About: Networks and Models with Heterogeneous Population Structure in Epidemiology

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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	Networks and Models with Heterogeneous Population Structure in Epidemiology
Creator	Kao, R
Source	PMC
abstract	Heterogeneous population structure can have a profound effect on infectious disease dynamics, and is particularly important when investigating “tactical” disease control questions. At times, the nature of the network involved in the transmission of the pathogen (bacteria, virus, macro-parasite, etc.) appears to be clear; however, the nature of the network involved is dependent on the scale (e.g. within-host, between-host, or between-population), the nature of the contact, which ranges from the highly specific (e.g. sexual acts or needle sharing at the person-to-person level) to almost completely non-specific (e.g. aerosol transmission, often over long distances as can occur with the highly infectious livestock pathogen foot-and-mouth disease virus—FMDv—at the farm-to-farm level, e.g. Schley et al. in J. R. Soc. Interface 6:455–462, 2008), and the timescale of interest (e.g. at the scale of the individual, the typical infectious period of the host). Theoretical approaches to examining the implications of particular network structures on disease transmission have provided critical insight; however, a greater challenge is the integration of network approaches with data on real population structures. In this chapter, some concepts in disease modelling will be introduced, the relevance of selected network phenomena discussed, and then results from real data and their relationship to network analyses summarised. These include examinations of the patterns of air traffic and its relation to the spread of SARS in 2003 (Colizza et al. in BMC Med., 2007; Hufnagel et al. in Proc. Natl. Acad. Sci. USA 101:15124–15129, 2004), the use of the extensively documented Great Britain livestock movements network (Green et al. in J. Theor. Biol. 239:289–297, 2008; Robinson et al. in J. R. Soc. Interface 4:669–674, 2007; Vernon and Keeling in Proc. R. Soc. Lond. B, Biol. Sci. 276:469–476, 2009) and the growing interest in combining contact structure data with phylogenetics to identify real contact patterns as they directly relate to diseases of interest (Cottam et al. in PLoS Pathogens 4:1000050, 2007; Hughes et al. in PLoS Pathogens 5:1000590, 2009).
has issue date	2010-06-02(xsd:dateTime)
bibo:doi	10.1007/978-1-84996-396-1_4
has license	no-cc
sha1sum (hex)	2af19a379c452f85805187303b75f3294799f7fd
schema:url	https://doi.org/10.1007/978-1-84996-396-1_4
resource representing a document's title	Networks and Models with Heterogeneous Population Structure in Epidemiology
has PubMed Central identifier	PMC7123232
schema:publication	Network Science
resource representing a document's body	covid:2af19a379c452f85805187303b75f3294799f7fd#body_text
is schema:about of	named entity 'GROW' named entity 'POPULATION STRUCTURE' named entity 'disease' named entity 'scale' named entity 'sexual' named entity 'data' named entity 'ranges' named entity 'Epidemiology' named entity 'Chapter' named entity 'REAL' named entity 'TO IDENTIFY' named entity 'INDIVIDUAL' named entity 'HETEROGENEOUS' named entity 'NETWORK' named entity 'NATURE' named entity 'EFFECT' named entity 'RESULTS' named entity 'SPECIFIC' named entity 'PARTICULAR' named entity 'SOC' named entity 'SARS' named entity 'DISEASE DYNAMICS' named entity 'EXAMINATIONS' named entity 'FMDV' named entity 'CRITICAL' named entity 'LIVESTOCK' named entity 'INSIGHT' named entity 'AEROSOL' named entity 'CLEAR' named entity 'MACRO' named entity 'TIMES' named entity 'MODELS' named entity 'INTERFACE' named entity 'PHENOMENA' named entity 'CONTACT' named entity 'PATHOGEN' named entity 'INFECTIOUS PERIOD' named entity 'GREATER' named entity 'PARASITE' named entity 'RELATION' named entity 'STRUCTURE DATA' named entity 'NETWORKS' named entity 'PHYLOGENETICS' named entity 'INTEREST' named entity 'COMBINING' named entity 'CHAPTER' named entity '462' named entity 'SCALE' named entity 'APPROACHES' named entity 'INCLUDE' named entity 'DATA' named entity 'RELATIONSHIP' named entity 'SELECTED' named entity 'HIGHLY' named entity 'THEIR' named entity 'DISTANCES' named entity 'FARM' named entity 'VIRUS' named entity 'NON-SPECIFIC' named entity 'INFECTIOUS' named entity 'ETC.' named entity 'HAVE' named entity 'INFECTIOUS DISEASE' named entity 'INVOLVED' named entity 'DISEASE CONTROL' named entity 'INTRODUCED' named entity 'ACTS' named entity 'ALMOST' named entity 'COMPLETELY' named entity 'INVESTIGATING' named entity 'EXAMINING' named entity 'IMPORTANT' named entity 'STRUCTURES' named entity 'OF INTEREST' named entity 'HETEROGENEOUS' named entity 'EPIDEMIOLOGY' named entity 'CONTACT' named entity 'THESE' named entity 'POPULATION' named entity 'DISEASE' named entity 'LEVEL' named entity 'SPREAD' named entity 'OCCUR'

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