Disentangling the Dynamics of VOCs and VOIs during the COVID-19 Pandemic in Peru using Bayesian Phylogenetics
Abstract At over 0.6% of the population, Peru has the highest SARS-CoV-2 mortality rate in the world. Much effort to sequence genomes was done in this country since mid-2020. However, an adequate analysis of the dynamics of the variants of concern and interest (VOCIs) is missing. We performed Bayesian phylodynamic analyses using different sampling approaches to determine the origin of the VOCIs in Peru, as well as their population dynamics. The COVID-19 pandemic in Peru was characterized by three waves. We showed that Lambda emerged in Peru during the first wave (April-July, 2020) but it did not reach high frequencies until the second wave. Alpha and Gamma were imported to Peru between the first and second waves (October-December, 2020). The gamma variant reached Peru from Brazil, whereas Alpha arrived at Peru from the United Kingdom. Effective population size (Ne) and effective reproductive number (Rt) suggest that Lambda, followed by Gamma, dominated the second wave of COVID-19 in Peru whereas Alpha was not very prevalent. During this wave, Lambda dominated all the Peruvian regions except the Northeast, where Gamma dominated. Migration patterns of Lambda and Gamma between the Northeast and other regions indicate that the different VOCI prevalence in the Northeast was not due to the isolation of this region. Also, during the second wave, Mu and Delta arrived in Peru (February-May, 2021) from the USA and the United Kingdom, respectively. Mu never reached high Rt nor Ne; on the other hand, at the final of the second wave, Delta showed Rt values higher than other VOCIs, beginning to increase its Ne and replacing Lambda and Gamma. Finally, Omicron reached Peru just before the beginning of the third wave (October-December, 2021) from the USA. Omicron had a high Rt since its arrival to Peru, originating new lineages and rapidly increasing its Ne which caused the third wave. In summary, we were able to determine important patterns of virus dynamics in Peru using Bayesian Phylogenetics. Besides, we demonstrated that Bayesian phylodynamics with a portion of the available genomes were sufficient to reconstruct the COVID-19 pandemic in Peru.