Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference
نویسندگان
چکیده
Epidemic models are powerful tools in understanding infectious disease. However, as they increase size and complexity, can quickly become computationally intractable. Recent progress modelling methodology has shown that surrogate be used to emulate complex epidemic with a high-dimensional parameter space. We show deep sequence-to-sequence (seq2seq) serve accurate surrogates for sequence based model parameters, effectively replicating seasonal long-term transmission dynamics. Once trained, our predict scenarios several thousand times faster than the original model, making them ideal policy exploration. demonstrate replacing traditional learned simulator facilitates robust Bayesian inference.
منابع مشابه
Simulation-based Bayesian inference for epidemic models
A powerful and flexible method for fitting dynamic models to missing and censored data is to use the Bayesian paradigm via data-augmented Markov chain Monte Carlo (DA-MCMC). This samples from the joint posterior for the parameters and missing data, but requires high memory overheads for large-scale systems. In addition, designing efficient proposal distributions for the missing data is typicall...
متن کاملBayesian approach to inference of population structure
Methods of inferring the population structure, its applications in identifying disease models as well as foresighting the physical and mental situation of human beings have been finding ever-increasing importance. In this article, first, motivation and significance of studying the problem of population structure is explained. In the next section, the applications of inference of p...
متن کاملA tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation.
Likelihood-based inference for disease outbreak data can be very challenging due to the inherent dependence of the data and the fact that they are usually incomplete. In this paper we review recent Approximate Bayesian Computation (ABC) methods for the analysis of such data by fitting to them stochastic epidemic models without having to calculate the likelihood of the observed data. We consider...
متن کاملBayesian Inference for Spatial Beta Generalized Linear Mixed Models
In some applications, the response variable assumes values in the unit interval. The standard linear regression model is not appropriate for modelling this type of data because the normality assumption is not met. Alternatively, the beta regression model has been introduced to analyze such observations. A beta distribution represents a flexible density family on (0, 1) interval that covers symm...
متن کاملCost Analysis of Acceptance Sampling Models Using Dynamic Programming and Bayesian Inference Considering Inspection Errors
Acceptance Sampling models have been widely applied in companies for the inspection and testing the raw material as well as the final products. A number of lots of the items are produced in a day in the industries so it may be impossible to inspect/test each item in a lot. The acceptance sampling models only provide the guarantee for the producer and consumer that the items in the lots are acco...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Proceedings of the ... AAAI Conference on Artificial Intelligence
سال: 2023
ISSN: ['2159-5399', '2374-3468']
DOI: https://doi.org/10.1609/aaai.v37i12.26658