Differentiated uniformization: A new method for inferring Markov chains on combinatorial state spaces including stochastic epidemic models

Code

• github.com/spang-lab/TenSIR
  OfficialIn papernone★ 6

Abstract

Motivation: We consider continuous-time Markov chains that describe the stochastic evolution of a dynamical system by a transition-rate matrix Q which depends on a parameter . Computing the probability distribution over states at time t requires the matrix exponential (tQ), and inferring from data requires its derivative \!(tQ)/. Both are challenging to compute when the state space and hence the size of Q is huge. This can happen when the state space consists of all combinations of the values of several interacting discrete variables. Often it is even impossible to store Q. However, when Q can be written as a sum of tensor products, computing (tQ) becomes feasible by the uniformization method, which does not require explicit storage of Q. Results: Here we provide an analogous algorithm for computing \!(tQ)/, the differentiated uniformization method. We demonstrate our algorithm for the stochastic SIR model of epidemic spread, for which we show that Q can be written as a sum of tensor products. We estimate monthly infection and recovery rates during the first wave of the COVID-19 pandemic in Austria and quantify their uncertainty in a full Bayesian analysis. Availability: Implementation and data are available at https://github.com/spang-lab/TenSIR.

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