Not being intimately familiar with the limitations of Kalman filters, how susceptible to bias is this approach when hidden states exist? In the case of COVID-19, the asymptomatic cases may be modeled as such, or not, depending on the approach. Also, the incubation and convalescent phase could also present a challenge given that patients can still transmit the virus.
It would be helpful to understand how Kalman filtering approach compares Markov chains, for example.
edit: Could Kalman filters be used to retroactively mark/flag/detect the introduction of a new strain of the same virus? If so, this could prove to be a novel way to quantify the clinically meaningful mutation rate - that is, the rate at which the virus mutates sufficiently to infect a new sub-population (or perhaps re-infect existing ones).
It would be helpful to understand how Kalman filtering approach compares Markov chains, for example.
edit: Could Kalman filters be used to retroactively mark/flag/detect the introduction of a new strain of the same virus? If so, this could prove to be a novel way to quantify the clinically meaningful mutation rate - that is, the rate at which the virus mutates sufficiently to infect a new sub-population (or perhaps re-infect existing ones).