CORONAVIRUS Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil Nuno R. Faria1,2,3,4*†, Thomas A. Mellan1,2†, Charles Whittaker1,2†, Ingra M. Claro3,5†, Darlan da S. Candido3,4†, Swapnil Mishra1,2†, Myuki A. E. Crispim6,7, Flavia C. S. Sales3,5, Iwona Hawryluk1,2, John T. McCrone8, Ruben J. G. Hulswit9, Lucas A. M. Franco3,5, Mariana S. Ramundo3,5, Jaqueline G. de Jesus3,5, Pamela S. Andrade10, Thais M. Coletti3,5, Giulia M. Ferreira11, Camila A. M. Silva3,5, Erika R. Manuli3,5, Rafael H. M. Pereira12, Pedro S. Peixoto13, Moritz U. G. Kraemer4, Nelson Gaburo Jr.14, Cecilia da C. Camilo14, Henrique Hoeltgebaum15, William M. Souza16, Esmenia C. Rocha3,5, Leandro M. de Souza3,5, Mariana C. de Pinho3,5, Leonardo J. T. Araujo17, Frederico S. V. Malta18, Aline B. de Lima18, Joice do P. Silva18, Danielle A. G. Zauli18, Alessandro C. de S. Ferreira18, Ricardo P. Schnekenberg19, Daniel J. Laydon1,2, Patrick G. T. Walker1,2, Hannah M. Schlüter15, Ana L. P. dos Santos20, Maria S. Vidal20, Valentina S. Del Caro20, Rosinaldo M. F. Filho20, Helem M. dos Santos20, Renato S. Aguiar21, José L. Proença-Modena22, Bruce Nelson23, James A. Hay24,25, Mélodie Monod15, Xenia Miscouridou15, Helen Coupland1,2, Raphael Sonabend1,2, Michaela Vollmer1,2, Axel Gandy15, Carlos A. Prete Jr.26, Vitor H. Nascimento26, Marc A. Suchard27, Thomas A. Bowden9, Sergei L. K. Pond28, Chieh-Hsi Wu29, Oliver Ratmann15, Neil M. Ferguson1,2, Christopher Dye4, Nick J. Loman30, Philippe Lemey31, Andrew Rambaut8, Nelson A. Fraiji6,32, Maria do P. S. S. Carvalho6,33, Oliver G. Pybus4,34‡, Seth Flaxman15‡, Samir Bhatt1,2,35*‡, Ester C. Sabino3,5*‡ Cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in Manaus, Brazil, resurged in late 2020 despite previously high levels of infection. Genome sequencing of viruses sampled in Manaus between November 2020 and January 2021 revealed the emergence and circulation of a novel SARS-CoV-2 variant of concern. Lineage P.1 acquired 17 mutations, including a trio in the spike protein (K417T, E484K, and N501Y) associated with increased binding to the human ACE2 (angiotensin-converting enzyme 2) receptor. Molecular clock analysis shows that P.1 emergence occurred around mid-November 2020 and was preceded by a period of faster molecular evolution. Using a two-category dynamical model that integrates genomic and mortality data, we estimate that P.1 may be 1.7- to 2.4-fold more transmissible and that previous (non-P.1) infection provides 54 to 79% of the protection against infection with P.1 that it provides against non-P.1 lineages. Enhanced global genomic surveillance of variants of concern, which may exhibit increased transmissibility and/or immune evasion, is critical to accelerate pandemic responsiveness. B razil has experienced high mortality during the COVID-19 pandemic, record- ing >300,000 deaths and >13 million reported cases, as of March 2021. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and disease burden have been highly variable across the country, with the state of Amazonas in north Brazil being the worst-affected region (1). Serological surveillance of blood donors in Manaus, the capital city of Amazonas and the largest city in the Amazon region, has suggested >67% cumulative attack rates by October 2020 (2). Similar but slightly lower seroprevalences have also been reported for cities in neighboring regions (3, 4). However, the level of previous infection in Manaus was clearly not sufficient to prevent a rapid resurgence in SARS-CoV-2 transmission and mortality there during late 2020 and early 2021 (5), which has placed sub- stantial pressure on the city’s health care system. Here, we show that the second wave of in- fection in Manaus was associated with the emergence and rapid spread of a new SARS- CoV-2 lineage of concern, named lineage P.1. The lineage carries a distinctive constellation of mutations (table S1), including several that have been previously determined to be of vi- rological importance (6–10) and that are lo- cated in the spike protein receptor binding domain (RBD), the region of the virus involved in recognition of the angiotensin-converting enzyme-2 (ACE2) cell surface receptor (11). Using genomic data, structure-basedmapping of mu- tations of interest onto the spike protein, and dynamical epidemiology modeling of genomic and mortality data, we investigated the emer- gence of the P.1 lineage and explored epi- demiological explanations for the resurgence of COVID-19 in Manaus. Identification and nomenclature of the P.1 lineage in Manaus In late 2020, two SARS-CoV-2 lineages of con- cern were discovered through genomic sur- veillance, both characterized by sets of notable mutations: lineage B.1.351, first reported in South Africa (12), and lineage B.1.1.7, detected in the UK (13). Both variants have transmitted rapidly in the countries where they were dis- covered and spread to other regions (14, 15). Analyses indicate that B.1.1.7 has higher transmissibility and causes more severe illness as compared with those of previously circu- lating lineages in the UK (1, 16, 17). After a rapid increase in hospitalizations in Manaus caused by severe acute respiratory infection (SARI) in December 2020 (Fig. 1A), we focused ongoing SARS-CoV-2 genomic surveillance (2, 18–22) on recently collected samples from the city (supplementary mate- rials, materials and methods, and table S2). Before this, only seven SARS-CoV-2 genome sequences fromAmazonas were publicly avail- able (SARS-CoV-2 was first detected in Manaus RESEARCH Faria et al., Science 372, 815–821 (2021) 21 May 2021 1 of 7 1MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK. 2The Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK. 3Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil. 4Department of Zoology, University of Oxford, Oxford, UK. 5Departamento de Moléstias Infecciosas e Parasitárias, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil. 6Fundação Hospitalar de Hematologia e Hemoterapia, Manaus, Brazil. 7Diretoria de Ensino e Pesquisa, Fundação Hospitalar de Hematologia e Hemoterapia, Manaus, Brazil. 8Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK. 9Division of Structural Biology, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK. 10Departamento de Epidemiologia, Faculdade de Saúde Pública da Universidade de São Paulo, Sao Paulo, Brazil. 11Laboratório de Virologia, Instituto de Ciências Biomédicas, Universidade Federal de Uberlândia, Uberlândia, Brazil. 12Institute for Applied Economic Research–Ipea, Brasília, Brazil. 13Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil. 14DB Diagnósticos do Brasil, São Paulo, Brazil. 15Department of Mathematics, Imperial College London, London, UK. 16Virology Research Centre, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil. 17Laboratory of Quantitative Pathology, Center of Pathology, Adolfo Lutz Institute, São Paulo, Brazil. 18Instituto Hermes Pardini, Belo Horizonte, Brazil. 19Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK. 20CDL Laboratório Santos e Vidal, Manaus, Brazil. 21Departamento de Genética, Ecologia e Evolução, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil. 22Laboratory of Emerging Viruses, Department of Genetics, Evolution, Microbiology, and Immunology, Institute of Biology, University of Campinas (UNICAMP), São Paulo, Brazil. 23Instituto Nacional de Pesquisas da Amazônia, Manaus, Brazil. 24Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA. 25Center for Communicable Disease Dynamics, Harvard T. H. Chan School of Public Health, Boston, MA, USA. 26Departamento de Engenharia de Sistemas Eletrônicos, Escola Politécnica da Universidade de São Paulo, São Paulo, Brazil. 27Department of Biomathematics, Department of Biostatistics, and Department of Human Genetics, University of California, Los Angeles, CA, USA. 28Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA. 29Mathematical Sciences, University of Southampton, Southampton, UK. 30Institute for Microbiology and Infection, University of Birmingham, Birmingham, UK. 31Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium. 32Diretoria Clínica, Fundação Hospitalar de Hematologia e Hemoterapia do Amazonas, Manaus, Brazil. 33Diretoria da Presidência, Fundação Hospitalar de Hematologia e Hemoterapia do Amazonas, Manaus, Brazil. 34Department of Pathobiology and Population Sciences, The Royal Veterinary College, London, UK. 35Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark. †These authors contributed equally to this work. ‡These authors contributed equally to this work. *Corresponding author. Email: n.faria@imperial.ac.uk (N.R.F.); samir.bhatt@sund.ku.dk (S.B.); sabinoec@usp.br (E.S.C.) D o w n lo ad ed fro m h ttp s://w w w .scien ce.o rg o n Jan u ary 2 2 , 2 0 2 5 on 13March2020) (19, 23).We sequenced SARS- CoV-2 genomes from 184 samples frompatients seeking COVID-19 testing in two diagnostic laboratories inManaus betweenNovember and December 2020, using the ARTIC V3 multi- plexed amplicon scheme (24) and theMinION sequencing platform. Because partial genome sequences can provide useful epidemiological information, particularly regarding virus ge- netic diversity and lineage composition (25), we harnessed information from partial (n = 41 viral sequences, 25 to 75% genome coverage), as well as near-complete (n= 95 viral sequences, 75 to 95%) and complete (n=48 viral sequences, ≥95%) sequences from Manaus (figs. S1 to S4), together with other available and published ge- nomes fromBrazil for context. Viral lineages were classified by using the Pangolin (26) software tool (http://pangolin.cog-uk.io), nextclade (https://clades. nextstrain.org), and standard phylogenetic analy- sis using complete reference genomes. Our early data indicated the presence of a novel SARS-CoV-2 lineage in Manaus that contained 17 amino acid changes (including 10 in the spike protein), three deletions, four synonymous mutations, and a four–base-pair nucleotide insertion compared with the most closely related available sequence (GISAID ID: EPI_ISL_722052) (Fig. 1B; lineage-definingmu- tations can be found in table S1) (27). This lineage was given a new designation, P.1, on the basis that (i) it is phylogenetically and genetically distinct from ancestral viruses, (ii) associated with rapid spread in a new area, and (iii) carries a constellation of mutations that may have phenotypic relevance (26). Phy- logenetic analysis indicated that P.1—and an- other lineage, P.2 (19)—were descendants of lineage B.1.1.28 that was first detected in Brazil in early March 2020 (Fig. 1B). Our preliminary resultswere sharedwith local teams on 10 January 2021 andpublished online on 12 January 2021 (27). Concurrently, cases of SARS-CoV-2 P.1 infec- tion were reported in Japan in travelers from Amazonas (28). As of 24 February 2021, P.1 had been confirmed in six Brazilian states, which in total received >92,000 air passengers fromManaus in November 2020 (Fig. 1C). Ge- nomic surveillance first detected lineage P.1 on 6 December 2020 (Fig. 1A), after which the frequency of P.1 relative to other lineages increased rapidly in the tested samples from Manaus (Fig. 1D; lineage frequency informa- tion can be found in fig. S5). Retrospective ge- nome sequencing might be able to recover earlier P.1 genomes. Between 2 November 2020 and 9 January 2021, we observed 7137 SARI cases and 3144 SARI deaths in Manaus (Fig. 1A). We generated a total of 182 SARS- CoV-2 sequences fromManaus during this pe- riod. This corresponds to one genome for each 39 SARI cases in Manaus, and this ratio is >100-fold higher as compared with the aver- age number of shared genomes per reported case during the same period in Brazil. Dating the emergence of the P.1 lineage We used molecular-clock phylogenetics to un- derstand the emergence and evolution of lineage P.1 (25). We first regressed root-to-tip genetic distances against sequence sampling dates (29) for the P.1, P.2, and B.1.1.28 lineages separately (figs. S6 to S8). This exploratory analysis revealed similar evolutionary rates within each lineage but greater root-to-tip dis- tances for P.1 compared with B.1.1.28 (fig. S8), suggesting that the emergence of P.1 was pre- ceded by a period of fastermolecular evolution. The B.1.1.7 lineage exhibits similar evolution- ary characteristics (13), which was hypothesized to have occurred in a chronically infected or immunocompromised patient (30, 31). To date the emergence of P.1, while ac- counting for a faster evolutionary rate along Faria et al., Science 372, 815–821 (2021) 21 May 2021 2 of 7 Fig. 1. SARS-CoV-2 epide- miological, diagnostic, genomic, and mobility data from Manaus. (A) Dark solid line shows the 7-day rolling average of the COVID-19 confirmed and suspected daily time series of hospitalizations in Manaus. Admissions in Manaus are from Fundação de Vigilância em Saúde do Amazonas (66). Green dots indicate daily severe acute respiratory mortality records from the SIVEP- Gripe (Sistema de Informação de Vigilância Epidemiológica da Gripe) database (67). Red dots indicate excess burial records based on data from Manaus Mayor’s office for comparison (supplementary materials, materials and methods). The arrow indicates 6 December 2020, the date of the first P.1 case identified in Manaus by our study. (B) Maximum likelihood tree (n = 962 viral genomes) with B.1.1.28, P.1, and P.2 sequences, with collapsed views of P.1 and P.2 clusters and highlighting other sequences from Amazonas state, Brazil. Ancestral branches leading to P.1 and P.2 are shown as dashed lines. A more detailed phylogeny is available in fig. S3. Scale bar is shown in units of nucleotide substitutions per site (s/s). (C) Number of air travel passengers from Manaus to all states in Brazil was obtained from National Civil Aviation Agency of Brazil (www.gov.br/anac). The ISO 3166-2:BR codes of the states with genomic reports of P.1 [GISAID (68), as of 24 February 2021], are shown in bold. An updated list of GISAID genomes and reports of P.1 worldwide is available at https://cov-lineages.org/global_report_P.1.html. (D) Number of genome sequences from Manaus belonging to lineages of interest (supplementary materials, materials and methods). Spike mutations of interest are denoted. 0 100 200 D a ily n o . o f a d m is s io n s a n d d e a th s i n M a n a u s A Admissions in Manaus (SIVEP-Gripe) Data source: May/20Mar/20 Sep/20Jul/20 Jan/21Nov/20 Mar/21 Excess burials in Manaus SARI mortality data (SIVEP-Gripe) B Thousand of flight passengers from Manaus D e s ti n a ti o n S ta te 10 20 300 Detection (24-02-2021): SP AM PA RO CE RR DF PE RJ AC MG MA MT RN GO Reported in GISAID Not reported in GISAID 0 50 100 A v a ila b le n o . g e n o m e s fr o m M a n a u s P.2 (E484K) P.1 (K417T, E484K, N501Y) Other lineages Lineage (mutations of interest): D 150 May/20Mar/20 Sep/20Jul/20 Jan/21Nov/20 Mar/21 Date of sample collection C 1.0E-4 s/s Amazonas Other Location: P.2 (n=143) P.1 (n=95) RESEARCH | RESEARCH ARTICLE D o w n lo ad ed fro m h ttp s://w w w .scien ce.o rg o n Jan u ary 2 2 , 2 0 2 5 its ancestral branch, we used a local molecular clockmodel (32) with a flexible nonparametric demographic tree prior (33). Using this ap- proach, we estimated the date of the com- mon ancestor of the P.1 lineage to be around 15 November 2020 [median, 95%Bayesian cred- ible interval (BCI), 6 October to 24 November 2020; mean, 9 November 2020] (fig. S9). This is only 3 to 4 weeks before the resurgence in SARS-CoV-2 confirmed cases inManaus (Figs. 1A and 2 and fig. S9). The P.1 sequences formed a single well-supported group (posterior proba- bility = 1.00) that clustered most closely with B.1.1.28 sequences fromManaus (Fig. 2, “AM”), suggesting that P.1 emerged there. The earliest P.1 samples were detected inManaus (34). The first known travel-related cases were detected in Japan (28) and São Paulo (table S3) and were both linked to travel from Manaus. Further- more, the local clock model statistically con- firmed a higher evolutionary rate for the branch immediately ancestral to lineage P.1 compared with lineage B.1.1.28 as a whole [Bayes factor (BF) = 6.04]. Our data indicate multiple introductions of the P.1 lineage from Amazonas to Brazil’s southeastern states (Fig. 2). We also detected seven small well-supported clusters of P.2 se- quences from Amazonas (two to six sequences, posterior probability = 1.00). Virus exchange between Amazonas state and the urban me- tropolises in southeast Brazil largely follows patterns of national air travel mobility (Fig. 1D and fig. S10). Infection with P.1 and sample viral loads We analyzed all quantitative reverse transcrip- tion polymerase chain reaction (RT-PCR) SARS- CoV-2–positive results from a laboratory that has provided testing inManaus sinceMay 2020 (Fig. 1A and data file S1), with the aim of ex- ploring trends in sample quantitative RT-PCR cycle threshold (Ct) values, which are inversely related to sample virus loads and transmis- sibility (35). By focusing on data from a single laboratory, we reduced instrument and pro- cess variation that can affect Ct measurements. We analyzed a set of quantitative RT-PCR positive cases for which virus genome sequenc- ing and lineage classification had been under- taken (n = 147 samples). Using a logistic function (Fig. 3A), we found that the fraction of samples classified as P.1 increased from 0 to 87% in around 7 weeks (table S4), quantifying the trend shown in Fig. 1C. We found a small but statistically significant association between P.1 infection and lower Ct values, for both the E gene (lognormal regression, P = 0.029, n = 128 samples, 65 of which were P.1) and N gene (P = 0.01, n = 129 samples, 65 of which were P.1), with Ct values lowered by 1.43 [0.17 to 2.60, 95% confidence interval (CI)] and 1.91 (0.49 to 3.23) cycles in the P.1 lineage on av- erage, respectively (Fig. 3B). Using a larger sample of 942 Ct values (in- cluding an additional 795 samples for which no lineage information was available), we in- vestigated Ct values across three time periods characterized by increasing P.1 relative abun- dance. Average Ct values for both the E and N genes declined through time, as both case numbers and the fraction of P.1 infections increased, with Ct values significantly lower in period 3 as compared with period 1 (E gene, P = 0.12 and P < 0.001 for comparison of time periods 2 and 3 to period 1; N gene, P = 0.14 and P < 0.001, respectively) (Fig. 3C). Analy- ses of Ct values for samples from a different laboratory, also based in Manaus, showed sim- ilarly significant declines between the first and third time periods defined here (P < 0.0001 for both E and N genes) (fig. S11 and data file S3). However, population-level Ct distributions are sensitive to changes in the average time since infection when samples are taken, so that median Ct values can decrease during epidemic growth periods and increase dur- ing epidemic decline (36). To account for this effect, we assessed the association between P.1 infection and Ct levels while controlling for the delay between symptom onset and sample collection. Statistical significance was lost for both data sets (E gene, P = 0.15, n = 42 samples, 22 of which were P.1; N gene, P = 0.12, n = 42 samples, 22 of which were P.1). Owing to this Faria et al., Science 372, 815–821 (2021) 21 May 2021 3 of 7 Mar-20 May-20 Aug-20 Oct-20 Dec-20 P.2 Amazonas Other location P.1 Fig. 2. Visualization of the time-calibrated maximum clade credibility tree reconstruction for B.1.1.28, P.1, and P.2 lineages in Brazil. Terminal branches and tips of Amazonas state are colored in brown, and those from other locations are colored in green (n = 962 viral genomes). Nodes with posterior probabilities of <0.5 have been collapsed into polytomies, and their range of divergence dates are illustrated as shaded expanses. RESEARCH | RESEARCH ARTICLE D o w n lo ad ed fro m h ttp s://w w w .scien ce.o rg o n Jan u ary 2 2 , 2 0 2 5 confounding factor, we cannot distinguish whether P.1 infection is associated with in- creased viral loads (37) or a longer duration of infection (38). Mathematical modeling of lineage P.1 epidemiological characteristics We next explored epidemiological scenarios that might explain the recent resurgence of transmission in Manaus (39). To do this, we extended a semimechanistic Bayesian model of SARS-CoV-2 transmissibility and mortal- ity (40–42) to include two categories of virus (“P.1” and “non-P.1”) and to account for in- fection severity, transmissibility, and pro- pensity for reinfection to vary between the categories. It also integrates information on the timing of P.1 emergence in Manaus using our molecular clock results (Fig. 2). The model explicitly incorporates waning of immune pro- tection after infection, parameterized on the basis of dynamics observed in recent studies (16, 43), to explore the competing hypothesis that waning of prior immunity might explain the observed resurgence (42). We used the model to evaluate the statistical support that P.1 possesses altered epidemiological charac- teristics compared with local non-P.1 lineages. Epidemiological model details and sensitiv- ity analyses (tables S5 to S10) can be found in the supplementary materials. The model is fitted to both COVID-19mortality data [with a correction for systematic reporting delays (44, 45)] and the estimated increase through time in the proportion of infections due to P.1 derived from genomic data (table S4). We assumed that within-category immunity wanes over time (50% wane within a year, although sensitivity analyses varying the rapidity of waning are presented in table S7) and that cross-immunity (the degree to which previous infectionwith a virus belonging to one category protects against subsequent infection with the other) is symmetric between categories. Our results suggest that the epidemiological characteristics of P.1 are different from those of previously circulating local SARS-CoV-2 lineages, but the results also highlight substantial un- certainty in the extent and nature of this dif- ference. Plausible values of transmissibility and cross-immunity exist in a limited area but are correlated (Fig. 4A, with the extent of immune evasion defined as 1 minus the in- ferred cross-immunity). This is expectedbecause in themodel, a higher degree of cross-immunity means that greater transmissibility of P.1 is required to generate a second epidemic. Within this plausible region of parameter space, P.1 can be between 1.7 and 2.4 times more transmis- sible (50% BCI, 2.0 median, with a 99% poste- rior probability of being >1) than local non- P1 lineages and can evade 21 to 46% (50% BCI, 32% median, with a 95% posterior probability of being able to evade at least 10%) of pro- tective immunity elicited by previous infection with non-P.1 lineages, corresponding to 54 to 79% (50% BCI, 68% median) cross-immunity (Fig. 4A). The joint-posterior distribution is inconsistent with a combination of highly in- creased transmissibility and low cross-immunity and, conversely, also with near-complete cross- immunity but only a small increase in transmis- sibility (Fig. 4A). Moreover, our results further show that natural immunity waning alone is unlikely to explain the observed dynamics in Manaus, with support for P.1 possessing altered epidemiological characteristics robust to a range of values assumed for the date of the lineage’s emergence and the rate of natural immunity waning (tables S5 and S7). We caution that these results are not generalizable to other set- tings; more detailed and direct data are needed to identify the exact degree and nature of the changes to the epidemiological characteris- tics of P.1 compared with previously circulat- ing lineages. We estimate that infections are 1.2 to 1.9 times more likely (50% BCI, median 1.5, 90% posterior probability of being >1) to result in mortality in the period after the emergence of P.1, compared with before, although posterior estimates of this relative risk are also corre- lated with inferred cross-immunity (Fig. 4B). More broadly, the recent epidemic in Manaus has strained the city’s health care system, lead- ing to inadequate access to medical care (46). We therefore cannot determine whether the estimated increase in relative mortality risk is due to P.1 infection, stresses on the Manaus Faria et al., Science 372, 815–821 (2021) 21 May 2021 4 of 7 Fig. 3. Temporal variation in the proportion of sequenced gen- omes belonging to P.1, and trends in quantitative RT-PCR Ct values for COVID-19 infec- tions in Manaus. (A) Logistic function fitting to the proportion of genomes in sequenced infec- tions that have been classified as P.1 (black circles, size indicating number of infections sequenced), divided up into time periods when the predicted proportion of infec- tions that are due to P.1 is <1/3 (light brown), between 1/3 and 2/3 (green), and greater than 2/3 (gray). For the model fit, the darker ribbon indicates the 50% credible interval, and the lighter ribbon indicates the 95% credible interval. For the data points, the gray thick line is the 50% exact bino- mial CI, and the thinner line is the 95% exact binomial CI. (B) Ct values for genes E and N in a sample of symptomatic cases presenting for testing at a health care facility in Manaus (laboratory A), stratified according to the period defined in (A) in which the oropharyngeal and nasal swab collections occurred. (C) Ct values for genes E and N in a subsample of 184 infections included in (B) that had their genomes sequenced (dataset A). Number of P.1 sequences from Manaus: 45 20 10 2 0.00 0.25 0.50 0.75 1.00 Oct Nov Dec Jan Feb A B C P ro p o rt io n o f P .1 g e n o m e s i n M a n a u s Date of collection 20 30 40 E gene N gene R T -q P C R C t va lu e ( lb a B , s e q u e n c e d ) Lineage: Not P.1 P.1 Target gene 20 30 40 E gene N gene Target gene R T -q P C R C t va lu e ( la b B ) Time Period: Before 16 Dec 18 to 28 Dec After 28 Dec RESEARCH | RESEARCH ARTICLE D o w n lo ad ed fro m h ttp s://w w w .scien ce.o rg o n Jan u ary 2 2 , 2 0 2 5 health care system, or both. Detailed clinical investigations of P.1 infections are needed. Our model makes the assumption of a homo- geneously mixed population and therefore ignores heterogeneities in contact patterns (dif- ferences in private versus public hospitals are provided in fig. S13). This is an important area for future research. The model fits observed time series data from Manaus on COVID-19 mortality (Fig. 4C) and the relative frequency of P.1 infections (Fig. 4D) and also captures previously estimated trends in cumulative sero- positivity in the city (Fig. 4E). We estimate the reproduction number (Rt) on 7 February 2021 to be 0.1 (median, 50% BCI, 0.04 to 0.2) for non-P.1 and 0.5 (median, 50% BCI, 0.4 to 0.6) for P.1 (Fig. 4F). Characterization and adaptation of a constellation of spike protein mutations Lineage P.1 contains 10 lineage-defining amino acidmutations in the virus spike protein (L18F, T20N, P26S, D138Y, R190S, K417T, E484K, N501Y, H655Y, and T1027I) compared with its immediate ancestor (B.1.1.28). In addition to the possible increase in the rate of molec- ular evolution during the emergence of P.1, we found by use of molecular selection analyses (47) evidence that eight of these 10 mutations are under diversifying positive selection (table S1 and fig. S14). (Single-letter abbreviations for the amino acid residues are as follows: A, Ala; C, Cys; D, Asp; E, Glu; F, Phe; G, Gly; H, His; I, Ile; K, Lys; L, Leu; M, Met; N, Asn; P, Pro; Q, Gln; R, Arg; S, Ser; T, Thr; V, Val; W, Trp; and Y, Tyr. In the mutants, other amino acids were substituted at certain locations; for ex- ample, K417T indicates that lysine at position 417 was replaced by threonine.) Three key mutations present in P.1—N501Y, K417T, and E484K—are in the spike protein RBD. The former two interact with human ACE2 (hACE2) (11), whereas E484K is located in a loop region outside the direct hACE2 in- terface (fig. S14). The same three residues are mutated with the B.1.351 variant of concern, andN501Y is also present in the B.1.1.7 lineage. The independent emergence of the same con- stellation of mutations in geographically dis- tinct lineages indicates a process of convergent molecular adaptation. Similar to SARS-CoV-1 (48–50), mutations in the RBD may increase affinity of the virus for host ACE2 and conse- quently influence host cell entry and virus trans- mission. Recentmolecular analysis of B.1.351 (51) indicates that the three P.1 RBDmutations may similarly enhance hACE2 engagement, provid- ing a plausible hypothesis for an increase in transmissibility of the P.1 lineage. Moreover, E484K is associated with reduced antibody neutralization (6, 9, 52, 53). RBD-presented epi- topes account for ~90% of the neutralizing ac- tivity of sera from individuals previously infected with SARS-CoV-2 (54); thus, tighter binding of P.1 viruses to hACE2 may further reduce the effectiveness of neutralizing antibodies. Faria et al., Science 372, 815–821 (2021) 21 May 2021 5 of 7 1 2 3 4 0.0 0.3 0.6 0.9 May/20Mar/20 Sep/20Jul/20 Jan/21Nov/20 Mar/21 Date May/20Mar/20 Sep/20Jul/20 Jan/21Nov/20 Mar/21 Independent seroprevalence estimates from Manaus non-P.1 P.1 C u m u la ti v e i n c id e n c e p e r c a p it a May/20Mar/20 Sep/20Jul/20 Jan/21Nov/20 Mar/21 0 100 D a ily n o . o f d e a th s 50 150 Transmissibility increase SARI mortality data (SIVEP-Gripe) Non-P.1 (50% CI) Non-P.1 (95% CI) P.1 (50% CI) P.1 (95% CI) 1.00 0.75 0.50 0.25 0.00 C ro s s -i m m u n it y 1.00 0.75 0.50 0.25 0.00 C ro s s -i m m u n it y 1 2 3 40 Relative risk of mortality 0.00 0.25 0.50 0.75 1.00 P .1 p ro p o rt io n i n M a n a u s Independent estimates from sequence data from Manaus D Date Jan/21Nov/20 Feb/21Dec/20 0 1 2 3 4 5 R e p ro d u c ti o n n u m b e r, R t non-P.1 P.1 DateDate P.1 EC F A B Fig. 4. Estimates of the epidemiological characteristics of P.1 inferred from a multicategory Bayesian transmission model fitted to data from Manaus, Brazil. (A) Joint posterior distribution of the cross-immunity and transmissibility increase inferred through fitting the model to mortality and genomic data. Gray contours indicate posterior density intervals ranging from the 95 and 50% isoclines. Marginal posterior distributions for each parameter shown along each axis. (B) As for (A), but showing the joint-posterior distribution of cross-immunity and the inferred relative risk of mortality in the period after emergence of P.1 compared with the period prior. (C) Daily incidence of COVID-19 mortality. Points indicate severe acute respiratory mortality records from the SIVEP-Gripe database (67, 69). Brown and green ribbons indicate model fit for COVID-19 mortality incidence, disaggregated by mortality attributable to non-P.1 lineages (brown) and the P.1 lineage (green). (D) Estimate of the proportion of P.1 infections through time in Manaus. Black data points with error bars are the empirical proportion observed in genomically sequenced cases (Fig. 3A), and green ribbons (dark = 50% BCI, light = 95% BCI) are the model fit to the data. (E) Estimated cumulative infection incidence for the P.1 and non-P.1 categories. Black data points with error bars are reversion-corrected estimates of seroprevalence from blood donors in Manaus (2). Colored ribbons are the model predictions of cumulative infection incidence for non-P.1 lineages (brown) and P.1 lineages (green). These points are shown for reference only and were not used to fit the model. (F) Bayesian posterior estimates of trends in reproduction number Rt for the P.1 and non-P.1 categories. RESEARCH | RESEARCH ARTICLE D o w n lo ad ed fro m h ttp s://w w w .scien ce.o rg o n Jan u ary 2 2 , 2 0 2 5 Conclusion Weshow that P.1most likely emerged inManaus inmid-November, where high attack rates have been previously reported.High rates ofmutation accumulation over short time periods have been reported in chronically infected or im- munocompromised patients (13). Given a sustained generalized epidemic in Manaus, we believe that this is a potential scenario for P.1 emergence. Genomic surveillance and early data sharing by teams worldwide have led to the rapid detection and characteriza- tion of SARS-CoV-2 and new variants of con- cern (VOCs) (25), yet such surveillance is still limited in many settings. The P.1 lineage is spreading rapidly across Brazil (55), and this lineage has now been detected in >36 coun- tries (56). But existing virus genome sampling strategies are often inadequate for determin- ing the true extent of VOCs in Brazil, andmore detailed data are needed to address the im- pact of different epidemiological and evolution- ary processes in their emergence. Sustainable genomic surveillance efforts to track variant frequency [for example, (57–59)] coupled with analytical tools to quantify lineage dynamics [for example, (60, 61)] and anonymized epide- miological surveillance data (62, 63) could enable enhanced real-time surveillance of VOCs worldwide. Studies to evaluate real- world vaccine efficacy in response to P.1 are urgently needed. Neutralization titers repre- sent only one component of the elicited re- sponse to vaccines, and minimal reduction of neutralization titers relative to earlier cir- culating strains is not uncommon. 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Sachi (Instituto Adolfo Lutz) for agreeing with the use of unpublished sequence data available in GISAID before publication. We thank the anonymous reviewers for their considerations and suggestions. We thank the administrators of the GISAID database for supporting rapid and transparent sharing of genomic data during the COVID-19 pandemic. A full list acknowledging the authors publishing data used in this study can be found in data file S4. Funding: This work was supported by a Medical Research Council-São Paulo Research Foundation (FAPESP) CADDE partnership award (MR/S0195/1 and FAPESP 18/14389-0) (https://caddecentre.org); FAPESP (E.C.S.: 18/ 14389-0; I.M.C: 2018/17176-8 and 2019/12000-1, F.C.S.S.: 2018/ 25468-9; J.G.d.J.: 2018/17176-8, 2019/12000-1, 18/14389-0; T.M.C.: 2019/07544-2; C.A.M.S.: 2019/21301-5; W.M.S.: 2017/ 13981-0, 2019/24251-9; L.M.d.S.: 2020/04272-9; M.C.d.P.: 2019/ 21568-1; V.H.N.: 2018/12579-7; C.A.P.: 2019/21858-0; and P.S.P.: 16/18445-7; J.L.P.-M.: 2020/04558-0); Wellcome Trust and Royal Society (N.R.F.: Sir Henry Dale Fellowship: 204311/Z/16/Z); Wellcome Trust (Wellcome Centre for Human Genetics: 203141/Z/ 16/Z); Clarendon Fund and Department of Zoology, University of Oxford (D.d.S.C.); Medical Research Council (T.A.B and R.J.G.H: MR/S007555/1); European Molecular Biology Organisation (R.J.G.H.: ALTF 869-2019); CNPq (R.S.A.: 312688/2017-2, 439119/ 2018-9; W.M.S.: 408338/2018-0, 304714/2018-6; V.H.N.: 304714/ 2018-6); FAPERJ (R.S.A.: 202.922/2018); FFMUSP (M.S.R.: 206.706; C.A.P.); Imperial College COVID-19 Research Fund (H.M.S. and S.F.); CAPES (G.M.F. and C.A.P., Code 001); Wellcome Trust Collaborator Award (P.L., A.R., and N.J.L.: 206298/Z/17/Z); European Research Council (P.L and A.R.: 725422-ReservoirDOCS); European Union’s Horizon 2020 project MOOD (P.L. and M.U.G.K.: 874850); U.S. National Institutes of Health (M.A.S.: U19 AI135995); Oxford Martin School (O.G.P.); Branco Weiss Fellowship (M.U.G.K); Covid-19 Research Fund (S.F.); EPSRC (S.F.: EP/V002910/1; M.M. through the EPSRC Centre for Doctoral Training in Modern Statistics and Statistical Machine Learning); BMGF (S.B.); UKRI (S.B.); Novo Nordisk Foundation (S.B.); Academy of Medical Sciences (S.B.); BRC (S.B.); MRC (S.B.); and Bill & Melinda Gates Foundation (O.R.: OPP1175094). We acknowledge support from the Rede Corona-ômica BR MCTI/ FINEP affiliated to RedeVírus/MCTI (FINEP 01.20.0029.000462/ 20, CNPq 404096/2020-4), FAPESP project 2018/12579-7 CNPq project 304714/2018-6 (V.H.N.), EPSRC Centre for Doctoral Training in Modern Statistics and Statistical Machine Learning at Imperial and Oxford (M.M.), and the Bill & Melinda Gates Foundation (OPP1175094) (O.R.). This work received funding from the UK Medical Research Council under a concordat with the UK Department for International Development. We additionally acknowledge support from Community Jameel and the NIHR Health Protection Research Unit in Modelling Methodology. Last, Faria et al., Science 372, 815–821 (2021) 21 May 2021 6 of 7 RESEARCH | RESEARCH ARTICLE D o w n lo ad ed fro m h ttp s://w w w .scien ce.o rg o n Jan u ary 2 2 , 2 0 2 5 we also gratefully acknowledge support from Oxford Nanopore Technologies for a donation of sequencing reagents and NVIDIA Corporation and Advanced Micro Devices for a donation of parallel computing resources. Author contributions: Conceptualization: N.R.F., T.A.M., C.W., I.M.C., D.d.S.C., A.R., C.D., O.G.P., S.F., S.B., and E.C.S. Methodology: N.R.F., T.A.M., C.W., I.M.C., D.d.S.C., S.M., F.C.S.S., I.H., M.S.R., J.G.d.J., L.A.M.F., P.S.A., T.M.C., C.A.M.S., E.R.M., J.T.M., R.H.M.P., P.S.P., M.U.G.K., R.J.G.H., T.A.B., O.G.P., M.A.S., S.L.K.P., O.R., N.M.F., N.J.L., P.L., A.R., C.D., S.F., S.M., and E.C.S. Investigation: N.R.F., T.A.M., C.W., I.M.C., D.d.S.C., S.M., M.A.E.C., F.C.S.S., I.H., M.S.R., J.G.d.J., L.A.M.F., P.S.A., T.M.C., C.A.M.S., E.R.M., J.T.M., R.H.M.P., P.S.P., M.U.G.K., R.J.H.H, N.G., W.M.S., L.J.T.A., C.d.C.C., H.H., G.M.F., E.C.R., L.M.d.S., M.C.d.P., F.S.V.M., A.B.d.L., J.d.P.S., D.A.G.Z., A.C.d.S.F., R.P.S., D.J.L., P.G.T.W., H.M.S., A.L.P.d.S., M.S.V., , V.S.D.C., R.M.F.F., H.M.d.S., R.S.A., B.N., J.A.H., M.M., X.M., H.C., R.S., A.G., M.A.S., T.A.B., S.L.K.P., C.-H.W., O.R., N.M.F., C.A.P., V.H.N., N.J.L., P.L., A.R., N.A.F., M.d.P.S.S.C., C.D., O.G.P., S.F., S.B., and E.C.S. Visualization: N.R.F., T.A.M., C.W., D.d.S.C., I.M.C., J.T.M., A.R., S.L.K.P., T.A.B., C.W., and S.B. Funding acquisition: N.R.F., N.J.L., A.R., O.G.P., N.A.F., S.F., S.B., and E.C.S. Project administration: N.R.F. and E.C.S. Supervision: N.R.F., O.G.P., A.R., C.D., N.J.L., S.B., and E.C.S. Writing, original draft: N.R.F., T.A.M., C.W., I.M.C., D.d.S.C., S.F., S.B., O.G.P., C.D., and E.C.S. Writing, review and editing: All authors. Competing interests: S.B. declares that he advises on The Scientific Pandemic Influenza Group on Modelling (SPI-M) and advises the FCA on a legal matter regarding COVID-19 infections in England in March 2020. He is not paid for either of these advisory roles, and neither are related to the work in this paper. All other authors declare that they have no competing interests. Data and materials availability: All data, code, and materials used in the analysis are available in a dedicated GitHub Repository (64, 65). This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/. This license does not apply to figures/photos/artwork or other content included in the article that is credited to a third party; obtain authorization from the rights holder before using such material. SUPPLEMENTARY MATERIALS science.sciencemag.org/content/372/6544/815/suppl/DC1 Materials and Methods Supplementary Text Figs. S1 to S16 Tables S1 to S10 References (70–102) Data Files S1 to S6 MDAR Reproducibility Checklist 25 February 2021; accepted 11 April 2021 Published online 14 April 2021 10.1126/science.abh2644 Faria et al., Science 372, 815–821 (2021) 21 May 2021 7 of 7 RESEARCH | RESEARCH ARTICLE D o w n lo ad ed fro m h ttp s://w w w .scien ce.o rg o n Jan u ary 2 2 , 2 0 2 5