The SARS-CoV-2 has infected almost 200 million people worldwide by July 2021 and the pandemic has been characterized by infection waves of viral lineages showing distinct fitness profiles. The simultaneous infection of a single individual by two distinct SARS-CoV-2 lineages provides a window of opportunity for viral recombination and the emergence of new lineages with differential phenotype. Several hundred SARS-CoV-2 lineages are currently well characterized but two main factors have precluded major coinfection/codetection analysis thus far: i) the low diversity of SARS-CoV-2 lineages during the first year of the pandemic which limited the identification of lineage defining mutations necessary to distinguish coinfecting viral lineages; and the ii) limited availability of raw sequencing data where abundance and distribution of intrasample/intrahost variability can be accessed. Here, we have put together a large sequencing dataset from Brazilian samples covering a period of 18 May 2020 to 30 April 2021 and probed it for unexpected patterns of high intrasample/intrahost variability. It enabled us to detect nine cases of SARS-CoV-2 coinfection with well characterized lineage-defining mutations. In addition, we matched these SARS-CoV-2 coinfections with spatio-temporal epidemiological data confirming their plausibility with the co-circulating lineages at the timeframe investigated. These coinfections represent around 0.61% of all samples investigated. Although our data suggests that coinfection with distinct SARS-CoV-2 lineages is a rare phenomenon, it is likely an underestimation and coinfection rates warrants further investigation. DATA SUMMARYThe raw fastq data of codetection cases are deposited on gisaid.org and correlated to gisaid codes: EPI_ISL_1068258, EPI_ISL_2491769, EPI_ISL_2491781, EPI_ISL_2645599, EPI_ISL_2661789, EPI_ISL_2661931, EPI_ISL_2677092, EPI_ISL_2777552, EPI_ISL_3869215. Supplementary data are available on https://doi.org/10.6084/m9.figshare.16570602.v1. The workflow code used in this study is publicly available on: https://github.com/dezordi/IAM_SARSCOV2.