Introduction and snapshot review: Relating infectious disease transmission models to data

O’Neill, Philip D.

doi:10.1002/sim.3968

Cited by 58 publications

(49 citation statements)

References 40 publications

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“…Case report data such as time series of disease incidence are often incomplete or subject to severe biases like underreporting. Moreover, disease dynamics are generally only partially observed in that the exact times at which infection and recovery events occur are rarely, if ever, directly observed [1], [2], [3]. Researchers have therefore turned to the large amounts of molecular sequence data becoming available when case report data are insufficient.…”

Section: Introductionmentioning

confidence: 99%

Inference for Nonlinear Epidemiological Models Using Genealogies and Time Series

2011

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Phylodynamics - the field aiming to quantitatively integrate the ecological and evolutionary dynamics of rapidly evolving populations like those of RNA viruses – increasingly relies upon coalescent approaches to infer past population dynamics from reconstructed genealogies. As sequence data have become more abundant, these approaches are beginning to be used on populations undergoing rapid and rather complex dynamics. In such cases, the simple demographic models that current phylodynamic methods employ can be limiting. First, these models are not ideal for yielding biological insight into the processes that drive the dynamics of the populations of interest. Second, these models differ in form from mechanistic and often stochastic population dynamic models that are currently widely used when fitting models to time series data. As such, their use does not allow for both genealogical data and time series data to be considered in tandem when conducting inference. Here, we present a flexible statistical framework for phylodynamic inference that goes beyond these current limitations. The framework we present employs a recently developed method known as particle MCMC to fit stochastic, nonlinear mechanistic models for complex population dynamics to gene genealogies and time series data in a Bayesian framework. We demonstrate our approach using a nonlinear Susceptible-Infected-Recovered (SIR) model for the transmission dynamics of an infectious disease and show through simulations that it provides accurate estimates of past disease dynamics and key epidemiological parameters from genealogies with or without accompanying time series data.

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Section: Introductionmentioning

confidence: 99%

Inference for Nonlinear Epidemiological Models Using Genealogies and Time Series

2011

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“…In particular, one can not simply take the country's population, since some people may not be included in surveys, may have good health conditions excluding them from the seasonal respiratory epidemic processes, etc. In fact, it is a known problem of uncertainty quantification in epidemiological modelling, and the adjustment to observed data for some referent time interval is one of the most widely accepted approaches [24]. At the same time, the scaling and shift procedure αI + β → I is linear as well as the the conservation law, which contains the linear combination of the variables.…”

Section: Influenzamentioning

confidence: 99%

Dynamical prediction of flu seasonality driven by ambient temperature: influenza vs. common cold

Postnikov

2016

Eur. Phys. J. B

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Abstract. This work presents a comparative analysis of Influenzanet data for influenza itself and common cold in the Netherlands during the last 5 years, from the point of view of modelling by linearised SIRS equations parametrically driven by the ambient temperature. It is argued that this approach allows for the forecast of common cold, but not of influenza in a strict sense. The difference in their kinetic models is discussed with reference to the clinical background.

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“…Complete subject–level data, which would consist of the exact times at which individuals transition through disease states, are of-ten unavailable (O’Neill, 2010). Fitting SEMs in the absence of complete subject–level data presents a complicated latent variable problem since it is usually impossible to analytically integrate over the missing data (O’Neill, 2002).…”

Section: Introductionmentioning

confidence: 99%

“…Existing approaches to fitting SEMs with intractable likelihoods have largely fallen into four groups: martingale methods, approximation methods, simulation based methods, and data augmentation (DA) methods (O’Neill, 2010). Martingale methods estimate the parameters of interest using estimating equations based on martingales for the counting processes within the SEM, e.g., infections and recoveries (Becker, 1977, Watson, 1981, Sudbury, 1985, Andersson and Britton, 2000, Linden-strand and Svensson, 2013).…”

Section: Introductionmentioning

confidence: 99%

Efficient Data Augmentation for Fitting Stochastic Epidemic Models to Prevalence Data

Fintzi

Cui

Wakefield

et al. 2017

Journal of Computational and Graphical Statistics

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Stochastic epidemic models describe the dynamics of an epidemic as a disease spreads through a population. Typically, only a fraction of cases are observed at a set of discrete times. The absence of complete information about the time evolution of an epidemic gives rise to a complicated latent variable problem in which the state space size of the epidemic grows large as the population size increases. This makes analytically integrating over the missing data infeasible for populations of even moderate size. We present a data augmentation Markov chain Monte Carlo (MCMC) framework for Bayesian estimation of stochastic epidemic model parameters, in which measurements are augmented with subject–level disease histories. In our MCMC algorithm, we propose each new subject–level path, conditional on the data, using a time–inhomogeneous continuous–time Markov process with rates determined by the infection histories of other individuals. The method is general, and may be applied to a broad class of epidemic models with only minimal modifications to the model dynamics and/or emission distribution. We present our algorithm in the context of multiple stochastic epidemic models in which the data are binomially sampled prevalence counts, and apply our method to data from an outbreak of influenza in a British boarding school.

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Introduction and snapshot review: Relating infectious disease transmission models to data

Cited by 58 publications

References 40 publications

Inference for Nonlinear Epidemiological Models Using Genealogies and Time Series

Inference for Nonlinear Epidemiological Models Using Genealogies and Time Series

Dynamical prediction of flu seasonality driven by ambient temperature: influenza vs. common cold

Efficient Data Augmentation for Fitting Stochastic Epidemic Models to Prevalence Data

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