Keynote presentation by Dr Peter Daqszak, President, EcoHealth Alliance, at the One Health for the Real World: zoonoses, ecosystems and wellbeing symposium, London 17-18 March 2016

1. Peter Daszak
EcoHealth Alliance, New York, USA
www.ecohealthalliance.org
Pre-empting the emergence of zoonoses by understanding their
socio-ecology

2. Economic Impact of Emerging Diseases

3. Temporal patterns in EID events
Jones et al. 2008
• EID events have increased over
time, correcting for reporter bias
(GLMP,JID F = 86.4, p <0.001,
d.f.=57)
• ~5 new EIDs each year
• ~3 new Zoonoses each year
• Zoonotic EIDs from wildlife
reach highest proportion in
recent decade

4. Optimal Stopping Problem
Pike et al. PNAS 2014

5. Simulation results
Critical Damage Level, D*, Mean EID Event Trigger, Z*, Expected First-Passage Time, t*,
option value, OV, and Expected Net Present Cost
Conclusion: Mitigating pandemics is cost-effective (10-fold ROI), but we need
to act rapidly (34 yrs) to reduce underlying drivers of spillover and spread.
Policy option A Policy option B Policy option C Policy option D
𝑚2 = 2.8857 𝑚2 = 2.5651 𝑚2 = 1.9238 𝑚2 = 0.8016
K = $56.3B K = $112.5B K = $225.0B K = $562.5B
D* $17.1B $20.0B $25.7B $47.6B
Z* 237.74 252.61 276.90 336.64
t* 3 8 15 34
OV $98.1B $156.2 $215.2 $215.1
E* $808.7 $790.0 $712.4 $743.4
Pike et al. PNAS 2014

7. Emerging Zoonotic Disease Hotspots I
Jones et al. 2008, Nature
• ~3 new zoonoses/year
• Corrected for sampling bias
(research effort)
• Two variables predicted
zoonotic disease emergence
from wildlife:
• Human population density
• Mammal species diversity

8. Pre-empt or combat, at their source, the
first stage of emergence of zoonotic
diseases

9. Which species will the next pandemic emerge from?

11. VIRUS-INDEPENDENT
TRAITS
RISK OF SPILLOVERVIRUS-SPECIFIC TRAITS
+ =
Geographic
hotspots for
emergence
Host species traits,
geographic range,
relatedness
Epidemiological/
contact interface
Viral prevalence in
host
Host abundance Circoviridae
Unassigned
Papillomaviridae
Astroviridae
Coronaviridae
Adenoviridae
Polyomaviridae
Caliciviridae
Hepadnaviridae
Parvoviridae
Arteriviridae
Herpesviridae
Asfarviridae
Retroviridae
Picornaviridae
Paramyxoviridae
Rhabdoviridae
Poxviridae
Reoviridae
Arenaviridae
Filoviridae
Flaviviridae
Orthomyxoviridae
Bunyaviridae
Togaviridae
Anelloviridae
Hepeviridae
Picobirnaviridae
Bornaviridae
0.00.20.40.60.81.0
Phylogenetic Breadth by Viral Family
MeanPhylogeneticBreadthofHosts
Host breadth/plasticity
Ranking risk for zoonotic potential of novel viruses
Arteriviridae
Asfarviridae
Circoviridae
Herpesviridae
Astroviridae
Caliciviridae
Adenoviridae
Coronaviridae
Hepadnaviridae
Parvoviridae
Retroviridae
Reoviridae
Polyomaviridae
Rhabdoviridae
Arenaviridae
Poxviridae
Flaviviridae
Unassigned
Paramyxoviridae
Bunyaviridae
Papillomaviridae
Picornaviridae
Togaviridae
Anelloviridae
Bornaviridae
Filoviridae
Hepeviridae
Orthomyxoviridae
Picobirnaviridae
Total number of human and non−human viruses by viral family (all detection)
NumberofKnownViruses
0
20
40
60
80 Infects Humans
Not Known to Infect Humans
Proportion known
zoonoses in virus
family
Phylogenetic
relatedness to
known zoonoses
Other virus-specific
traits

14. SARS-like CoV locate within SARS cluster
P1b
S

15. Li et al. (2005) Science 310: 676-679

16. Ge et al. (2013) Nature
Human SARS CoV Tor2
Human SARS CoV BJ01
Human SARS CoV GZ02
Civet SARS CoV SZ3
Bat SL-CoV Rs_4087-1
Bat SL-CoV Rs_4110
Bat SL-CoV Rs_4090
Bat SL-CoV Rs_4079
Bat SL-CoV Rs_3367
Bat SL-CoV Rs_4105
Bat SL-CoV Rs_SHC014
Bat SL-CoV Rs_4084
Bat SL-CoV Rs_3267-1
Bat SL-CoV Rs_3262-1
Bat SL-CoV Rs_3369
Bat SL-CoV Rf1
Bat SL-CoV Rs_4075
Bat SL-CoV Rs_4092
Bat SL-CoV Rs_4085
Bat SL-CoV Rs_3262-2
Bat SL-CoV Rs_3267-2
Bat SL-CoV HKU3-1
Bat SL-CoV Rm1
Bat SL-CoV Rp3
Bat SL-CoV Rs_4108
Bat SL-CoV Rs672
Bat SL-CoV Rs_4081
Bat SL-CoV Rs_4096
Bat SL-CoV Rs_4087-2
Bat Sl-CoV Rs_4097
Bat SL-CoV Rs_4080
Bat SARS-related CoV BM48
Bat CoV HKU9-1
68
99
94
85
98
80
92
64
97
99
51
95
86
*
*

17. 0
1
2
3
4
5
0 200 400 600 800 1000 1200 1400 1600 1800
Discovery Curve for CoV in Pteropus Bats
(Bangladesh)
I
II
III
IV
Coronavirus
Positive = 3.7%

18. Anthony et al. mBio 2013
• ~58 unknown viruses in Pteropus giganteus
• ~320,000 unknown viruses in all mammals; ~72,152 in the 1,244 known bat species
• One-off cost to identify 100% = $6.8 Billion
• One-off cost to identify 85% = $1.4 Billion ($140 million p.a. over 10 yrs)
• Cost of SARS = $10-50 Billion
• ~250 bat viruses in last 5 years, 530 total = 7% of the estimated #

20. relative
influence
(%)
std.
dev.
population 27.99 2.99
mammal
diversity
19.84 3.30
change: pop 13.54 1.54
change: pasture 11.71 1.30
urban extent 9.77 1.62
crop
crop_change
past
urban_land
past_change
pop_change
mamdiv
pop
0 10 20
rel.inf.mean
variable
Hotspots II – new variables and methods

21. Hotspots II – influence of each variable
Allen et al., in prep

23. Human-Camel Interface
Photo: K.J. Olival
http://www.efratnakash.com

24. • Modeled distribution of
MERS-CoV bats
• Camel production (FAO)
• Modeled risk of MERS
spillover (horn of Africa)
Where did MERS originate?

25. Unidentified MERS-CoV cases:

26. USAID PREDICT-2
Africa
Cameroon
Gabon
DR Congo
Republic of Congo
Rwanda
Tanzania
Uganda
Liberia
Guinea
Sierra Leone
Kenya
Ethiopia
Egypt
Jordan
Sudan & S. Sudan
Asia
Bangladesh
Cambodia
China
Lao PDR
N. India
Indonesia
Nepal
Malaysia
Myanmar
Thailand
Vietnam
Geographic Focus

27. • Linking specific behaviors and practices with evidence of
spillover
– Identify relationships between exposure and outcome
– What are the mechanisms of spillover transmission
• Understand the communities and context within which
risk occurs
– What are the circumstances that increase or decrease risk
PREDICT 2: Behavioral Research

28. Observational Research
– Introduction of research to target community
– Identify individuals of power and influence/barriers to access
– Evaluation of settings of possible disease transmission from animals to humans
– Does not require IRB approval to conduct

29. Focus group discussions
 Carefully planned and guided discussion
 Captures ideas that people agree on in public
 Targets ‘experts’ with regular animal contact
 Requires IRB approval and informed consent

30. Ethnographic interviews
– One-to-one semi-structured interviews
– Learn about daily and household life
– Assess privately held beliefs and experiences
– Requires IRB approval and informed consent

31. Uganda Malaysia Brazil
DEEP FOREST
Pristine Intermediate Urban
• Systematic animal sampling
• Broad viral screening
• Human behavioral data collection

32. Mapping human-animal contact from behavioral surveys
A) Raw Landscape Development
Intensity (LDI) Index
(0=pristine, 1=highly disturbed)
A) Reclassified LDI (P=Pristine,
I=Intermediate, D=disturbed)
A) Percentage of respondents
reporting wildlife consumption
B) Relative human-animal contact
rate
Brazil Malaysia Uganda

33. Wildlife Trade in China

34. Presumed medicinal properties
• Reduces blood viscosity
• Anti-inflammatory

35. Questionnaire Implementation
Field Investigation Phase

36. oropharyngeal swab sample collection
Field Investigation Phase

37. Field Investigation Phase
Blood sample collection

41. Valuing Ecosystem Services
If we convert forest, diseases emerge.
These diseases cost billions of dollars
annually.
Can we include these costs in decision
making to reduce deforestation?

43. Conversion: Benefits
Benefits
• Meet global demand for
goods and services
• Generate household
income
• Regional/national
economic growth

44. Costs
• Converting land costs money
- clearing forest
- cultivating field
• Maintaining land productivity
- fertilizer
- irrigation
• Lost ecosystem benefits
- Abiotic and biotic services
- Naturally derived products
- Exposure to disease
Conversion: Costs

45. How much to convert?
0
benefit of
converting a little
more land
cost of converting
a little more land
no
difference
between
benefits
and costs

46. deforestation and malaria in Sabah, MY
Numberofmalariacases
Deforested area in Sabah – Malaysia (red) and the number of cases of Malaria in blue
(2001 – 2013) (Zambrana-Torrelio unpub. data)
Similar trends observed in Brazil (Olson et al. 2010) and Indonesia (Garg 2015)

47. Simulations: Sabah
Model: No health impact of conversion
Model: With health impact of conversion
Conversion of Sabah
1980 1990 2000 2010 2020 2030 2040 2050 2060
0
0.05
0.10
0.15
0.20
0.25
0.30
Year
ProportionofSabahconverted
ES
ES + H

48. Collaborators
• 100+ partners in 24 countries
• Wuhan Inst. Virol.: Zhengli Shi, Ben Hu, Xingye Ge
• Yunnan CDC: Yunzhe Zhang
• Wuhan CDC: Shiyue Li
• Columbia Univ. (Ian Lipkin, Simon Anthony)
• UC Davis, Metabiota, WCS, Smithsonian
• Universiti Malaysia Sabah, Sabah Wildlife Dept.
Funders