I’m inclined to place problems like this in the general frame of state-space modeling [1,2], and then attempt to describe the various other approaches as special cases. For example, do the stochastic volatility models mentioned in [2] subsume the other approaches being discussed here?
The way computational efficiency has motivated WiSER is interesting. I would tend to appeal to Gustafson’s Law in approaching these massive patient registries with their distinct, non-interacting individuals. I would estimate n independent state-space models to obtain patient-level parameter vector estimates which could then be subject to a further stages of analysis such as visualization and regression. The problem of “interpretability” ideally is solved by bespoke model construction ex ante, rather than by grafting an ‘interpretation’ onto a generic model post hoc. Under such a philosophy, one would confront the zero-activity periods ‘head-on’ with a latent state variable having associated Markov transition probabilities (interpretation: sedentariness), which has the effect of bringing a crucial scientific question into clear view rather than sweeping it under the rug of a generic modeling approach.
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Künsch HR. Particle filters. Bernoulli. 2013;19(4):1391-1403. doi:10.3150/12-BEJSP07
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Kantas N, Doucet A, Singh SS, Maciejowski J, Chopin N. On Particle Methods for Parameter Estimation in State-Space Models. Statist Sci. 2015;30(3):328-351. doi:10.1214/14-STS511