RESUMEN
The SARS-CoV-2 variants of concern (VOCs) Delta and Omicron spread globally during mid and late 2021, respectively, with variable impact according to the immune population landscape. In this study, we compare the dissemination dynamics of these VOCs in the Amazonas state, one of Brazils most heavily affected regions. We sequenced the virus genome from 4,128 patients collected in Amazonas between July 1st, 2021 and January 31st, 2022 and investigated the lineage replacement dynamics using a phylodynamic approach. The VOCs Delta and Omicron displayed similar patterns of phylogeographic spread but significantly different epidemic dynamics. The Delta and Omicron epidemics were fueled by multiple introduction events, followed by the successful establishment of a few local transmission lineages of considerable size that mainly arose in the Capital, Manaus. The VOC Omicron spread and became dominant much faster than the VOC Delta. We estimate that under the same epidemiological conditions, the average Re of Omicron was [~]3.3 times higher than that of Delta and the average Re of the Delta was [~]1.3 times higher than that of Gamma. Furthermore, the gradual replacement of Gamma by Delta occurred without an upsurge of COVID-19 cases, while the rise of Omicron fueled a sharp increase in SARS-CoV-2 infection. The Omicron wave displayed a shorter duration and a clear decoupling between the number of SARS-CoV-2 cases and deaths compared with previous (B.1.* and Gamma) waves in the Amazonas state. These findings suggest that the high level of hybrid immunity (infection plus vaccination) acquired by the Amazonian population by mid-2021 was able to limit the spread of the VOC Delta and was also probably crucial to curb the number of severe cases, although not the number of VOC Omicron new infections.
RESUMEN
In late March 2020, SARS-CoV-2 arrived in Manaus, Brazil, and rapidly developed into a large-scale epidemic that collapsed the local health system, and resulted in extreme death rates. Several key studies reported that [~]76% of residents of Manaus were infected (attack rate AR[~=]76%) by October 2020, suggesting protective herd immunity had been reached. Despite this, in November an unexpected second wave of COVID-19 struck again, and proved to be larger than the first creating a catastrophe for the unprepared population. It has been suggested that this could only be possible if the second wave was driven by reinfections. Here we use novel methods to model the epidemic from mortality data, evaluate the impact of interventions, in order to provide an alternative explanation as to why the second wave appeared. The method fits a "flexible" reproductive number R0(t) that changes over the epidemic, and found AR[~=]30-34% by October 2020, for the first wave, which is far less than required for herd immunity, yet in-line with recent seroprevalence estimates. The two-strain model provides an accurate fit to observed epidemic datasets, and finds AR[~=]70% by March 2021. Using genomic data, the model estimates transmissibility of the new P.1 virus lineage, as 1.9 times as transmissible as the non-P1. The model thus provides a reasonable explanation for the two-wave dynamics in Manaus, without the need to rely on reinfections which until now have only been found in small numbers in recent surveillance efforts. SignificanceThis paper explores the concept of herd immunity and approaches for assessing attack rate during the explosive outbreak of COVID-19 in the city of Manaus, Brazil. The event has been repeatedly used to exemplify the epidemiological dynamics of the disease and the phenomenon of herd immunity, as claimed to be achieved by the end of the first wave in October 2020. A novel modelling approach reconstructs these events, specifically in the presence of interventions. The analysis finds herd immunity was far from being attained, and thus a second wave was readily possible, as tragically occurred in reality. Based on genomic data, the multi-strain model gives insights on the new highly transmissible variant of concern P.1 and role of reinfection.