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A Gaussian-process approximation to a spatial SIR process using moment closures and emulators.
Trostle, Parker; Guinness, Joseph; Reich, Brian J.
Afiliação
  • Trostle P; Department of Statistics, North Carolina State University, Raleigh, NC, 27607, United States.
  • Guinness J; Department of Statistics and Data Science, Cornell University, Ithaca, NY, 14853, United States.
  • Reich BJ; Department of Statistics, North Carolina State University, Raleigh, NC, 27607, United States.
Biometrics ; 80(3)2024 Jul 01.
Article em En | MEDLINE | ID: mdl-39036985
ABSTRACT
The dynamics that govern disease spread are hard to model because infections are functions of both the underlying pathogen as well as human or animal behavior. This challenge is increased when modeling how diseases spread between different spatial locations. Many proposed spatial epidemiological models require trade-offs to fit, either by abstracting away theoretical spread dynamics, fitting a deterministic model, or by requiring large computational resources for many simulations. We propose an approach that approximates the complex spatial spread dynamics with a Gaussian process. We first propose a flexible spatial extension to the well-known SIR stochastic process, and then we derive a moment-closure approximation to this stochastic process. This moment-closure approximation yields ordinary differential equations for the evolution of the means and covariances of the susceptibles and infectious through time. Because these ODEs are a bottleneck to fitting our model by MCMC, we approximate them using a low-rank emulator. This approximation serves as the basis for our hierarchical model for noisy, underreported counts of new infections by spatial location and time. We demonstrate using our model to conduct inference on simulated infections from the underlying, true spatial SIR jump process. We then apply our method to model counts of new Zika infections in Brazil from late 2015 through early 2016.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Simulação por Computador / Processos Estocásticos / Infecção por Zika virus Limite: Humans Idioma: En Revista: Biometrics Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Simulação por Computador / Processos Estocásticos / Infecção por Zika virus Limite: Humans Idioma: En Revista: Biometrics Ano de publicação: 2024 Tipo de documento: Article