparticular, higherquality serosurveyswith lowerrisk of bias appearedto generatehigherIFRs.Itislikelythat,becauseofageandperhaps underlyingcomorbiditiesinthepopulation,differentplaceswill experiencedifferentIFRsduetothedisease.Giventheissueswith mortality recording, it is also likely that this represents an underestimate of thetrueIFR figure.Moreresearch lookingat age-stratifiedIFRisurgentlyneededtoinformpolicymakingon thisfront.
Authors’declarations
Theauthorsdeclare noconflictsofinterest. Nofundingwas received for this study. Apre-print versioncan befound here:
https://www.medrxiv.org/content/10.1101/
2020.05.03.20089854v1.
Noethicalapprovalwassoughtforthisstudy.
AppendixA.Supplementarydata
Supplementarymaterialrelatedtothisarticlecanbefound,inthe onlineversion,atdoi:https://doi.org/10.1016/j.ijid.2020.09.1464.
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