Multiple change point clustering of count processes with application to California COVID data

•Analysis of 275-day long time series of county level COVID-19 data in California state.•Multiple change point estimation in the framework of mixture modeling and model-based clustering.•Change point given by gap between logit transformed segments in negative binomial nonhomogeneous Levy process.•Three geographically meaningful clusters, each with several change points indicating the spread and decline of infection.