About: Inference, prediction and optimization of non-pharmaceutical interventions using compartment models: the PyRoss library

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Inference, prediction and optimization of non-pharmaceutical interventions using compartment models: the PyRoss library
Creator	Li, Yuting Singh, Rajesh Adhikari, R Bolitho, Austen Caballero, Fernando Cates, Michael Dolezal, Jakub Ekeh, Timothy Guioth, Jules Jack, Robert Kappler, Julian Kikuchi, Lukas Kobayashi, Hideki Peterson, Joseph Pietzonka, Patrick Remez, Benjamin Rohrbach, Paul Turk, Günther
Source	ArXiv
abstract	PyRoss is an open-source Python library that offers an integrated platform for inference, prediction and optimisation of NPIs in age- and contact-structured epidemiological compartment models. This report outlines the rationale and functionality of the PyRoss library, with various illustrations and examples focusing on well-mixed, age-structured populations. The PyRoss library supports arbitrary structured models formulated stochastically (as master equations) or deterministically (as ODEs) and allows mid-run transitioning from one to the other. By supporting additional compartmental subdivision ad libitum, PyRoss can emulate time-since-infection models and allows medical stages such as hospitalization or quarantine to be modelled and forecast. The PyRoss library enables fitting to epidemiological data, as available, using Bayesian parameter inference, so that competing models can be weighed by their evidence. PyRoss allows fully Bayesian forecasts of the impact of idealized NPIs by convolving uncertainties arising from epidemiological data, model choice, parameters, and intrinsic stochasticity. Algorithms to optimize time-dependent NPI scenarios against user-defined cost functions are included. PyRoss's current age-structured compartment framework for well-mixed populations will in future reports be extended to include compartments structured by location, occupation, use of travel networks and other attributes relevant to assessing disease spread and the impact of NPIs. We argue that such compartment models, by allowing social data of arbitrary granularity to be combined with Bayesian parameter estimation for poorly-known disease variables, could enable more powerful and robust prediction than other approaches to detailed epidemic modelling. We invite others to use the PyRoss library for research to address today's COVID-19 crisis, and to plan for future pandemics.
has issue date	2020-05-19(xsd:dateTime)
has license	arxiv
sha1sum (hex)	d9445f0bb21694062b2ab94b778aa4c29d167569
resource representing a document's title	Inference, prediction and optimization of non-pharmaceutical interventions using compartment models: the PyRoss library
resource representing a document's body	covid:d9445f0bb21694062b2ab94b778aa4c29d167569#body_text
is schema:about of	named entity 'ADDRESS' named entity 'PARAMETER' named entity 'PHARMACEUTICAL' named entity 'OPEN' named entity 'SUBDIVISION' named entity 'USING' named entity 'EPIDEMIOLOGICAL' named entity 'PARAMETERS' named entity 'POPULATIONS' named entity 'INCLUDED' named entity 'posterior probability' named entity 'MAP' named entity 'stochastic' named entity 'residence time' named entity 'covariance matrix' named entity 'basic reproductive ratio' named entity 'eigenvalues' named entity 'stochastic' named entity 'cost functions' named entity 'ad libitum' named entity 'Bayesian' named entity 'deterministically' named entity 'parameter estimation' named entity 'pharmaceutical' named entity 'compartment models' named entity 'systems of differential equations' named entity 'infection' named entity 'posterior distribution' named entity 'numpy' named entity 'time series' named entity 'Bayesian' named entity 'growth is exponential' named entity 'deterministic' named entity 'joint distribution' named entity 'agent-based' named entity 'positive or negative' named entity 'return infinity' named entity 'master equation' named entity 'probability' named entity 'master equations' named entity 'epidemic' named entity 'deterministic' named entity 'morbidity' named entity 'toy model' named entity 'deterministic' named entity 'epidemiological' named entity 'cost functional' named entity 'random variable' named entity 'multivariate normal distribution' named entity 'sampling' named entity 'ODE' named entity 'residence time' named entity 'epidemiological' named entity 'epidemiological data' named entity 'MAP' named entity 'Markovian' named entity 'high-dimensional' named entity 'stochastic' named entity 'finite differences' named entity 'contact structure' named entity 'epidemiological' named entity 'random sample' named entity 'Gaussian' named entity 'maximum a posteriori' named entity 'infection' named entity 'epidemic' named entity 'infection' named entity 'epidemic' named entity 'ODEs' named entity '3.6.3'

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