Pre-empting the emergence of zoonoses by understanding their socio-ecology

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

Pre-empting the emergence of zoonoses by understanding their socio-ecology

Pre-empting the emergence of zoonoses by understanding their socio-ecology

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Pre-empting the emergence of zoonoses by understanding their socio-ecology