Ecological origins of novel human pathogens.
ABSTRACT A systematic literature survey suggests that there are 1399 species of human pathogen. Of these, 87 were first reported in humans in the years since 1980. The new species are disproportionately viruses, have a global distribution, and are mostly associated with animal reservoirs. Their emergence is often driven by ecological changes, especially with how human populations interact with animal reservoirs. Here, we review the process of pathogen emergence over both ecological and evolutionary time scales by reference to the "pathogen pyramid." We also consider the public health implications of the continuing emergence of new pathogens, focusing on the importance of international surveillance.
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ABSTRACT: Many human viral infections have a zoonotic, i.e., wild or domestic animal, origin. Several zoonotic viruses are transmitted to humans directly via contact with an animal or indirectly via exposure to the urine or feces of infected animals or the bite of a bloodsucking arthropod. If a virus is able to adapt and replicate in its new human host, human-to-human transmissions may occur, possibly resulting in an epidemic, such as the A/H1N1 flu pandemic in 2009. Thus, predicting emerging zoonotic infections is an important challenge for public health officials in the coming decades. The recent development of viral metagenomics, i.e., the characterization of the complete viral diversity isolated from an organism or an environment using high-throughput sequencing technologies, is promising for the surveillance of such diseases and can be accomplished by analyzing the viromes of selected animals and arthropods that are closely in contact with humans. In this review, we summarize our current knowledge of viral diversity within such animals (in particular blood-feeding arthropods, wildlife and domestic animals) using metagenomics and present its possible future application for the surveillance of zoonotic and arboviral diseases.International Journal of Molecular Sciences 06/2014; · 2.34 Impact Factor
Conference Paper: Predicting bat reservoirs of zoonotic infectious diseases[Show abstract] [Hide abstract]
ABSTRACT: Background/Question/Methods Bats have long been known to harbor infectious diseases harmful to humans, but have been increasingly linked with novel emerging infections such as those caused by the SARS coronavirus, Nipah, and Ebola virus. Recent work has postulated that feeding and ranging habits may contribute to high probability of human transmission (e.g., rabies), and that aspects of bat social behavior and physiology may make this group ideally suited to be zoonotic reservoirs. In this analysis, we apply machine learning methods to examine all 1116 species of extant bats and over 50 variables describing their life history, physiology, and ecology to investigate whether there are common traits shared among species that carry the greatest diversity of zoonotic diseases. We also identify which particular species of bats have the highest probability of carrying a zoonotic pathogen. Results/Conclusions Results suggest that larger size at birth size and larger size at weaning are the most important predictors of bat species carrying many zoonotic infections. These traits are most common in bats that are long lived and have multiple litters per year. Our results also suggest that environmental factors such as mean AET that are characteristic of highly vegetated habitats are important predictors of zoonotic bat reservoirs. We highlight several bat species that show greater than 70% probability of carrying an undiscovered zoonotic pathogen that should be targeted for surveillance, and identify multiple geographic hotspots where the geographic ranges of these novel reservoir species overlap.99th ESA Annual Convention 2014; 08/2014
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ABSTRACT: The increasing frequency of zoonotic disease events underscores a need to develop forecasting tools toward a more preemptive approach to outbreak investigation. We apply machine learning to data describing the traits and zoonotic pathogen diversity of the most speciose group of mammals, the rodents, which also comprise a disproportionate number of zoonotic disease reservoirs. Our models predict reservoir status in this group with over 90% accuracy, identifying species with high probabilities of harboring undiscovered zoonotic pathogens based on trait profiles that may serve as rules of thumb to distinguish reservoirs from nonreservoir species. Key predictors of zoonotic reservoirs include biogeographical properties, such as range size, as well as intrinsic host traits associated with lifetime reproductive output. Predicted hotspots of novel rodent reservoir diversity occur in the Middle East and Central Asia and the Midwestern United States.Proceedings of the National Academy of Sciences 06/2015; 112(22):7039-44. DOI:10.1073/pnas.1501598112 · 9.81 Impact Factor