Individual-Based Model (ibm): an alternative framework for epidemiological compartment models
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Universidade Federal de Minas Gerais
Descrição
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Artigo de periódico
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Membros da banca
Resumo
A traditional approach to model infectious diseases is to use compartment
models based on di erential equations, such as the SIR (Susceptible-Infected-Recovered)
model. These models explain average behavior, but are inadequate to account for
stochastic
uctuations of epidemiological variables. An alternative approach is to use
Individual-Based Model (IBM), that represent each individual as a set of features
that change dynamically over time. This allows modeling population phenomena as
aggregates of individual interactions. This paper presents a general framework to
model epidemiological systems using IBM as an alternative to replace or complement
epidemiological compartment models. The proposed modeling approach is shown to
allow the study of some phenomena which are related to nite-population demographic
stochastic
uctuation. In particular, a procedure for the computation of the probability
of disease eradication within a time horizon in the case of systems which have mean- eld
endemic equilibrium is presented as a direct application of the proposed approach. It
is shown, how this general framework may be described as an algorithm suitable to
model di erent types of compartment models. Numerical simulations illustrate how this
approach may provide greater insight about a great variety of epidemiological systems.
Abstract
Assunto
Probabilidades, Teoria das medidas, Processo estocástico
Palavras-chave
Individual-Based model; mathematical epidemiology; stochastic fluctuations, epidemiological compartment models.