Inference and forecasting phase shift regime of COVID-19 sub-lineages with a Markov-switching model.

Abstract

The occurrences and domination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants are still crucial factors for determining the coronavirus disease 19 (COVID-19) policies. We collected weekly Phylogenetic Assignment of Named Global Outbreak sub-lineages, naming genetically distinct lineages of SARS-CoV-2, including variants of concern, in the United Kingdom, South Africa, South Korea, Denmark, Germany, the United States, and worldwide. This study included 12,296,756 samples of the max share of the sub-lineages from the 33rd week of 2020 to the 40th week of 2022. This study conducted a two-state Markov-switching model to estimate the probability of the phase shift state and predicted the probability of each regime with the Hamilton filter and Kim's smoothing algorithm. We discovered different weekly patterns based on dominant SARS-CoV-2 variants in target area. Due to differences in containment policies and outbreak waves, we observed a time lag in dominant variants in these countries. Using the inferred probability of the phase shift regime for forecasting, it showed significant probabilities that the stable phase will still be stable in the next week. It also showed significant probabilities that the unstable phase will still be unstable in the next week. Our findings present the probability of observing the phase shift regime each week. Until a new SARS-CoV-2 variant occurs, the regime tended to stay with a low probability of phase shift regime. When a new SARS-CoV-2 variant would occur, the regime would immediately react and change the probability. We propose the Markov-switching model to determine COVID-19 policies and predict SARS-CoV-2 variants. IMPORTANCE Using regime-switching models, we attempted to determine whether there is a link between changes in severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) variants and infection waves, as well as forecasting new SARS-Cov-2 variants. We believe that our study makes a significant contribution to the field because it proposes a new approach for forecasting the ongoing pandemic, and the spread of other infectious diseases, using a statistical model which incorporates unpredictable factors such as human behavior, political factors, and cultural beliefs.