  This paper considers the problem of joint change detection and identification
assuming multiple composite postchange hypotheses. We propose a multihypothesis
changepoint detection-identification procedure that controls the probabilities
of false alarm and wrong identification. We show that the proposed procedure is
asymptotically minimax and pointwise optimal, minimizing moments of the
detection delay as probabilities of false alarm and wrong identification
approach zero. The asymptotic optimality properties hold for general stochastic
models with dependent observations. We illustrate general results for
detection-identification of changes in multistream Markov ergodic processes. We
consider several examples, including an application to rapid
detection-identification of COVID-19 in Italy. Our proposed sequential
algorithm allows much faster detection of COVID-19 than standard methods.
