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PREDICT: Emerging Pandemic Threats Program

New Initiatives in

Surveillance: The USAID

EPT “PREDICT” Project

Stephen S. Morse Co-Director, PREDICT

Columbia University & University of California, Davis

IOM Symposium on “Emerging Infections,

Microbial Threats to Health, and the Microbiome” December 12, 2012


GLOBAL SURVEILLANCE for early

warning and anticipation:

• The primary recommendation of

every expert group for the last 20

years

• Including the IOM reports


Some progress in recent years

• ProMED

• GPHIN, HealthMap

• WHO GOARN, GLEWS

• WHO Revised IHR(2005)

• FAO/OIE/WHO Tripartite collaborations

• CDC: FETP, Global Disease Detection (GDD)

• DoD AFHSC/GEIS, DTRA


Zoonoses in disease

emergence

• 1407 human pathogens

• 58% are zoonotic

• 130 of the 177 recently emerged

pathogens are zoonotic (RR=2.0)

-- Woolhouse ME, Gowtage-Sequeria S. Host range

and emerging and reemerging pathogens. Emerg

Infect Dis 2005; 11(12): 1842-7.

Courtesy Dr. Larry Madoff

EPT Program

USAID EPT grantees

PREDICT

Univ. California, Davis; EcoHealth Alliance; Wildlife Conservation

Society; Metabiota/Global Viral (formerly Global Viral Forecasting,

GVF); Smithsonian Institution

RESPOND

Development Alternatives Inc.; Tufts Univ.; Univ. of Minnesota;

Training Resources Group; Ecology & Environment

PREVENT

FHI 360; GV

IDENTIFY

World Health Organization; Food and Agriculture Organization; World

Organization for Animal Health

Pathogen

Detection,

Risk (biological)

Determination

Outbreak

response

capacity

Risk (behavioral)

Determination,

Risk Reduction

Laboratories


Pre-empt or combat, at their source,

the first stages of the emergence of

zoonotic diseases that pose a

significant threat to public health

(potentially pandemic infections)

The Challenge


Goal

Developing global capacity to anticipate

and prevent emerging zoonotic

diseases from wildlife pathogens

DAY

CA

SE

S

Current Outbreak Detection and Response

Adapted from J. Davis, Climate Adaptation Workshop, Nov. 2003

First

Case Detection/

Reporting

Lab

Confirmation

Response

Opportunity

for control

DAY

CA

SE

S

First Case

Detection/

Reporting

Lab Confirmation

Response

Effective Health Early Warning

Surveillance,

Observations

and

Monitoring

Information

Adapted from J. Davis, Climate Adaptation Workshop, Nov. 2003

Opportunity

for control


PREDICT Surveillance

Strategy

• Targeting key interfaces and species

• Modeling to help target surveillance

• An iterative and adaptive process

– Learning and adapting from results and

experience

Developing a Targeted Surveillance Strategy in high risk locations for emergence

Wildlife_nologos.kmz


Emerging Infections “Hotspots”:

Where Will They be in Future?

Zoonoses from wildlife From: Jones et al., Nature 451:990-994 (21 February 2008)


Digitized Risks and Interfaces

Activities of Interest:

• Hunting

• Markets/trade

• Wildlife/livestock conflict

• Morbidity/mortality events

• Free-ranging – undisturbed

• Logging/deforestation

• Water restriction

PREDICT Surveillance

PREDICT: Emerging Pandemic Threats Program Photo: Dr. Jonna Mazet (UC Davis)


Courtesy Dr. William Karesh

(market in Jakarta, Indonesia)

Developing a Targeted Surveillance Strategy responsive to potential pathogen emergence

• Surveillance of sick or dead wild animals – Using mobile phone

technology to rapidly detect symptomatic animals & evaluate spillover

• Monitor for human & livestock outbreaks of zoonotic disease – Sample wildlife in

proximity to affected communities to determine source

• Primates

• Bats

• Rodents

• Birds

• Suids

• Carnivores

• Ungulates

Taxonomic Groups

Developing a Targeted Surveillance Strategy for wildlife species of highest risk

Known viruses, corrected for number of species & sampling effort per Order

0 10 20 30 40

ARTIODACTYLA

CARNIVORA

CHIROPTERA

LAGOMORPHA

NON-HUMAN PRIMATES

PERISSODACTYLA

RODENTIA

viruses/(dispubs/sp)

yes Blood, swabs, urine, feces, tissues

pos

Refine with specific primers or sequencing Advanced pathogen discovery

Specimen type

Targeted screening for different wildlife taxa

primates bats rodents human birds

Fam

ily le

vel p

rim

ers

Retro, Filo, Flavi, Orthomyxo, Paramyxo, Pox,Corona, Arena

Flavi, Corona, Henipa, Rhabdo, Arena, Filo, Reo

Arena, Hanta, Pox, Alpha, Reo

Syndromic testing

Orthomyxo, Paramyxo, Flavi

Follow-up on-the-ground field investigations develop diagnostics

screen local human cases screen local wildlife

Spe

cifi

c vi

ral,

bac

teri

al t

est

ing

12 PCR protocols developed and going out to countries

PREDICT Surveillance Highlights

Trained >1,500 field personnel, veterinarians,

laboratory technicians, public health workers and

ministry officials from 20 countries

Building capacity to test for viral families in 33 labs

Collected samples from ~35,000 animals (bats,

rodents, birds, carnivores, primates, and ungulates)

Discovered more than 200 novel viruses in wildlife:

corona, boca, herpes, retro, adeno, rhabdo

Documented human pathogens in wildlife and animal-

origin pathogens in humans

Lab methodology (family level PCR) saves 40-90% of

costs, rapid turnaround

CIRMF Lab - Gabon

Metabiota

Developing a Targeted Surveillance Strategy using global information real-time


Surveillance Enabled

by New Technologies

• Pathogen diagnostics and discovery

– Molecular identification

– Both known and unknown agents

• Reporting

– Internet

– Mobile phones

• Informatics, modeling


Photo: Dr. Jonna Mazet (UC Davis)

Percentage of the World's

Population Covered by a Mobile

Cellular Signal,

2003 vs. 2009

Source: ITU World Telecommunication/ICT Indicators database


The good news:

New technologies in diagnostics

and communications have

revolutionized ability to identify

and report infections

We have gone from a paucity of

data to a flood of data


But with this comes a

dilemma:

New technologies have

revolutionized ability for

diagnosis and surveillance

But how do we separate the

signal from the noise, to identify

which pathogens to focus on?


Pre-Empting Infections

at Their Source

• Need to develop a risk

assessment framework

• Some elements exist, but still

rudimentary


SOME PREDICTIONS

SEEM EASY …

• Human exposure (suitable interface) essential

• Changing demographics • Aging population with additional susceptible people

• Increased urbanization: higher density, pathogen

exchange

• Globalization

• More new interfaces • Increasing food demand

• Increasing changes in land use, allowing more opportunities

for exposure to previously unfamiliar species and pathogens

• Likely result: Increase in emerging infections


Host Jumping: What Determines Host

Range?

• No simple answer, likely varies with the

pathogen and the host interaction

For example:

• Flu: Receptor binding necessary but not

sufficient

• Coronaviruses: Receptor binding seems

sufficient

• HIV: Requires co-receptor (chemokine

receptor)


Predicting Pandemic Potential:

Host-Pathogen Interactions

• Host-receptor interactions: Necessary but not always sufficient – Changing receptor affinity (from avian to mammalian-type)

was the first step in the recent laboratory work adapting H5N1 avian influenza virus to mammalian transmission

– But other mutations were also required

– However, highly host-specific receptor structures may provide a significant barrier to infection of a new host

• Why are some viruses relatively benign in their natural hosts, but induce hyperinflammatory responses in a new host? (E.g., Ebola or Nipah viruses in humans) – Research to test in cell cultures, humanized mice



Requirements for Human Infection

• Relative importance (or “success rate”) of

host relatedness versus contact frequency:

– e.g., HIV-1 (from chimpanzees), vs. SARS (from bats and civets), Nipah (from bats and pigs), or influenza (from pigs and birds)

• Is an animal virus that is more closely related to a known human pathogen more likely to infect people?

– Paramyxoviruses possible examples

– But most viral families’ potential recognized only after the fact



Pathogen Factors

• Association between emergence and a broad host

range?

• Estimates of viral ‘evolvability’: Role of high mutability

in pathogen success?

• Patterns of host-virus co-evolution. Relationships

can be assessed by genetic sequence comparisons

– Strong patterns of co-evolution over recent evolutionary

time suggest stable long term interactions

• Is history destiny? Require better understanding

of the pathogen’s history


Transmissibility

• Essential for pathogen success

• Possible relation to virulence

• Genetics and evolution of transmissibility

poorly understood

• In many cases, even mechanisms of

transmission poorly understood

• Human behavior very important factor


Crossing the Animal-Human Interface

• To evaluate public health importance,

need the other side of the story: identifying

which of these animal microbes infect

humans

• What mechanisms promote or inhibit

transfer across interfaces?

• Commonalities: What do the successful

pathogens have in common?


“Pathogen discovery” is

only one part of PREDICT

Other key objectives include:

• Surveillance: Sampling, testing, data

collection

• Building capacity for surveillance in

developing countries

• Better understanding the biology and

ecology of emergence and microbial

background


It’s Not Only Technical Challenges

Besides continuing improvement in

technology and surveillance coverage:

• Sustaining capacity

• Political will

• Avoiding complacency

• Maintaining resources


THANK YOU …

Sincere thanks to all our colleagues

USAID and the PREDICT team

• Drs. Dennis Carroll, Murray Trostle, Andrew Clements, Alisa

Pereira, Rob Henry, August Pabst, Julian Brown, USAID EPT

mission liaisons

• Dr. Jonna A.K. Mazet (Co-Director & PI, University of

California, Davis); Drs. Peter Daszak, William Karesh, and

colleagues at EcoHealth Alliance; Dr. Damien Joly and

colleagues at WCS; Drs. Nathan Wolfe, Joseph Fair and

colleagues at Global Viral/Metabiota; Drs. Suzan Murray, Chris

Whittier, and colleagues at SI; PREDICT Country

Coordinators; and many others

• Drs. Mark Woolhouse, Colin Parrish (Cornell), Ralph Baric

(UNC), Ian Lipkin, and Mark Buller (St. Louis University)

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