key: cord-0304850-4few4qfp authors: Lamarca, A. P.; Almeida, L. G. P. d.; Francisco Junior, R. d. S.; Cavalcante, L.; Brustolini, O.; Gerber, A. L.; Guimaraes, A. P. d. C.; Oliveira, T. H. d.; Nascimento, E. R. d. S.; Policarpo, C.; Souza, I. V. d.; Carvalho, E. M. d.; Ribeiro, M. S.; Carvalho, S.; Silva, F. D. d.; Garcia, M. H. d. O.; Souza, L. M. d.; Silva, C. G. d.; Ribeiro, C. L. P.; Cavalcanti, A. C.; Mello, C. M. B. d.; Tanuri, A.; Vasconcelos, A. T. R. title: Phylodynamic analysis of SARS-CoV-2 spread in Rio de Janeiro, Brazil, highlights how metropolitan areas act as dispersal hubs for new variants. date: 2022-01-17 journal: nan DOI: 10.1101/2022.01.17.22269136 sha: 99ccd68327f95a49f9ad579ed1f7ff0c08030175 doc_id: 304850 cord_uid: 4few4qfp During the first semester of 2021, all of Brazil has suffered an intense wave of COVID-19 associated with the Gamma variant. In July, the first cases of Delta variant were detected in the state of Rio de Janeiro. In this work, we have employed phylodynamic methods to analyze more than 1,600 genomic sequences of Delta variant collected until September in Rio de Janeiro to reconstruct how this variant has surpassed Gamma and dispersed throughout the state. After the introduction of Delta, it has initially spread mostly in the homonymous city of Rio de Janeiro, the most populous of the state. In a second stage, dispersal occurred to mid- and long-range cities, which acted as new close-range hubs for spread. We observed that the substitution of Gamma by Delta was possibly caused by its higher viral load, a proxy for transmissibility. This variant turnover prompted a new surge in cases, but with lower lethality than was observed during the peak caused by Gamma. We reason that high vaccination rates in the state of Rio de Janeiro were possibly what prevented a higher number of deaths. The state of Rio de Janeiro, Brazil, has suffered an intense wave of COVID-19 in the first semester of 2021, mostly associated with the Gamma variant (P.1 and its sublineages in PANGO classification) (1). With an estimated population of more than 17.4 million people, the state had a maximum of 31,440 COVID-19 cases and 2,000 deaths from the disease in a week. From mid-May onwards, this number diminished to 13,232 cases and 609 deaths in a week at the end of June. The first cases of Delta variant in Rio de Janeiro were then detected in July, leading to the first communitary outbreak of the variant in Brazil (2) . Since then, Delta has spread across different Brazilian states and became the dominant variant circulating in the country (2) (3) (4) (5) . The state of Rio de Janeiro is the third most populous state of Brazil and the second most dense (399,15 hab/km²). It has the fourth-highest Human Development Index in the country and almost half of this population lives in the homonymous city of Rio de Janeiro. Previous works have shown that population density, proximity to international airports and city infrastructure influence the speed and routes in which SARS-CoV-2 spreads (6) (7) (8) (9) (10) (11) (12) (13) . Understanding how this virus disperses within and between urban regions is crucial to elaborate efficient and science-driven public health policies to contain the pandemic. The continued genomic surveillance of SARS-CoV-2 lineages conducted in the state of Rio de Janeiro (14) is a rare opportunity to investigate and monitor the dispersal of the Delta variant since its first introduction. In this work, we employed phylodynamic methods to analyze more than 1,600 Delta genomes collected between July and September and inferred the dispersal patterns and routes of the variant in the state. Epidemiological analyses -Daily number of confirmed COVID-19 cases, deaths and people vaccinated were obtained from the National Immunization Program (Programa Nacional de Imunização, PNI), esus-VE and SIVEP-Gripe databases through the COVID-19 portal All newly sequenced and assembled genomes are publicly available at GISAID (Table S1) Evolutionary analyses and phylogeographic reconstruction -We obtained from the GISAID database all genomes classified as lineage B.1.617.2 or its sublineages (Delta variant) that were submitted until October 1 st and collected in the state of Rio de Janeiro, Brazil. We removed from this dataset any sequence that did not contain complete date information, was shorter than 29,000 pb, had more than 1% of Ns, more than 0,05% of unique amino acid mutations ("high-coverage" in GISAID) or didn't have the locality/city information. We added our newly sequenced genomes collected in the state, totalling 1602 genome sequences. These sequences were independently aligned to the WH01 (EPI ISL 406798) sequence from Wuhan, China using MAFFT v.7 (16) with the --addfragments option. All genomes' 3' and 5' ends were trimmed using the SeqKit toolkit (17) . Maximum likelihood trees were then inferred with IQTREE2 (18) using the GTR+F+I+G4 model selected by the ModelFinder algorithm (19) with "-mset mrbayes" option and 1000 ultrafast-bootstrap replicates (20) . The outgroup (WH01) was then removed and root-to-tip distances were calculated using TempEst. Using Cook's distance, we removed from the alignment sequences that most influenced the correlation between root-to-tip and sampling date. This final dataset contained 1512 sequences and was used in all subsequent analyses. A new maximum likelihood tree was generated using the same parameters as before. This new tree was used as a fixed topology in the subsequent analyses (divergence dating and phylogeography). . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted January 17, 2022. ; https://doi.org/10.1101/2022.01.17.22269136 doi: medRxiv preprint We then moved to investigate whether the outbreak of Delta in Rio de Janeiro was originated by one or multiple introductions of the variant in the state. As representative sequences of the global diversity, we used the genomes belonging to the Delta clade in the SARS-CoV-2 global phylogeny (https://github.com/nextstrain/ncov) generated through the Nextstrain pipeline (21) . We further tested the origin and monophyly of lineages AY.99.1 and AY.99.2 by obtaining from GISAID the 20 oldest sequences of each of these lineages that were not from the state of Rio de Janeiro (ten global sequences and ten from Brazil) and adding them to the background dataset. The background genomes were combined to our dataset from Rio de Janeiro, aligned to the WH01 genome and the 3' and 5' ends of the new sequences were trimmed. A maximum likelihood tree was inferred as described before . Divergence dates were estimated using BEAST v.1.10.4 employing the strict clock model with a uniform prior (max substitution rate of 1e-3, min 6e-8), the GTR substitution model with empirical frequencies and the Gamma+Invariant sites model, and the Coalescent-Exponential Growth tree prior. The MCMC was run through a chain of 100,000,000 with sampling every 10,000th. The consensus tree was summarized with Tree Annotator and used as the fixed topology and branch lengths for the phylogeographic reconstruction of the spread of SARS-CoV-2 in Rio de Janeiro. To accomplish this, we used the BEAST software and the Relaxed Random Walk model with Cauchy's distribution on coordinates randomly selected within each sample's collection city. The MCMC ran through a chain of 100,000,000 with sampling every 10,000th. Dispersal routes were extracted from the consensus tree using the seraphim package and plotted using the ggplot2 package, both in R software. Finally, we also inferred divergence dates and dispersal using TreeTime (22) to confirm our findings. For divergence dating, we kept the root of the maximum likelihood tree previously inferred (--keeproot), used the "skyline" coalescent model (--coalescent skyline) and assumed correlation between root-to-tip distances (--covariation). For the phylogeographic analysis, we used the "mugration" model inference with the sampling city as the discrete character to be analyzed. The results were analyzed and plotted using ggtree and ggplot packages in R. . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted January 17, 2022. ; https://doi.org/10.1101/2022.01.17.22269136 doi: medRxiv preprint On June 16th of 2021, the first case of the Delta variant was confirmed in Rio de Janeiro, and a fast increase of the variant's frequency in the state was reported (2) . At the time, Rio de Janeiro was on the decline of COVID-19 cases caused by the outbreak of Gamma variant in the country (14, 23) . The fast spread of Delta observed in Rio de Janeiro incited concern for a new COVID-19 surge. Analyzing epidemiological data publicly available, we observed that there was indeed an increase of cases in the state (Fig. 1A) , with a maximum of 23,086 cases in a week at the beginning of August (week 32). This number corresponds to approximately 73% of cases from the first peak (end of March, week 11) of the bimodal wave caused by Gamma and is very similar to the number of cases in the second peak (early May, week 19) . However, the increase in deaths caused by this new variant was much smaller, with the 890 lives lost (week 33) being less than half of the maximum observed in the first semester of 2,021 (n = 2,016, week 11; Fig. 1B ). While recent research suggests that Delta causes a higher number of hospitalizations and aggravated symptoms than previous lineages (24,25), we believe the advances in vaccination throughout the state has reduced the lethality from 7,6% (late March, week 13) to the approximately 3% at the peak of the Delta surge. Interestingly, in the last four weeks of the temporal series analyzed, there was an increase in lethality (max 6,5%) even though cases and deaths were decreasing. This increase was observed in all age groups above 40 years and infants until 4 years ( Fig. 1C) and we could not identify the factor determining it. Age group proportions in the number of cases was similar throughout 2021 while the distribution in deaths saw first a decrease in the proportion of older age groups followed by normalization, possibly caused by health authorities prioritizing vaccination in such groups first (Fig. S2, Fig. 1D ). To investigate how Delta surpassed Gamma in the state of Rio de Janeiro, we compared the relative quantification of viral load (RQVL = 2 -∆CT ) of samples infected by variants circulating in the period analyzed in this study. We observed that samples from Delta exhibited significantly higher RQVL values than Gamma (Wilcoxon test, p.value = 0.01; Fig. S3a ). While a few samples from Gamma . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted January 17, 2022. ; https://doi.org/10.1101/2022.01.17.22269136 doi: medRxiv preprint accumulated more than 90% of the circulating viruses, more samples from Delta were required to represent the same amount of the viral load than Gamma (Fig. S3b) . For example, 1% of the samples (n = 4) classified as Gamma harbored approximately 96% of all viruses circulating in the state at the analyzed period. On the other hand, 1% of Delta samples corresponded to 42% of the viral load suggesting an elevated number of supercarriers within Delta lineage. Viral load of Delta tends to be high until the 7th day, while viral load from Gamma samples in our database seems to decrease from the 5th day (Fig. S4) . The Delta variant is known for its higher transmissibility (32, 33) These results consolidate the important role of large and dense urbanized areas as dispersal hubs immediately after introducing a new SARS-CoV-2 variant or lineage (6) (7) (8) (9) (10) (11) (12) (13) . Therefore, identification and surveillance of these hubs are of fundamental value to early control new variants that may emerge in the future. The results also suggest that non-medical interventions such as mass screening (37) (38) (39) (40) , use of masks (41) (42) (43) , social distancing and lockdowns (44) (45) (46) in metropolitan areas might result in better long-term effects on pandemic control than when applied on small cities, because it may reduce the number of seeding events on small cities (47) (48) (49) . . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted January 17, 2022. ; https://doi.org/10.1101/2022.01.17.22269136 doi: medRxiv preprint The authors declare that there are no conflicts of interest. This work was developed in the frameworks of Corona-ômica-RJ (FAPERJ = E-26/210.179/2020). The study was approved by the Ethics Committee (30161620.0.1001.5257 and 34025020.0.0000.5257). Research protocol was approved without informed consent in accordance with Brazilian National Health Council's Resolution 510/2016. All samples were residual COVID-19 clinical diagnostic samples de-identified before receipt by the researchers. . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted January 17, 2022. . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted January 17, 2022. ; https://doi.org/10.1101/2022.01.17.22269136 doi: medRxiv preprint . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted January 17, 2022. . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted January 17, 2022. . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted January 17, 2022. . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted January 17, 2022. . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted January 17, 2022. . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted January 17, 2022. File S1. Evolutionary tree in newick format generated using Delta genomes from Rio de Janeiro, Brazil, and around the world. . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. 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Phys Rev Research Lockdowns result in changes in human mobility which may impact the epidemiologic dynamics of SARS-CoV-2 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity We would like to thank all the authors and administrators of the GISAID database, which allowed this study of genomic epidemiology to be conducted properly. A complete list acknowledging the authors publishing data used in this study can be found in Table S2 .