Individual-level modeling of the spread of influenza within households

Malik, Rajat; Deardon, Rob; Kwong, Grace P. S.; Cowling, Benjamin J.

doi:10.1080/02664763.2014.881787

Cited by 7 publications

(8 citation statements)

References 20 publications

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“…Thus, this package will be helpful to many researchers and students in epidemiology as well as in statistics. These models can be used to model disease systems of humans (e.g., Malik et al (2014)), animals (e.g., Kwong et al (2013)), or plants (e.g., Pokharel and Deardon (2016)), as well as other transmission-based systems such as invasive species (e.g., Cook et al (2007)) or fire spread (Vrbik et al, 2012). The EpiILM package continues to exist as a work in progress.…”

Section: Resultsmentioning

confidence: 99%

“…Human diseases such as influenza, measles or HIV, tend to be transmitted via interactions which can be captured by contact networks. For example, Malik et al (2014) used a network representing whether two people shared the same household for modelling influenza spread in Hong Kong. Networks can also be used to characterize social or sexual relationships.…”

Section: Introductionmentioning

confidence: 99%

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Individual-Level Modelling of Infectious Disease Data: EpiILM

Warriyar¹,

Almutiry²,

Deardon³

2020

The R Journal

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In this article we introduce the R package EpiILM, which provides tools for simulation from, and inference for, discrete-time individual-level models of infectious disease transmission proposed by Deardon et al. (2010). The inference is set in a Bayesian framework and is carried out via Metropolis-Hastings Markov chain Monte Carlo (MCMC). For its fast implementation, key functions are coded in Fortran. Both spatial and contact network models are implemented in the package and can be set in either susceptible-infected (SI) or susceptible-infected-removed (SIR) compartmental frameworks. Use of the package is demonstrated through examples involving both simulated and real data.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Individual-Level Modelling of Infectious Disease Data: EpiILM

Warriyar¹,

Almutiry²,

Deardon³

2020

The R Journal

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“…For example, Deardon et al (2010) introduced a class of discrete time individual-level models (ILMs) which incorporate population heterogeneities by modeling the transmission of disease given various individual-level risk factors. The general framework of ILMs have already been successfully applied to a broad range of epidemic data, e.g., the 2001 UK foot-andmouth outbreak (Deardon et al 2010;Deeth and Deardon 2016;Malik, Deardon, and Kwong 2016), tomato spotted wilt virus (TSWV) disease Deardon 2014, 2016), the spread of 1-18-4 genotype of the porcine reproductive and respiratory syndrome in Ontario swine herds (Kwong, Poljak, Deardon, and Dewey 2013), and influenza transmission within households in Hong Kong during 2008 to 2009 and 2009 to 2010 (Malik, Deardon, Kwong, and Cowling 2014). Equivalent continuous time ILMs which capture the complex interactions between susceptible and infected individuals through spatial and contact networks can also be considered.…”

Section: Introductionmentioning

confidence: 99%

Continuous Time Individual-Level Models of Infectious Disease: Package EpiILMCT

Almutiry¹,

K²,

Deardon³

2021

J. Stat. Soft.

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This paper describes the R package EpiILMCT, which allows users to study the spread of infectious disease using continuous time individual level models (ILMs). The package provides tools for simulation from continuous time ILMs that are based on either spatial demographic, contact network, or a combination of both of them, and for the graphical summarization of epidemics. Model fitting is carried out within a Bayesian Markov Chain Monte Carlo framework. The continuous time ILMs can be implemented within either susceptible-infected-removed (SIR) or susceptible-infected-notified-removed (SIN R) compartmental frameworks. As infectious disease data is often partially observed, data uncertainties in the form of missing infection times -and in some situations missing removal times -are accounted for using data augmentation techniques. The package is illustrated using both simulated and an experimental data set on the spread of the tomato spotted wilt virus disease.

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“…These models can incorporate various heterogeneities within a population, are flexible, and can be extended to multiple scenarios. For example, Malik et al (2014) used this model framework to investigate human influenza transmission in Hong Kong incorporating the vaccination status, age, and residential network of individuals into the model. However, for large populations, high‐dimensional parameter spaces, and/or long epidemics, the parametrization of these models can become computationally prohibitive.…”

Section: Introductionmentioning

confidence: 99%

Emulation‐based inference for spatial infectious disease transmission models incorporating event time uncertainty

Pokharel

Deardon

2021

Scandinavian J Statistics

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Mechanistic models of infectious disease spread are key to inferring spatiotemporal infectious disease transmission dynamics. Ideally, covariate data and the infection status of individuals over time would be used to parameterize such models. However, in reality, complete data are rarely available; for example, infection times are almost never observed. Bayesian data‐augmented Markov chain Monte Carlo (MCMC) methods are commonly used to allow us to infer such missing or censored data. However, for large disease systems, these methods can be highly computationally expensive. In this paper, we propose two methods of approximate inference for such situations based on so‐called emulation techniques. Here, both methods are set in a Bayesian MCMC framework but replace the computationally expensive likelihood function by a Gaussian process‐based likelihood approximation. In the first method, we build an emulator of the discrepancy between summary statistics of simulated and observed epidemic data. In the second method, we develop an emulator of an importance sampling‐based likelihood approximation. We show how both methods offer substantial computational efficiency gains over standard Bayesian MCMC‐based method, and can be used to infer the transmission of complex infectious disease systems. We also show that importance sampling‐based methods tend to perform more satisfactorily.

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Individual-level modeling of the spread of influenza within households

Cited by 7 publications

References 20 publications

Individual-Level Modelling of Infectious Disease Data: EpiILM

Individual-Level Modelling of Infectious Disease Data: EpiILM

Continuous Time Individual-Level Models of Infectious Disease: Package EpiILMCT

Emulation‐based inference for spatial infectious disease transmission models incorporating event time uncertainty

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