Oral Presentation 64th International Conference of the Wildlife Disease Association 2015

Mapping the risk and modeling the costs of emerging zoonoses (#6)

Peter Daszak 1 , Kevin J. Olival 1 , William B. Karesh 1 , Damien Joly 2 , Christine Kreuder Johnson 3 , David Finnoff 4
  1. EcoHealth Alliance, New York, NY, United States
  2. Metabiota Inc., San Francisco, CA, USA
  3. UC Davis, Davis, CA, USA
  4. University of Wyoming, Laramie, WY, USA

Emerging zoonoses are a major threat to public health, causing morbidity and mortality on a global scale, and costing billions of dollars annually.  Yet our understanding of how diseases emerge, and our capacity to predict and prevent their emergence, is rudimentary at best.  As part of the USAID-EPT-PREDICT program, we have assembled a modeling and analytics team to analyze the causes of disease emergence and spread.  Our goals are to identify the regions, sites and human-animal interfaces of highest risk for disease emergence and maximize surveillance and control programs for known and previously unknown zoonoses.  In this talk I highlight some of our recent work, including a completely revised EID hotspots risk map that identifies land use change as a significant driver of EID events from wildlife. To estimate the potential for future zoonotic disease emergence, we have analyzed viral discovery curves following repeated sampling of individual species. This gives us the first ever estimate of the likely number of unknown viral pathogens of zoonotic potential in mammals – the ‘zoonotic pool’.  To identify which wildlife are most likely to harbor potential zoonoses, we conducted the first ever analysis of all viral-host relationships in mammals, corrected for reporting bias.  This work also sheds light on whether bats are more ‘special’ as a zoonotic virus reservoir than other species. To generate local scale risk maps, we have conducted human risk behavior analysis, wildlife sampling and viral discovery in three Tropical forest systems. This “DEEP FOREST” project provides a strategy to understand where disease emergence is most likely to occur in a landscape and where funds would be best spent on surveillance and control. Finally, we analyzed the cost of pandemics, and their control, and show that in the long term, prevention of disease emergence through programs like PREDICT is more cost effective than controlling diseases after they have emerged. This is borne out by our analysis of the economics of the West African Ebola outbreak, and what this signifies for future pandemic prevention programs.