About: Inferring infection hazard in wildlife populations by linking data across individual and population scales

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Inferring infection hazard in wildlife populations by linking data across individual and population scales
Creator	Riley, Steven Lloyd-Smith, James Cross, Paul Gilbert, Amy Graham, Andrea Webb, Colleen Miller, Ryan Pepin, Kim Buhnerkempe, Michael Golas, Ben Hoeting, Jennifer Hooten, Mevin Kay, Shannon Samuel, Michael Shriner, Susan
Source	Medline; PMC
abstract	Our ability to infer unobservable disease‐dynamic processes such as force of infection (infection hazard for susceptible hosts) has transformed our understanding of disease transmission mechanisms and capacity to predict disease dynamics. Conventional methods for inferring FOI estimate a time‐averaged value and are based on population‐level processes. Because many pathogens exhibit epidemic cycling and FOI is the result of processes acting across the scales of individuals and populations, a flexible framework that extends to epidemic dynamics and links within‐host processes to FOI is needed. Specifically, within‐host antibody kinetics in wildlife hosts can be short‐lived and produce patterns that are repeatable across individuals, suggesting individual‐level antibody concentrations could be used to infer time since infection and hence FOI. Using simulations and case studies (influenza A in lesser snow geese and Yersinia pestis in coyotes), we argue that with careful experimental and surveillance design, the population‐level FOI signal can be recovered from individual‐level antibody kinetics, despite substantial individual‐level variation. In addition to improving inference, the cross‐scale quantitative antibody approach we describe can reveal insights into drivers of individual‐based variation in disease response, and the role of poorly understood processes such as secondary infections, in population‐level dynamics of disease.
has issue date	2017-01-16(xsd:dateTime)
bibo:doi	10.1111/ele.12732
bibo:pmid	28090753
has license	no-cc
sha1sum (hex)	d9c5e459ad57189254e11b1f9be8e810d57223e7
schema:url	https://doi.org/10.1111/ele.12732
resource representing a document's title	Inferring infection hazard in wildlife populations by linking data across individual and population scales
has PubMed Central identifier	PMC7163542
has PubMed identifier	28090753
schema:publication	Ecol Lett
resource representing a document's body	covid:d9c5e459ad57189254e11b1f9be8e810d57223e7#body_text
is schema:about of	named entity '2015' named entity 'linking' named entity 'HOST' named entity '2015' named entity 'code' named entity 'Metropolis-Hastings algorithm' named entity 'Bayesian' named entity 'prior' named entity 'algorithm' named entity 'host model' named entity 'statistical analyses' named entity 'Metropolis-Hastings' named entity 'Markov chain Monte Carlo' named entity 'infection' named entity 'Metropolis-Hastings' named entity 'host model' named entity 'assessed' named entity 'infection' named entity 'infection' named entity 'Systematic Sampling' named entity 'antibody' named entity 'sample size' named entity 'snow goose' named entity 'algorithm' named entity 'autoregressive' named entity 'antibody' named entity 'antibody' named entity 'Gibbs sampling' named entity 'antibody' named entity 'antibody' named entity 'decay rate' named entity 'sampling' named entity 'Metropolis-Hastings algorithm' named entity 'calendar days' named entity 'credible intervals' named entity 'sampling' named entity 'influenza' named entity 'antibody' named entity 'infection' named entity 'lesser snow geese' named entity 'antibody' named entity 'time period' named entity 'antibody' named entity 'antibody' named entity 'Metropolis-Hastings' named entity 'avian influenza' named entity 'antibody' named entity 'antibody' named entity 'antibody response' named entity 'antibody' named entity 'individual-based' named entity 'disease transmission' named entity 'infection' named entity 'sampling' named entity 'antibody' named entity 'Poisson' named entity '95%' named entity 'sampling' named entity 'Sampling' named entity 'primary infection' named entity 'antibody' named entity 'antibody' named entity 'mixture model' named entity 'anamnestic response' named entity 'Figure 8' named entity 'MCMC' named entity 'antibody'

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