Emerging infectious diseases are characterized by complex interactions among disease agents, vectors, wildlife, humans, and the environment. Since the appearance of West Nile virus (WNV) in New York City in 1999, it has infected over 8,000 people in the United States, resulting in several hundred deaths in 46 contiguous states. The virus is transmitted by mosquitoes and maintained in various bird reservoir hosts. Its unexpected introduction, high morbidity, and rapid spread have left public health agencies facing severe time constraints in a theory-poor environment, dependent largely on observational data collected by independent survey efforts and much uncertainty. Current knowledge may be expressed as a priori constraints on models learned from data. Accordingly, we applied a Bayesian probabilistic relational approach to generate spatially and temporally linked models from heterogeneous data sources. Using data collected from multiple independent sources in Maryland, we discovered the integrated context in which infected birds are plausible indicators for positive mosquito pools and human cases for 2001 and 2002.
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Ecological Society of America is collaborating with JSTOR to digitize, preserve and extend access to Ecology.Abstract. Eigenvalues, the solutions to the characteristic polynomial, are important measures of community behavior. Their range and practical measurement present difficult challenges in ecology. We therefore present the derivation of variance of eigenvalues of the community matrix, var(X) = var (ai) + (n -1)aijaji, as well as a novel related formula, namely, the expectancy of pairwise eigenvalues (EPV), var(Xpairwise) = var(aii-pairwise) + aijaji.We propose that the two formulae may be useful in evaluating the relative contributions of inter-and intraspecific effects on the behavior of large systems. EPV allows estimating eigenvalue distribution of systems of unknown size.
Eigenvalues, the solutions to the characteristic polynomial, are important measures of community behavior. Their range and practical measurement present difficult challenges in ecology. We therefore present the derivation of variance of eigenvalues of the community matrix, var(λ) = var (aii) + (n − 1)aijaji, as well as a novel related formula, namely, the expectancy of pairwise eigenvalues (EPV), var(λpairwise) = var(aii−pairwise) + aijaji. We propose that the two formulae may be useful in evaluating the relative contributions of inter‐ and intraspecific effects on the behavior of large systems. EPV allows estimating eigenvalue distribution of systems of unknown size.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.