About: An object simulation model for modeling hypothetical disease epidemics

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About: An object simulation model for modeling hypothetical disease epidemics – EpiFlex Goto Sponge NotDistinct Permalink

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

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	An object simulation model for modeling hypothetical disease epidemics – EpiFlex
Creator	Hanley, Brian
Source	PMC
abstract	BACKGROUND: EpiFlex is a flexible, easy to use computer model for a single computer, intended to be operated by one user who need not be an expert. Its purpose is to study in-silico the epidemic behavior of a wide variety of diseases, both known and theoretical, by simulating their spread at the level of individuals contracting and infecting others. To understand the system fully, this paper must be read together in conjunction with study of the software and its results. EpiFlex is evaluated using results from modeling influenza A epidemics and comparing them with a variety of field data sources and other types of modeling. EpiFlex is an object-oriented Monte Carlo system, allocating entities to correspond to individuals, disease vectors, diseases, and the locations that hosts may inhabit. EpiFlex defines eight different contact types available for a disease. Contacts occur inside locations within the model. Populations are composed of demographic groups, each of which has a cycle of movement between locations. Within locations, superspreading is defined by skewing of contact distributions. RESULTS: EpiFlex indicates three phenomena of interest for public health: (1) R(0 )is variable, and the smaller the population, the larger the infected fraction within that population will be; (2) significant compression/synchronization between cities by a factor of roughly 2 occurs between the early incubation phase of a multi-city epidemic and the major manifestation phase; (3) if better true morbidity data were available, more asymptomatic hosts would be seen to spread disease than we currently believe is the case for influenza. These results suggest that field research to study such phenomena, while expensive, should be worthwhile. CONCLUSION: Since EpiFlex shows all stages of disease progression, detailed insight into the progress of epidemics is possible. EpiFlex shows the characteristic multimodality and apparently random variation characteristic of real world data, but does so as an emergent property of a carefully constructed model of disease dynamics and is not simply a stochastic system. EpiFlex can provide a better understanding of infectious diseases and strategies for response.
has issue date	2006-08-23(xsd:dateTime)
bibo:doi	10.1186/1742-4682-3-32
bibo:pmid	16928271
has license	cc-by
sha1sum (hex)	61d9a0fe39f4e845c44a06787de6f5f033b998a3
schema:url	https://doi.org/10.1186/1742-4682-3-32
resource representing a document's title	An object simulation model for modeling hypothetical disease epidemics – EpiFlex
has PubMed Central identifier	PMC1570461
has PubMed identifier	16928271
schema:publication	Theor Biol Med Model
resource representing a document's body	covid:61d9a0fe39f4e845c44a06787de6f5f033b998a3#body_text
is schema:about of	named entity 'operated' named entity 'in-silico' named entity 'comparing' named entity 'behavior' named entity 'level' named entity 'epidemic' named entity 'conjunction' named entity 'modeling' named entity 'hypothetical' named entity 'epidemic' named entity 'simulation model' named entity 'modeling' named entity 'vector' named entity 'population size' named entity 'age segregation' named entity 'organism' named entity 'significant difference' named entity 'Probability' named entity 'morbidity' named entity 'EPDM' named entity 'Cauchy distribution' named entity 'public health' named entity 'epidemic' named entity 'Monte Carlo' named entity 'competitive inhibition' named entity 'LaForce' named entity 'California' named entity 'probability' named entity 'algorithm' named entity 'population increases' named entity 'Smalltalk' named entity 'epidemic' named entity 'flow chart' named entity 'Simula' named entity 'Anchorage airport' named entity 'infectious disease spread' named entity 'infection' named entity 'disease vector' named entity 'morbidity rate' named entity 'host immunity' named entity 'serological' named entity 'mucosa' named entity 'seroprevalence' named entity 'SARS epidemic' named entity 'vector' named entity 'Monte Carlo' named entity 'histogram' named entity 'Public health' named entity 'infection' named entity 'incubation' named entity 'disease transmission' named entity 'infection' named entity 'epidemic' named entity 'influenza' named entity 'C++' named entity 'morbidity' named entity 'descendents' named entity 'influenza' named entity 'population groups' named entity 'US Census' named entity 'contact tracing' named entity 'influenza' named entity 'infection' named entity 'emerging disease' named entity 'synergistic' named entity 'Object-Oriented' named entity 'infection' named entity 'demographic group' named entity 'influenza' named entity 'influenza' named entity 'population size' named entity 'birth and death' named entity 'Prodromal' named entity '5.5' named entity 'histogram' named entity 'rabies' named entity 'potentially infectious' named entity 'Hong Kong' named entity 'influenza epidemic' named entity 'demographic group' named entity 'herd immunity'

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