About: Data–model fusion to better understand emerging pathogens and improve infectious disease forecasting

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About: Data–model fusion to better understand emerging pathogens and improve infectious disease forecasting 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
has title	Data–model fusion to better understand emerging pathogens and improve infectious disease forecasting
Creator	Ostfeld, Richard Glass, Gregory Ladeau, Shannon Latimer, Andrew Thompson Hobbs, N
Source	Medline; PMC
abstract	Ecologists worldwide are challenged to contribute solutions to urgent and pressing environmental problems by forecasting how populations, communities, and ecosystems will respond to global change. Rising to this challenge requires organizing ecological information derived from diverse sources and formally assimilating data with models of ecological processes. The study of infectious disease has depended on strategies for integrating patterns of observed disease incidence with mechanistic process models since John Snow first mapped cholera cases around a London water pump in 1854. Still, zoonotic and vector‐borne diseases increasingly affect human populations, and methods used to successfully characterize directly transmitted diseases are often insufficient. We use four case studies to demonstrate that advances in disease forecasting require better understanding of zoonotic host and vector populations, as well of the dynamics that facilitate pathogen amplification and disease spillover into humans. In each case study, this goal is complicated by limited data, spatiotemporal variability in pathogen transmission and impact, and often, insufficient biological understanding. We present a conceptual framework for data–model fusion in infectious disease research that addresses these fundamental challenges using a hierarchical state‐space structure to (1) integrate multiple data sources and spatial scales to inform latent parameters, (2) partition uncertainty in process and observation models, and (3) explicitly build upon existing ecological and epidemiological understanding. Given the constraints inherent in the study of infectious disease and the urgent need for progress, fusion of data and expertise via this type of conceptual framework should prove an indispensable tool.
has issue date	2011-07-01(xsd:dateTime)
bibo:doi	10.1890/09-1409.1
bibo:pmid	21830694
has license	no-cc
sha1sum (hex)	9bdc7a21cece54eee0a5dd98b6d0f8c5db6dd212
schema:url	https://doi.org/10.1890/09-1409.1
resource representing a document's title	Data–model fusion to better understand emerging pathogens and improve infectious disease forecasting
has PubMed Central identifier	PMC7163730
has PubMed identifier	21830694
schema:publication	Ecol Appl
resource representing a document's body	covid:9bdc7a21cece54eee0a5dd98b6d0f8c5db6dd212#body_text
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