Branching Processes in Random Environments with Thresholds

Abstract: Motivated by applications to COVID dynamics, we describe a branching process in random environments model ${Z_n}$ whose characteristics change when crossing upper and lower thresholds. This introduces a cyclical path behavior involving periods of increase and decrease leading to supercritical and subcritical regimes. Even though the process is not Markov, we identify subsequences at random time points ${(\tau_j, \nu_j)}$ - specifically the values of the process at crossing times, {\it{viz.}}, ${(Z_{\tau_j}, Z_{\nu_j})}$ - along which the process retains the Markov structure. Under mild moment and regularity conditions, we establish that the subsequences possess a regenerative structure and prove that the limiting normal distribution of the growth rates of the process in supercritical and subcritical regimes decouple. For this reason, we establish limit theorems concerning the length of supercritical and subcritical regimes and the proportion of time the process spends in these regimes. As a byproduct of our analysis, we explicitly identify the limiting variances in terms of the functionals of the offspring distribution, threshold distribution, and environmental sequences.