About: Linking longitudinal and cross-sectional biomarker data to understand host-pathogen dynamics: Leptospira in California sea lions (Zalophus californianus) as a case study

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About: Linking longitudinal and cross-sectional biomarker data to understand host-pathogen dynamics: Leptospira in California sea lions (Zalophus californianus) as a case study Goto Sponge NotDistinct Permalink

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Linking longitudinal and cross-sectional biomarker data to understand host-pathogen dynamics: Leptospira in California sea lions (Zalophus californianus) as a case study
Creator	Lloyd-Smith, James Galloway, Renee Greig, Denise Jensen, Eric Buhnerkempe, Michael Gulland, Frances Gomez, Forrest Orr, Anthony Wu, Qingzhong Prager Id, K
source	Medline; PMC
abstract	Confronted with the challenge of understanding population-level processes, disease ecologists and epidemiologists often simplify quantitative data into distinct physiological states (e.g. susceptible, exposed, infected, recovered). However, data defining these states often fall along a spectrum rather than into clear categories. Hence, the host-pathogen relationship is more accurately defined using quantitative data, often integrating multiple diagnostic measures, just as clinicians do to assess their patients. We use quantitative data on a major neglected tropical disease (Leptospira interrogans) in California sea lions (Zalophus californianus) to improve individual-level and population-level understanding of this Leptospira reservoir system. We create a “host-pathogen space” by mapping multiple biomarkers of infection (e.g. serum antibodies, pathogen DNA) and disease state (e.g. serum chemistry values) from 13 longitudinally sampled, severely ill individuals to characterize changes in these values through time. Data from these individuals describe a clear, unidirectional trajectory of disease and recovery within this host-pathogen space. Remarkably, this trajectory also captures the broad patterns in larger cross-sectional datasets of 1456 wild sea lions in all states of health but sampled only once. Our framework enables us to determine an individual’s location in their time-course since initial infection, and to visualize the full range of clinical states and antibody responses induced by pathogen exposure. We identify predictive relationships between biomarkers and outcomes such as survival and pathogen shedding, and use these to impute values for missing data, thus increasing the size of the useable dataset. Mapping the host-pathogen space using quantitative biomarker data enables more nuanced understanding of an individual’s time course of infection, duration of immunity, and probability of being infectious. Such maps also make efficient use of limited data for rare or poorly understood diseases, by providing a means to rapidly assess the range and extent of potential clinical and immunological profiles. These approaches yield benefits for clinicians needing to triage patients, prevent transmission, and assess immunity, and for disease ecologists or epidemiologists working to develop appropriate risk management strategies to reduce transmission risk on a population scale (e.g. model parameterization using more accurate estimates of duration of immunity and infectiousness) and to assess health impacts on a population scale.
has issue date	2020-06-29(xsd:dateTime)
bibo:doi	10.1371/journal.pntd.0008407
bibo:pmid	32598393
has license	cc0
sha1sum (hex)	4db01488fa74d104be16be34becdb4039417d235
schema:url	https://doi.org/10.1371/journal.pntd.0008407
resource representing a document's title	Linking longitudinal and cross-sectional biomarker data to understand host-pathogen dynamics: Leptospira in California sea lions (Zalophus californianus) as a case study
has PubMed Central identifier	PMC7351238
has PubMed identifier	32598393
schema:publication	PLoS Negl Trop Dis
resource representing a document's body	covid:4db01488fa74d104be16be34becdb4039417d235#body_text
is schema:about of	named entity 'health' named entity 'pathogen' named entity 'doi' named entity 'California' named entity 'RELATIONSHIPS' named entity 'INDUCED' named entity 'CROSS-SECTIONAL' named entity 'NEGLECTED' named entity 'FALL' named entity 'EXPOSED' named entity 'HOST' named entity 'INFECTION' named entity '1456' named entity 'PATHOGEN' named entity 'STATES' named entity 'SERUM' named entity 'LOCATION' named entity 'UNDERSTANDING' named entity 'DESCRIBE' named entity 'INFECTED' named entity 'OUR' named entity 'MAPPING' named entity 'USE' named entity 'LARGER' named entity 'CHANGES' named entity 'RECOVERY' named entity 'MULTIPLE' named entity 'DEFINED' named entity 'exposed' named entity 'antibody' named entity 'range' named entity 'missing data' named entity 'shedding' named entity 'Data' named entity 'size' named entity 'predictive' named entity 'pathogen' named entity 'reservoir' named entity 'simplify' named entity 'Zalophus' named entity 'PLOS NEGLECTED TROPICAL DISEASES' named entity 'patterns' named entity 'values' named entity 'space' named entity 'values' named entity 'cross-sectional' named entity 'infection' named entity 'quantitative' named entity 'sea lions' named entity 'system' named entity 'space' named entity 'Leptospira' named entity 'biomarker' named entity 'California sea lions' named entity 'host-pathogen' named entity 'infection' named entity 'neglected tropical disease' named entity 'pathogen' named entity 'epidemiologists' named entity 'initial infection' named entity 'pathogen dynamics' named entity 'renal index' named entity 'renal index' named entity 'sea lion' named entity 'biomarkers' named entity 'antibodies' named entity 'titer' named entity 'titer' named entity 'clinical signs' named entity 'Año Nuevo Island' named entity 'host-pathogen' named entity 'triage'

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