Abstract
State space models, including compartmental models, are used to model physical, biological and social phenomena in a broad range of scientific fields. A common way of representing the underlying processes in these models is as a system of stochastic processes which can be simulated forwards in time. Inference of model parameters based on observed time-series data can then be performed using sequential Monte Carlo techniques. However, using these methods for routine inference problems can be made difficult due to various engineering considerations: allowing model design to change in response to new data and ideas, writing model code which is highly performant, and incorporating all of this with up-to-date statistical techniques. Here, we describe a suite of packages in the R programming language designed to streamline the design and deployment of state space models, targeted at infectious disease modellers but suitable for other domains. Users describe their model in a familiar domain-specific language, which is converted into parallelised C++ code. A fast, parallel, reproducible random number generator is then used to run large numbers of model simulations in an efficient manner. We also provide standard inference and prediction routines, though the model simulator can be used directly if these do not meet the user’s needs. These packages provide guarantees on reproducibility and performance, allowing the user to focus on the model itself, rather than the underlying computation. The ability to automatically generate high-performance code that would be tedious and time-consuming to write and verify manually, particularly when adding further structure to compartments, is crucial for infectious disease modellers. Our packages have been critical to the development cycle of our ongoing real-time modelling efforts in the COVID-19 pandemic, and have the potential to do the same for models used in a number of different domains.
Highlights
To mathematically model a physical or biological process one must develop and test a model, combine it with potentially noisy or poor quality data, and produce high-quality reproducible results in a computationally efficient manner
We can enhance uptake by combining sensible software engineering choices with carefully designed statistical and computational methods. This make design advantages such as speed, unit-tested code and reproducible random number generation as broadly accessible as possible. With these aims in mind, we describe the development of three libraries in the R programming language8, designed to make the implementation of state space models as easy and reliable as possible
For the remainder of this section we focus on stochastic models, continuous time Markov chain (CTMC) formulations of infectious disease transmission models
Summary
To mathematically model a physical or biological process one must develop and test a model, combine it with potentially noisy or poor quality data, and produce high-quality reproducible results in a computationally efficient manner. Frameworks which automate common computational and statistical methods can facilitate some of the complex steps in the process, and come with guarantees of efficiency and reproducibility3,4 – a necessity in modern science, when this science is used in real time to support policy making. Frameworks which automate common computational and statistical methods can facilitate some of the complex steps in the process, and come with guarantees of efficiency and reproducibility3,4 – a necessity in modern science, when this science is used in real time to support policy making5–7 When designing these frameworks, it is generally fair to assume that a typical multi-disciplinary modeller is a domain expert and technically minded, but should not have to become a software engineer in order to develop an efficient implementation. This make design advantages such as speed, unit-tested code and reproducible random number generation as broadly accessible as possible
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