Modeling COVID-19 Infection Rates by Regime-Switching Unobserved Components Models

Modeling COVID-19 Infection Rates by Regime-Switching Unobserved Components Models

Blog Article

The COVID-19 pandemic is characterized by a recurring sequence of peaks and troughs.This article proposes a regime-switching unobserved components (UC) approach to model the trend of COVID-19 4 post backdrop stand infections as a function of this ebb and flow pattern.Estimated regime probabilities indicate the prevalence of either an infection up- or down-turning regime for every day of the observational period.This method provides an intuitive real-time analysis of the state of the pandemic as well as a tool for identifying structural changes ex post.

We find that when applied to U.S.data, the model closely tracks regime changes caused by viral mutations, policy intra-trac3 interventions, and public behavior.