Ecological Origins of Novel Human Pathogen
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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- "Studies of a wide variety of emerging viral pathogens show a strong association with the role of NHPs as enzootic hosts and the potential for emergence as human pathogens (Woolhouse and Gaunt, 2007), and the history of CHIKV, DENV and YFV certainly supports this concept. Others that also use NHP hosts and can infect A. aegypti, and which therefore appear to have urban emergence potential, include Mayaro and Zika viruses, both of which are increasingly recognized as important human pathogens with widespread distributions in the New and Old Worlds, respectively (Weaver and Reisen, 2010). "
ABSTRACT: Chikungunya virus (CHIKV) has a long history of emergence into urban transmission cycles from its ancestral, enzootic, sylvatic foci in Sub-Saharan Africa, most recently in the Americas beginning in 2013. Since 2004, reemergence has resulted in millions of cases of severe, debilitating and often chronic arthralgia on five continents. Here, we review this history based on phylogenetic studies, and discuss probable future spread and disease in the Americas. We also discuss a series of mutations in the recently emerged Indian Ocean Lineage that have adapted the virus for transmission for the first time by the Aedes albopictus urban mosquito vector, and compare CHIKV to other arboviruses with and without similar histories of urbanization. This article forms part of a symposium in Antiviral Research on "Chikungunya discovers the New World." Copyright © 2015 Elsevier B.V. All rights reserved.Antiviral research 05/2015; 120. DOI:10.1016/j.antiviral.2015.04.016 · 3.94 Impact Factor
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- "For such prevention to take place, timely and accurate prediction of outbreaks is critical. More than two thirds of emerging infectious diseases in recent decades are zoonotic in origin (crossing from animals to humans) [1, 2]. An example is the recent emergence of highly pathogenic avian influenza. "
ABSTRACT: Background Time series models can play an important role in disease prediction. Incidence data can be used to predict the future occurrence of disease events. Developments in modeling approaches provide an opportunity to compare different time series models for predictive power. Results We applied ARIMA and Random Forest time series models to incidence data of outbreaks of highly pathogenic avian influenza (H5N1) in Egypt, available through the online EMPRES-I system. We found that the Random Forest model outperformed the ARIMA model in predictive ability. Furthermore, we found that the Random Forest model is effective for predicting outbreaks of H5N1 in Egypt. Conclusions Random Forest time series modeling provides enhanced predictive ability over existing time series models for the prediction of infectious disease outbreaks. This result, along with those showing the concordance between bird and human outbreaks (Rabinowitz et al. 2012), provides a new approach to predicting these dangerous outbreaks in bird populations based on existing, freely available data. Our analysis uncovers the time-series structure of outbreak severity for highly pathogenic avain influenza (H5N1) in Egypt.BMC Bioinformatics 08/2014; 15(1):276. DOI:10.1186/1471-2105-15-276 · 2.58 Impact Factor
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- "Only individuals living in dense forest (presumably with an abundant and diverse primate fauna) showed both polymerase chain reaction (PCR) and serological evidence of SFV infection, suggesting that transmission is greater in more intact forests (Wolfe et al. 2004). Intensive or sustained usage of areas of high biodiversity may also mediate the risk of other emerging and zoonotic diseases from a variety of reservoir hosts (Woolhouse and Gaunt 2007). For instance, the increased disease incidence documented where deforestation is occurring often cannot be ascribed directly to the loss of biodiversity that comes along with deforestation (and thus, to a dilution effect), but rather to the increase in human contact with forested habitat that deforestation entails (Wolfe et al. 2005). "
ABSTRACT: Control of human infectious disease has been promoted as a valuable ecosystem service arising from the conservation of biodiversity. There are two commonly discussed mechanisms by which biodiversity loss could increase rates of infectious disease in a landscape. First, loss of competitors or predators could facilitate an increase in the abundance of competent reservoir hosts. Second, biodiversity loss could disproportionately affect non-competent, or less competent reservoir hosts, which would otherwise interfere with pathogen transmission to human populations by, for example, wasting the bites of infected vectors. A negative association between biodiversity and disease risk, sometimes called the "dilution effect hypothesis," has been supported for a few disease agents, suggests an exciting win-win outcome for the environment and society, and has become a pervasive topic in the disease ecology literature. Case studies have been assembled to argue that the dilution effect is general across disease agents. Less touted are examples in which elevated biodiversity does not affect or increases infectious disease risk for pathogens of public health concern. In order to assess the likely generality of the dilution effect, we review the association between biodiversity and public health across a broad variety of human disease agents. Overall, we hypothesize that conditions for the dilution effect are unlikely to be met for most important diseases of humans. Biodiversity probably has little net effect on most human infectious diseases but, when it does have an effect, observation and basic logic suggest that biodiversity will be more likely to increase than to decrease infectious disease risk.Ecology 04/2014; 95(4):817-32. DOI:10.1890/13-1041.1 · 4.66 Impact Factor