The effect of spatial scale on predicting time series: A study on epidemiological system identification

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dc.contributor.authorMonteiro L.H.A.
dc.contributor.authorOliveira D.N.
dc.contributor.authorChaui-Berlinck J.G.
dc.date.accessioned2024-03-13T01:34:47Z
dc.date.available2024-03-13T01:34:47Z
dc.date.issued2009
dc.description.abstractA susceptible-infective-recovered (SIR) epidemiological model based on probabilistic cellular automaton (PCA) is employed for simulating the temporal evolution of the registered cases of chickenpox in Arizona, USA, between 1994 and 2004. At each time step, every individual is in one of the states S, I, or R. The parameters of this model are the probabilities of each individual (each cell forming the PCA lattice) passing from a state to another state. Here, the values of these probabilities are identified by using a genetic algorithm. If nonrealistic values are allowed to the parameters, the predictions present better agreement with the historical series than if they are forced to present realistic values. A discussion about how the size of the PCA lattice affects the quality of the model predictions is presented.
dc.description.volume2009
dc.identifier.doi10.1155/2009/137854
dc.identifier.issn1563-5147
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/37366
dc.relation.ispartofMathematical Problems in Engineering
dc.rightsAcesso Aberto
dc.titleThe effect of spatial scale on predicting time series: A study on epidemiological system identification
dc.typeArtigo
local.scopus.citations4
local.scopus.eid2-s2.0-66749119996
local.scopus.subjectArizona , USA
local.scopus.subjectEpidemiological models
local.scopus.subjectModel prediction
local.scopus.subjectProbabilistic cellular automatons
local.scopus.subjectSpatial scale
local.scopus.subjectSystem identifications
local.scopus.subjectTemporal evolution
local.scopus.subjectTime step
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=66749119996&origin=inward
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