Retrospective Insights of the COVID-19 Epidemic in the Major Latin American City, São Paulo, Southeastern Brazil

São Paulo is the financial center of Brazil, with a population of over 12 million, that receives travelers from all over the world for business and tourism. It was the first city in Brazil to report a case of COVID-19 that rapidly spread across the city despite the implementation of the restriction measures. Despite many reports, much is still unknown regarding the genomic diversity and transmission dynamics of this virus in the city of São Paulo. Thus, in this study, we provide a retrospective overview of the COVID-19 epidemic in São Paulo City, Southeastern, Brazil, by generating a total of 9995 near-complete genome sequences from all the city’s different macro-regions (North, West, Central, East, South, and Southeast). Our analysis revealed that multiple independent introduction events of different variants (mainly Gamma, Delta, and Omicron) occurred throughout time. Additionally, our estimates of viral movement within the different macro-regions further suggested that the East and the Southeast regions were the largest contributors to the Gamma and Delta viral exchanges to other regions. Meanwhile, the North region had a higher contribution to the dispersion of the Omicron variant. Together, our results reinforce the importance of increasing SARS-CoV-2 genomic monitoring within the city and the country to track the real-time evolution of the virus and to detect earlier any eventual emergency of new variants of concern that could undermine the fight against COVID-19 in Brazil and worldwide.

[1]  E. C. Mattos,et al.  Genomic epidemiology of the SARS-CoV-2 epidemic in Brazil , 2022, Nature Microbiology.

[2]  S. Kashima,et al.  SARS-CoV-2 epidemic in Brazil: how the displacement of variants has driven distinct epidemic waves , 2022, Virus Research.

[3]  E. C. Mattos,et al.  SARS‐COV‐2 genomic monitoring in the state of São Paulo unveils two emerging AY.43 sublineages , 2022, Journal of medical virology.

[4]  M. Kraemer,et al.  Rapid epidemic expansion of the SARS-CoV-2 Omicron variant in southern Africa , 2021, Nature.

[5]  A. T. Vasconcelos,et al.  The Emergence of the New P.4 Lineage of SARS-CoV-2 With Spike L452R Mutation in Brazil , 2021, Frontiers in Public Health.

[6]  Nam-Joo Lee,et al.  Genomic epidemiology reveals the reduction of the introduction and spread of SARS-CoV-2 after implementing control strategies in Republic of Korea, 2020 , 2021, Virus evolution.

[7]  M. Nicolelis,et al.  The impact of super-spreader cities, highways, and intensive care availability in the early stages of the COVID-19 epidemic in Brazil , 2021, Scientific Reports.

[8]  M. Suchard,et al.  Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil , 2021, Science.

[9]  Nuno R. Faria,et al.  Resurgence of COVID-19 in Manaus, Brazil, despite high seroprevalence , 2021, The Lancet.

[10]  D. Cyranoski Alarming COVID variants show vital role of genomic surveillance , 2021, Nature.

[11]  A. Tanuri,et al.  Genomic Characterization of a Novel SARS-CoV-2 Lineage from Rio de Janeiro, Brazil , 2020, Journal of Virology.

[12]  Jacqui Wise,et al.  Covid-19: New coronavirus variant is identified in UK , 2020, BMJ.

[13]  Bruno Siciliano,et al.  The Impact of COVID-19 Partial Lockdown on Primary Pollutant Concentrations in the Atmosphere of Rio de Janeiro and São Paulo Megacities (Brazil) , 2020, Bulletin of Environmental Contamination and Toxicology.

[14]  Samir Bhatt,et al.  Evolution and epidemic spread of SARS-CoV-2 in Brazil , 2020, Science.

[15]  Trevor Bedford,et al.  Genomic surveillance reveals multiple introductions of SARS-CoV-2 into Northern California , 2020, Science.

[16]  E. Holmes,et al.  Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding , 2020, The Lancet.

[17]  G. Gao,et al.  A Novel Coronavirus from Patients with Pneumonia in China, 2019 , 2020, The New England journal of medicine.

[18]  Olga Chernomor,et al.  IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era , 2019, bioRxiv.

[19]  Trevor Bedford,et al.  Nextstrain: real-time tracking of pathogen evolution , 2017, bioRxiv.

[20]  Richard A Neher,et al.  TreeTime: Maximum-likelihood phylodynamic analysis , 2017, bioRxiv.

[21]  P. Sagulenko Maximum likelihood phylodynamic analysis , 2017 .

[22]  Andrew Rambaut,et al.  Exploring the temporal structure of heterochronous sequences using TempEst (formerly Path-O-Gen) , 2016, Virus evolution.

[23]  Christina A. Cuomo,et al.  Pilon: An Integrated Tool for Comprehensive Microbial Variant Detection and Genome Assembly Improvement , 2014, PloS one.

[24]  Björn Usadel,et al.  Trimmomatic: a flexible trimmer for Illumina sequence data , 2014, Bioinform..

[25]  Heng Li,et al.  A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data , 2011, Bioinform..

[26]  Gonçalo R. Abecasis,et al.  The Sequence Alignment/Map format and SAMtools , 2009, Bioinform..

[27]  Richard Durbin,et al.  Sequence analysis Fast and accurate short read alignment with Burrows – Wheeler transform , 2009 .