Ending Pandemics Iniave

EndingPandemicsIni.a.ve

AdamW.Crawley,MPHISDSWebinar–January22,2016

SkollGlobalThreats–EndingPandemics•  OurApproach•  FasterDetec0onandRepor0ngThroughNovel

SurveillanceSystems•  Verifica0onThroughCrowdsourcedEpidemicIntelligence•  Coordina0onThroughRegionalDiseaseSurveillance

Networks•  AssessingProgressThroughMeasurement

SGTFApproachforEndingPandemics

•  Ourvisionistoendpandemics.Weworktodecreasethelikelihoodofapandemiceventoccurringbyshorteningthe0mefromoutbreakstarttodetec0on,withafocusondiseasehotspots,whereviruseswithpandemicpoten0alarelikelytoemerge.

•  Weaimtoachievethisbyenablingpartners,throughtheuseofinnova0veapproaches,to:

•  Quicklycaptureandshareinforma0ontodetecthuman&animaloutbreaks;•  Trainandlinkfieldepidemiologiststoverifyoutbreaksfaster;and•  Supportandconnectregionaldiseasesurveillancesystemstoencouragecollabora0on

betweenna0onalpublichealthsystems.

•  Ourhypothesisisthatindividualprojects,leveragingnewtechnologiesandinnova0veapproaches,willspreadbeyondpilotcountriesthroughregionalsurveillancenetworksandotherconvenings.

1. We are measuring global baseline in more detail to determine best impact measure

Tradi.onalpublichealthsystemsrequirerepor.ngoutbreaksthroughmul.plelevelsofhealthauthori.es

Patient Zero •  Patient

experiences symptoms

Frontline healthcare workers

•  Identify unusual events

Local officials and field epidemiologists

•  Gather data •  Identify issue and

submit report •  Confirm occurrence

of outbreak

Labs •  Test samples •  Verify cause of

outbreak (e.g. virus strain)

Ministry of Health •  National response activated •  Inform World Health Organization

Potential outbreak reported

Patient visits healthcare provider

Outbreak confirm

ed Send samples

to lab

Lag time between outbreak and official detection enables virus to

spread

Paralleltothehuman-centeredpublichealthsystemisarepor.ngmechanismforanimalhealth

Patient Zero Local health officials

Labs

Sick animals Local Veterinarian Labs

Local healthcare workers

Local food/ agriculture officials

Ministry of Health, WHO

Ministry of Agriculture, FAO, OEI

Human health

Animal health

The "One Health" concept integrates the

human-animal-environment nexus in disease detection

CurrentSGTFworktodecreasedetec.on.me

Note SGTF is currently evaluating opportunities for Point of Care diagnostics for verification.

Connect national & regional

surveillance systems by

building networks

Patient Zero Local health officials

Labs

Sick animals Local Veterinarian Labs

Local healthcare workers

Local food/ agriculture officials

Ministry of Health

Ministry of

Agriculture

Detect outbreaks at the patient level by

leveraging technology

Detect outbreaks in animal populations by

leveraging technology

Verify cases faster by training and linking

field epidemiologists 1 2

3

Epidemiccurve

Rou0nediseaserepor0ng

TIME

CASES

7

Epidemiccurve

Rou0nediseaserepor0ng

Clinicianreports

TIME

CASES

8

Epidemiccurve

Rou0nediseaserepor0ng

Clinicianreports

Sen0nelnetworks

TIME

CASES

9

Epidemiccurve

Rou0nediseaserepor0ng

Clinicianreports

Sen0nelnetworks

Digitaldiseasedetec0on

TIME

CASES

10

Epidemiccurve

Rou0nediseaserepor0ng

Clinicianreports

Sen0nelnetworks

Digitaldiseasedetec0on

Par0cipatoryepidemiology

TIME

CASES

11

Epidemiccurve

Rou0nediseaserepor0ng

Clinicianreports

Sen0nelnetworks

Digitaldiseasedetec0on

Par0cipatoryepidemiology

TIME

CASES

Evenearlier:diseasedetec0oninanimals12

Fondation Mérieux, in partnership with the Ministry of Health of Senegal and PATH, is building a scalable mobile phone and web platform to transfer epidemiological data from 120 medical laboratories across Senegal to the Ministry of Health (MoH) to shorten the time between detection and response to reduce biothreats and stop pandemics.

FasterLaboratoryRepor.ngUsingMobile

Digital,informalsurveillancesystemsaregenera0nganincreasingamountofsignalsEpiCorewilllinkvolunteerepidemiologiststovalidateearlysignalsofdiseaseoutbreaksFieldEpidemiologyTrainingProgramsin55countrieswillhaveaccesstotrainingindigitaldiseasedetec0onmethods

PARTICIPATORYSURVEILLANCE

TRADITIONAL PARTICIPATORY

SPEED

SCALABILITY

SENSITIVITY

SPECIFICITY

CREDIBILITY

PARTICIPATORYSURVEILLANCE

InfluenzanetBelgiumDenmarkFranceItaly

NetherlandsPortugal

SpainSwedenUnited

Kingdom

FluNearYouCanada

UnitedStates

GuardiansofHealthBrazil

SaudenaCopa2014WorldCup

SACIDSKenya

Tanzania

DoctorMe&PODDThailand

FluTrackingAustralia

SaludBoricuaPuertoRico

MassGatheringPar.cipatorySurveillanceinBrazil

World Cup 2014 & Olympics 2016

BrazilMinistryofHealthhaschampionedtheWorldCuppilottoengagefanstoreportsymptomsofillness

SGTFisleadingdiscussionswiththeInterna.onalOlympicsCommi\eetobuild

onthissuccessforthe2016gamesinRio

MassGatheringPar.cipatorySurveillanceinBrazil

CrossBorderPar.cipatorySurveillanceinAfrica

Complemen.ngtheinterna.onaldiseasesurveillancestrategieswith:–  Par.cipatoryengagementoflocalcommuni.es.–  Applica.onofmobiletechnologiesfordiseasesurveillance;repor.ng

includingeventrepor.ng&feedback–  Inter-sectoralcollabora.on:AnimalHealth,HumanHealth&ICTsectors

(EpiHack)

Thailand:Opendream&TheDoctorMeApplica.on

•  SEAsiaisahotspotforemergingdiseases:•  AvianInfluenza,SARS,Dengue

•  Thailandboastsarapidlyemergingtechnologysector:

•  Mobilephonepenetra0on:131.8%ofpopula0on

•  Internetaccess:35.8%ofpopula0on

•  In2011Opendream,ThaiHealthPromo0onFounda0onandFolkDoctorFounda0onlaunchDoctorMemobileapplica0on:

•  400,000+downloads•  35,000ac0vemonthlyusers

!

TheChiangMaigrantfurtherextendsourinnova.onsinpar.cipatoryepidemiology

•  Establishhuman/animalpar0cipatoryepidemiologyapproachesinChiangMaiwithlocalpartners

•  Detect both human and animal illnesses in real time, before an epidemic occurs

•  Empower human and animal health officials with more timely data to respond faster and contain outbreaks

•  Scale successes beyond Chiang Mai •  Expand in Thailand through national health

organizations and village governments •  Scale successes to additional countries using

the Mekong Basin Disease Surveillance network and CORDS

•  Engage regional philanthropies and corporate partners to support expansion

Focus area: Chiang Mai Province in

Thailand

1

2

3

4

Project Goals

Smolinskietal.(2015)AmericanJournalofPublicHealth

FluSeason2014-2015NumberofPar.cipantsbyState

25

30

35

40

45

50

-120 -100 -80long

lat

Number of Participants

1-500

500-1000

1000-1500

1500-2000

>2000

TotalnumberofPar0cipants=49,814

FluSeason2014-2015NumberofPar.cipantsbyState–AdjustedforPopula.on

TotalnumberofPar0cipants=49,814

25

30

35

40

45

50

-120 -100 -80long

lat

Percent of Population

0.005 - 0.01

0.01 - 0.015

0.015 - 0.02

0.02 - 0.025

>0.025

Gender

Chi-SquareTest:χ2=1946.8,df=1,p<0.00001*Alargerpropor0onoffemalespar0cipatedtothestudy,withrespecttothebaselinevalue

0.0

0.2

0.4

0.6

Female MaleGender

Rel

ativ

e Fr

eque

ncie

spopulation

FNY

US

Distribu.onofAgeGroups

Chi-SquareTest:χ2=13048.75,df=7,p<0.00001*Allageclasseswererepresentedinthesample,however,asignificantdifferencebetweentherepar00oninageofac0vepar0cipantsandU.S.popula0onisobserved

0.00

0.05

0.10

0.15

0.20

<15 15-29 30-39 40-49 50-59 60-69 70-79 80<Age Group

Rel

ativ

e Fr

eque

ncie

s

population

FNY

US

HealthDevelopmentIndexasSESProxy(bycounty)

Two-sampleKolmogorov-SmirnovTest:D=0.6304p<0.0001*Thedistribu0onsofHDIissignificantlydifferentforFNYpar0cipantsandUSpopula0on

0 2 4 6 8 10

0.0

0.2

0.4

Density of HDI

HDI

Density US population

FNY participants

Characteris.csAssessed

Variable Descrip.on

Gender Male;Female

AgeGroup 13-29;30-39;40-49;50-59;60-69;70-79

ILIStatusatFirstSurvey ReportedILIasdefinedbyCDC(feverwithcoughand/orsorethroat)1atfirstsurvey;didnotreportILIatfirstsurvey

HouseholdMembers Reportedforatleastoneotherhouseholdmember;didnotreportforotherhouseholdmembers

HealthDevelopmentIndex Con0nuousScale1-10,1indica0nglowSES,10indica0nghighSES

SummaryofResults

Variable OR(p-value)

ILIStatusat1stSurvey(yes) 0.22(<0.0001)

HouseholdMembers(yes) 3.29(<0.0001)

HealthDevelopmentIndex 1.12(<0.0001)

Gender(Females) 0.75(<0.0001)

AgeGroup(70-80) 1.23(<0.0001)

(60-70) 1.14(0.0001)

(40-50) 0.70(<0.0001)

(30-40) 0.54(<0.0001)

(13-30) 0.67(<0.0001)

13-30

30-40

40-50

60-70

70-80

Gender

HDI

House

ILI

1 2 3Odds Ratio

Ques.ons?

[email protected]

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