Approximate Bayesian Computation and Simulation-Based Inference for Complex Stochastic Epidemic Models

McKinley, Trevelyan J.; Vernon, Ian; Andrianakis, Ioannis; McCreesh, Nicky; Oakley, Jeremy E.; Nsubuga, Rebecca N.; Goldstein, Michael; White, Richard G.

doi:10.1214/17-sts618

Cited by 73 publications

(76 citation statements)

References 65 publications

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“…However, ABC is inefficient and can fail when the parameter space is high dimensional, when there are many calibration targets, or when the prior distributions are very different from the posterior distributions. McKinley et al (2018) found that popular ABC variants that improve the algorithm's efficiency were not computationally feasible for calibrating stochastic epidemiological models. We propose an Incremental Mixture ABC (IMABC) approach for MSM model calibration that begins with a basic rejectionsampling ABC step (e.g., Pritchard et al, 1999) and then incrementally adds points to regions where targets are well predicted.…”

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confidence: 99%

Microsimulation model calibration using incremental mixture approximate Bayesian computation

Rutter¹,

Ozik²,

DeYoreo³

et al. 2019

Ann. Appl. Stat.

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Microsimulation models (MSMs) are used to inform policy by predicting population-level outcomes under different scenarios. MSMs simulate individual-level event histories that mark the disease process (such as the development of cancer) and the effect of policy actions (such as screening) on these events. MSMs often have many unknown parameters; calibration is the process of searching the parameter space to select parameters that result in accurate MSM prediction of a wide range of targets. We develop Incremental Mixture Approximate Bayesian Computation (IMABC) for MSM calibration, which results in a simulated sample from the posterior distribution of model parameters given calibration targets. IMABC begins with a rejection-based ABC step, drawing a sample of points from the prior distribution of model parameters and accepting points that result in simulated targets that are near observed targets. Next, the sample is iteratively updated by drawing additional points from a mixture of multivariate normal distributions and accepting points that result in accurate predictions. Posterior estimates are obtained by weighting the final set of accepted points to account for the adaptive sampling scheme. We demonstrate IMABC by calibrating CRC-SPIN 2.0, an updated version of a MSM for colorectal cancer (CRC) that has been used to inform national CRC screening guidelines.

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confidence: 99%

Microsimulation model calibration using incremental mixture approximate Bayesian computation

Rutter¹,

Ozik²,

DeYoreo³

et al. 2019

Ann. Appl. Stat.

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“…Fitting stochastic models to population-level time-course data is a relatively new field of research. One issue that consistently arises in this field is that fitting by comparison of experimental and simulation distributions has the distinct drawback of high computational execution time [34][35][36][37]. We have encountered this issue in our study as well.…”

Section: Discussionmentioning

confidence: 95%

Uncovering modeling features of viral replication dynamics from high-throughput single-cell virology experiments

Teufel

Liu

Draghi

et al. 2020

Preprint

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Viruses experience selective pressure on the timing and order of events during infection to maximize the number of viable offspring they produce. Additionally, they may experience variability in cellular environments encountered, as individual eukaryotic cells can display variation in gene expression among cells. This leads to a dynamic phenotypic landscape that viruses must face to produce offspring. To examine replication dynamics displayed by viruses faced with this variable landscape, we have developed a method for fitting a stochastic mechanistic model of viral infection to growth data from high-throughput single-cell poliovirus infection experiments. The model's mechanistic parameters provide estimates of several aspects associated with the virus's intracellular dynamics. We examine distributions of parameter estimates and assess their variability to gain insight into the root causes of variability in viral growth dynamics. We also fit our model to experiments performed under various drug treatments and examine which parameters differ under these conditions. We find that parameters associated with translation and early stage viral replication processes are essential for the model to capture experimentally observed dynamics. In aggregate, our results suggest that differences in viral growth data generated under different treatments can largely be captured by steps that occur early in the replication process.

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“…ABC SMC sampling draws large groups of candidate predictors simultaneously to avoid local minima and to speed up computation. ABC SMC sampling offers several attractive features of Bayesian analysis, including the ability to quantify the uncertainty in predictions (McKinley et al ., ) and to account for missing data with data augmentation methods that add potentially unobserved states to the parameter space (O’Neill and Roberts, ), or faster alternatives to data augmentation such as modified ‘poor man's data augmentation’ algorithms (Sweeting and Kharroubi, ). Sufficient statistics can be of key importance in ABC methods, and finding the correct such statistic in spatial epidemic models may provide a fruitful avenue for future research.…”

Section: Discussion On the Paper By Laber Meyer Reich Pacifici Comentioning

confidence: 99%

Optimal Treatment Allocations in Space and Time for On-Line Control of an Emerging Infectious Disease

Laber

Meyer

Reich

et al. 2018

Journal of the Royal Statistical Society Series C: Applied Statistics

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Summary A key component in controlling the spread of an epidemic is deciding where, when and to whom to apply an intervention. We develop a framework for using data to inform these decisions in realtime. We formalize a treatment allocation strategy as a sequence of functions, one per treatment period, that map up‐to‐date information on the spread of an infectious disease to a subset of locations where treatment should be allocated. An optimal allocation strategy optimizes some cumulative outcome, e.g. the number of uninfected locations, the geographic footprint of the disease or the cost of the epidemic. Estimation of an optimal allocation strategy for an emerging infectious disease is challenging because spatial proximity induces interference between locations, the number of possible allocations is exponential in the number of locations, and because disease dynamics and intervention effectiveness are unknown at outbreak. We derive a Bayesian on‐line estimator of the optimal allocation strategy that combines simulation–optimization with Thompson sampling. The estimator proposed performs favourably in simulation experiments. This work is motivated by and illustrated using data on the spread of white nose syndrome, which is a highly fatal infectious disease devastating bat populations in North America.

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Approximate Bayesian Computation and Simulation-Based Inference for Complex Stochastic Epidemic Models

Cited by 73 publications

References 65 publications

Microsimulation model calibration using incremental mixture approximate Bayesian computation

Microsimulation model calibration using incremental mixture approximate Bayesian computation

Uncovering modeling features of viral replication dynamics from high-throughput single-cell virology experiments

Optimal Treatment Allocations in Space and Time for On-Line Control of an Emerging Infectious Disease

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