Genome sequencing efforts for the surveillance of the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) has granted public access to a massive number of virus genomes generated worldwide (https://www.gisaid.org/). Exploring SARS-CoV-2 genome data using genomic epidemiology has allowed researchers to characterize increasing virus diversity (Hill et al., 2021), track emerging viral subpopulations, and explore virus evolution in real-time, both at local and global scales (for examples see Kraemer et al., 2021; du Plessis et al., 2021; Worobey et al., 2020; Candido et al., 2020; COVID-19 Genomics UK (COG-UK) consortiumcontact@cogconsortium.uk, 2020). Throughout the development of the COVID-19 pandemic, viral variants have emerged and circulated across different regions of the world (Kraemer et al., 2021; Chen et al., 2021), displaying specific mutations that define their phylogenetic patterns (Li et al., 2021; Tao et al., 2021). The emergence and spread of SARS-CoV-2 lineages has been routinely monitored since early 2021, informing public health authorities on their responses to the ongoing pandemic (Oude Munnink and Koopmans, 2023).

Emerging virus lineages are classified using a dynamic nomenclature system (‘Pango system’, Phylogenetic Assignment of Named Global Outbreak Lineages), developed to consistently assign newly generated genomes to existing lineages, and to designate novel virus lineages according to their phylogenetic identity and epidemiological relevance (Rambaut et al., 2020; Ruis and Colquhoun, 2021). Virus lineages that may pose an increased risk to global health have been classified as Variants of Interest (VOI), Variants under Monitoring (VUM), and Variants of Concern (VOC), potentially displaying one or more of the following biological properties (Tao et al., 2021; Centers for Disease Control and Prevention, 2022): increased transmissibility (Horby et al., 2021), decreasing the effectiveness of available diagnostics or therapeutic agents (such as monoclonal antibodies) (Weisblum et al., 2020), and evasion of immune responses (including vaccine-derived immunity) (Oude Munnink and Koopmans, 2023; Greaney et al., 2021a; Greaney et al., 2021b). Up to date, five SARS-CoV-2 lineages (including all descending sub-lineages) have been designated as VOC: B.1.1.7 (Alpha), B.1.351 (Beta), P.1 (Gamma), B.1.617.2 (Delta), and B.1.1.529 (Omicron) (Oude Munnink and Koopmans, 2023; World Health Organization, 2021; O’Toole et al., 2021a).

Virus lineages that dominate across various geographic regions are likely to have an evolutionary advantage, driven in part by a genetic increase in virus fitness (i.e. mutations enhancing transmissibility and/or immune escape; Martin et al., 2021; Escalera-Zamudio et al., 2023; Kumar et al., 2021; Vöhringer et al., 2021; Caniels et al., 2021). Moreover, the spread of different VOC across the world has been linked to human movement, often resulting in the replacement of previously dominating virus lineages (Oude Munnink and Koopmans, 2023). However, exploring lineage replacement/fitness dynamics remains a challenge, as these are impacted by numerous factors, including differential and stochastic growth rates that vary across geographic regions, a shifting immune structure of the host population (linked to viral pre-exposure levels and vaccination rates), (Vöhringer et al., 2021; Caniels et al., 2021) and changing social behaviours (such as fluctuating human mobility patterns and the implementation of local non-pharmaceutical interventions across time) (Vöhringer et al., 2021; Zhang et al., 2020; Boutin et al., 2021). Thus, the epidemiological and evolutionary processes enabling some lineages to spread and become dominant across distinct geographic regions, whilst others fail to do so, remain largely understudied.

Mexico has been severely impacted by the COVID-19 pandemic, evidenced by a high number of cumulative deaths relative to other countries in Latin America (Sánchez-Talanquer et al., 2021). Since the first introductions of the virus in early 2020 and up to November 2021 (Taboada et al., 2020), the local epidemiological curve fluctuated between three waves of infection (observed in July 2020, January 2021, and August 2021; Sánchez-Talanquer et al., 2021; Taboada et al., 2020; Ritchie, 2020). Prior to the first peak of infection, non-pharmaceutical interventions (including social distancing and suspension of non-essential activities) were implemented at a national scale from March 23, 2020 to May 30, 2020. Nonetheless, a reopening plan for the country was already announced in May 13, 2020, whilst the national vaccination campaign did not begin before December 2020 (Sánchez-Talanquer et al., 2021). The ‘Mexican Consortium for Genomic Surveillance’ (abbreviated CoViGen-Mex) Consorcio de Vigilancia Genómica MexCoV2, 2021a was launched in February 2021, establishing systematic sequencing effort for a genomic epidemiology-based surveillance of SARS-CoV-2 in Mexico. In close collaboration with the national ministry of health, and driven by the sequencing capacity in the country, the program aimed to sequence per month approximately 1200 representative samples derived from positive cases recorded throughout national territory, based on the proportion of cases reported across states. In May 2021, the sequencing scheme was upgraded to follow the official case report line, in order to better coordinate case reporting and genome sampling across the country.

Derived from publicly accessible genome data from Mexico deposited in GISAID (https://www.gisaid.org/) from 2020 to 2021 (corresponding to the first year of the epidemic), over 48,000 viral genomes were available, resulting in an approximate average of 2000 viral genomes sequenced per month. However, starting February 2021, the CoViGen-Mex sampling scheme gradually increased its sequencing output from a mean of less than 500 genomes per month to over 1000 genomes from May 2021 onwards. As of the time of writing this manuscript, around 80,000 SARS-CoV-2 genomes from Mexico were available in GISAID, with one-third of these generated by CoViGen-Mex Consorcio de Vigilancia Genómica MexCoV2, 2021a (and other national institutions sequencing the rest). From these, approximately 95% of all SARS-CoV-2 genomes from Mexico were generated in the country and 5% in the USA.

Investigating the dominance and replacement patterns of SARS-CoV-2 can provide valuable information for understanding viral spread and shed light on virus evolution and adaptation processes. During the first year of the epidemic in Mexico, over 200 different virus lineages were detected, including all VOCs O’Toole et al., 2021a; Consorcio de Vigilancia Genómica MexCoV2, 2021b. Various virus lineages co-circulated across the national territory, a noteworthy observation in light of recombinant SARS-CoV-2 lineages that emerged in North America during 2021 (Gutierrez et al., 2022). Additionally, some virus lineages displayed specific dominance and replacement patterns that differed from those observed in neighboring countries, specifically the USA (Consorcio de Vigilancia Genómica MexCoV2, 2021a; Consejo Nacional de Humanidades, Ciencias y Tecnologías, 2021; Taboada et al., 2021). With this in mind, our study aimed to examine the dominance and replacement patterns of SARS-CoV-2 in Mexico from 2020 to 2021. We explored whether the spread of dominant lineages was driven by specific mutations that impacted local growth rates (further shaped the immune landscape of the local host population, depending mostly on virus pre-exposure levels at this time). We also investigated whether viral diffusion processes within the country were associated with local human mobility patterns, anticipating that the SARS-CoV-2 epidemic in Mexico may have been impacted by the epidemiological behavior of the virus in neighboring countries.

To achieve this, we investigated the introduction, spread, and replacement dynamics of five virus lineages that dominated during the first year of the epidemic: B.1.1.222, B.1.1.519, B.1.1.7 (VOC Alpha), P.1 (VOC Gamma), and B.1.617.2 (VOC Delta) (Consorcio de Vigilancia Genómica MexCoV2, 2021a; Consejo Nacional de Humanidades, Ciencias y Tecnologías, 2021; Taboada et al., 2021). We used a phylodynamic approach to analyze publicly available cumulative SARS-CoV-2 genome data from the country in the context of virus genome data collected worldwide. We also devised a human migration and phylogenetic-informed subsampling approach to increase the robustness of our tailored phylogeographic analyses. To investigate lineage-specific spatial epidemiology, we contrasted our phylodynamic results with epidemiological and human mobility data, focusing on quantifying lineage importations into Mexico and characterizing local extended transmission chains across geographic regions (i.e. states). Our analysis revealed similar dynamics for the B.1.1.222 and B.1.1.519 lineages, with both likely originating in Mexico and denoting single extended transmission chains sustained for over a year. For P.1, B.1.1.7, and B.1.617.2 lineages, multiple introduction events were identified, with the detection of a few large transmission chains across the country. For B.1.617.2, which represented the largest and most genetically diverse clades identified, we observed a within-the-country virus diffusion pattern seeding from the south with subsequent movement into the central and north. We further identify Mexico’s southern border may have played an important role in the introduction and spread of SARS-CoV-2 (and other epidemics) across the country.

The sampling date of this study comprises January 2020 to November 2021, corresponding to the first year of the epidemic in Mexico, just before the introduction of ‘Omicron’ (B.1.1.529) into the country (Consorcio de Vigilancia Genómica MexCoV2, 2021a; Consejo Nacional de Humanidades, Ciencias y Tecnologías, 2021; Taboada et al., 2021). During this time, Mexico reported a daily mean test rate ranging between 0.13–0.18 test per 1,000 inhabitants (Ritchie, 2020). Despite a lower testing rate compared to other countries, the cumulative number of viral genomes generated throughout 2020 and 2021 (both by CoViGen-Mex and other national institutions) correlates with the number of cases recorded at a national scale, corresponding to approximately 100 viral genomes per 10,000 cases, or ~1% of the official COVID-19 cases (Figure 1—figure supplement 1). Although SARS-CoV-2 sequencing remained centralized to Mexico City, the proportion of viral genomes per state roughly coincided with the spatial distribution of confirmed cases (with Mexico City reporting most cases), as stated officially Informe Técnico Diario COVID-19 MÉXICO, 2021 (Figure 1—figure supplement 1). Therefore, SARS-CoV-2 sequencing in Mexico has been sufficient to explore the spatial and temporal frequency of viral lineages across national territory (Consorcio de Vigilancia Genómica MexCoV2, 2021a; Consejo Nacional de Humanidades, Ciencias y Tecnologías, 2021; Taboada et al., 2021), now to further investigate the number of lineage-specific introduction events, and to characterize the extension and geographic distribution of associated transmission chains under a genomic epidemiology approach, as presented in this study. Our comparative analysis on the temporal distribution of virus lineages in Mexico confirmed previous published observations (O’Toole et al., 2021a; Consejo Nacional de Humanidades, Ciencias y Tecnologías, 2021; Taboada et al., 2021) showing that relative to other virus lineages circulating at the time, only the B.1.1.222, B.1.1.519, B.1.1.7 (Alpha), P.1 (Gamma), and B.1.617.2 (Delta) lineages displayed a dominant prevalence pattern within the country. Moreover, for most of these dominant lineages, peaks in genome sampling frequency (defined here as the proportion of viral genomes assigned to a specific lineage, relative to the proportion of viral genomes assigned to any other virus lineage in a given time point) often coincided with the epidemiological waves of infection recorded (except for B.1.1.7 and P.1) (Figure 1a and b).

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B.1.1.222

The B.1.1.222 lineage circulated in North America between April 2020 and September 2021, mostly within the USA (~80% of all B.1.1.222-assigned genomes) and Mexico (~20% of all B.1.1.222-assigned genomes). With limited reports from other regions of the world, B.1.1.222 was thus considered as endemic to the region (https://cov-lineages.org/) O’Toole et al., 2021b. The first B.1.1.222-assigned genome was sampled from Mexico on April 2020 (Mexico/CMX-INER-0026/2020-04-04) O’Toole et al., 2021b, whilst the last B.1.1.222-assigned genome was sampled from the USA on September 2021 (USA/CA-CDPH-1002006730/2021-09-14). The latest sampling date for B.1.1.222 in Mexico corresponds to July 2021 (Mexico/CHH_INER_IMSS_1674/2021-07-26), two months before the latest sampling date of the lineage at an international scale. We observe that in Mexico, the B.1.1.222 lineage was continuously detected between April 2020 and May 2021, followed by a steady decline after July 2021 (Figure 1b). During its circulation period, most B.1.1.222 genomes were collected from the central region of the country, represented by Mexico City (CMX; Figure 2a). For B.1.1.222, a rising genome sampling frequency was observed from May 2020 onwards, coinciding with the first epidemiological wave recorded during July 2020. Subsequently, genome sampling frequency progressively increased to reach a highest of 35% recorded in October 2020, denoting established dominance before the emergence and spread of B.1.1.519 (Figure 1b).

Figure 2

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Data from the first year of the epidemic (available until February 2021, as analyzed by Taboada et al., 2021; O’Toole et al., 2021a; Consejo Nacional de Humanidades, Ciencias y Tecnologías, 2021) initially revealed that the B.1.1.222 lineage had reached a maximum genome sampling frequency of approximately 10%. However, our results show an important frequency underestimation (10% vs 35%), since the vast majority of B.1.1.222-assigned genomes from Mexico (>80%) were generated, assigned, and submitted to GISAID after February 2021. These observations are based on publicly available genome data, and both these values may actually underestimate lineage prevalence. However, calculating 'real' frequency values goes beyond the scope of this study. Notably, in the USA, the B.1.1.222 lineage reached a maximum genome sampling frequency of 3.5%, compared to a 35% observed in Mexico. This four-fold difference in the number of B.1.1.222-assigned genomes between the USA and Mexico reflects a significant disparity in sequencing efforts between the two countries, and contrasts with region-specific epidemiological scenarios Hill et al., 2021. This is of great importance since sequencing disparities and sampling gaps between countries can hinder the development of global outbreak control strategies and exacerbate existing health inequalities.

Phylodynamic analysis for the B.1.1.222 lineage revealed one main clade deriving from a single earliest MRCA (most recent common ancestor) with a ‘most likely’ location (supported by a relative Posterior Probability [PP] of 0.99) inferred to be ‘Mexico’, denoting lineage emergence in the country (Figure 2a). The inferred date for this MRCA corresponds to March 2020, further denoting a cryptic circulation period of a month (before the earliest sampling date for the lineage within the country, see Methods section "Time-scaled analysis"). Subsequent ‘introductions’ should be interpreted as ‘re-introduction’ events into the country (with dates ranging from October 2020 to July 2021). After emergence, B.1.1.222 was seeded into the USA from Mexico multiple times. In this context, we estimate a minimum of 237 introduction events from Mexico into the USA (95% HPD interval = [225-250]), and a minimum of 106 introduction events from the USA into Mexico (95% HPD interval = [93-120]; Figure 2a). Based on inferred node dates (for MRCAs) in the MCC tree, the B.1.1.222 lineage displayed a total persistence of up to 16 months.

B.1.1.7

The B.1.1.7 lineage was first detected in the UK in September 2020, spreading to more than 175 countries in over a year O’Toole et al., 2021d. The earliest B.1.1.7-assigned genome from Mexico was sampled on late December 2020 (Mexico/TAM-InDRE-94/2020-12-31), while the latest B.1.1.7-assigned genome was sampled on October 2021 (Mexico/QUE_InDRE_FB47996_S8900/2021-10-13). Our analysis derived from cumulative genome data from the country revealed a continuous detection between February and September 2021. A peak in genome sampling frequency was observed around May 2021, coinciding with a lower number of cases recorded at the time (Figure 1b). Our results further confirm that the B.1.1.7 lineage reached an overall lower sampling frequency of up to 25% (relative to other virus lineages circulating in the country), as noted prior to this study (e.g. see Zárate et al., 2022; Sánchez-Talanquer et al., 2021; Ritchie, 2020; Subsecretaria de Prevención y Promoción de la Salud, 2021b). Of interest, similar observations were independently made for other Latin American countries, such as Brazil, Chile, and Peru (https://www.gisaid.org/), likely denoting region-specific dynamics for this lineage.

Phylodynamic analysis for B.1.1.7 revealed an earliest MRCA dating to late October 2020, denoting a cryptic circulation period of approximately two months prior to detection in the country. The earliest genome sampling date also coincides with at least four independent and synchronous introduction events that date back to December 2020 (Figure 3a). In total, we estimated a minimum of 224 introduction events into Mexico (95% HPD interval = [219-231]). Potentially linked to the establishment of a systematic genome sequencing in Mexico, most of these were identified after February 2021. Within the MCC, we further identified seven clades (C1a to C7a) representing extended local transmission chains, with C3 and C7 being the largest (Figure 3a, Supplementary file 2).

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During its circulation period, most B.1.1.7 genomes from Mexico were generated from the state of Chihuahua, with these also representing the earliest B.1.1.7-assigned genomes from the country (Taboada et al., 2021; Zárate et al., 2022). However, only a small proportion of these genomes grouped within a larger clade denoting an extended transmission chain (C2a), with the rest falling within minor clusters, or representing singleton events (Figure 3a). Relative to other states, Chihuahua generated an overall lower proportion of viral genomes throughout 2020–2021 (Figure 1—figure supplement 1). Between February 2021 and September 2021 (corresponding to the circulation period of the B.1.1.7 lineage/Alpha VOC in Mexico), Mexico City reported the highest number of COVID-19 cases (https://coronavirus.gob.mx/datos/#DOView). During this time, Mexico City also reported the highest number of cases related to the B.1.1.7 lineage/Alpha VOC, with 2452 confirmed cases, followed by the states of Mexico, Jalisco, and Nuevo Leon (https://coronavirus.gob.mx/variantes/). Therefore, neither phylogenetic nor epidemiological data from the country support the notion that Chihuahua may have been an initial sink-source for the B.1.1.7 lineage/Alpha VOC (or for any other virus lineage, when comparing DTA results). Various factors can impact virus lineage distribution in a given region at a specific time point, including stochastic population processes, and the role of asymptomatic carriers, which can contribute to the difficulty in identifying extended transmission chains and their geographic distribution. Consequently, we can only speculate that given its proximity to the US border, Chihuahua may have been an early introduction point of the lineage from the US. However, this observation is not supported by our phylogeographic analyses, given the restrictions on determining source locations for virus introduction events into the country related to sampling limitations.

For the larger C3a and C7a clades, both MRCA’s date to February 2021, denoting independent and synchronous introduction events (Figure 3a). The C3a comprises genomes collected from 22/32 states in the country, predominantly from Mexico City (CMX), followed by southern states of Yucatán (YUC) and Quintana Roo (ROO) (Figure 3—figure supplement 1). The C3a displayed a persistence of three months: from March to June 2021. For the C7a, viral genomes were sampled from 20/32 states of the country, with >70% of these coming from the southern state of Tabasco (TAB) and north-eastern state of Tamaulipas (TAM) (Figure 3—figure supplement 1). The C7a displayed a persistence of four months: from March to July 2021. Based on inferred node dates within the MCC tree, the B.1.1.7 lineage displayed a total persistence of approximately 10 months.

B.1.617.2

Initially detected in India during October 2020, the B.1.617.2 lineage spread globally to become dominant, and was later associated with an increase in COVID cases recorded globally following March 2021 (O’Toole et al., 2021e; Tian et al., 2021). The parental B.1.617.2 lineage further diverged into >230 descending sub-lineages (designated as the AY.X) that spread to different regions of the world (O’Toole et al., 2021a; O’Toole et al., 2021e; O’Toole et al., 2021f). Again, as our dataset comprises both B.1.617.2 and AY. X-assigned genomes, it is henceforth referred here as a B.1.617.2+.

The first ‘B.1.617.2-like’ genome from Mexico was sampled on September 2020 (Mexico/AGU-InDRE_FB18599_S4467/2020-09-22), followed by a sporadic genome detection throughout January 2021 (with <10 sequences) GISAID, 2008. However, the comparative analysis on genome sampling frequencies revealed that expansion of B.1.617.2+only occurred after April 2021 (Figure 1b). We further confirmed that by August 2021, the lineage had reached a relative frequency of >95%, coinciding with the peak of the third wave of infection recorded in the country Subsecretaria de Prevención y Promoción de la Salud, 2021a. Up to the sampling date of this study, we detected >80 B.1.617.2 sub-lineages (AY.X) circulating in Mexico, with most viral genomes assigned as AY.20 (22%), AY.26 (13%), and AY.100 (5%), followed by AY.113, AY.62, and AY.3. Of interest, these were previously noted to be mostly prevalent within North America (https://cov-lineages.org/) O’Toole et al., 2021g; O’Toole et al., 2021h; O’Toole et al., 2021i; O’Toole et al., 2021j; O’Toole et al., 2021k. During its circulation period, B.1.617.2+displayed a more homogeneous genome sampling distribution across Mexico, as compared to other virus lineages. Again, this is likely to be associated with the establishment of a systematic viral genome sampling and sequencing following February 2021, further driven by the widespread expansion of the lineage throughout the country (Figure 4).

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Phylodynamic analysis for B.1.617.2+revealed a minimum number of 142 introduction events into Mexico (95% HPD interval = [125-147]). Within the MCC, six major clades denoting extended transmission chains were identified (C1d-C6d), with C1d, C3d, C5d, and C6d being the largest (Figure 4, Supplementary file 2). At least four independent introduction events were detected as the earliest (and synchronous) MRCAs, all dating to April 2021 (including the ancestral nodes of the C3d, C4d, and C6d clades). Based on inferred node dates in the MCC tree, we report a total lineage persistence of seven months (up to November 30, 2021). C2d comprises ‘Mexico’ virus genomes assigned as AY.62, sampled mainly from the state of Yucatán. Clade C4d comprises genomes from Mexico assigned as AY.3, sampled mostly from the central and south of the country (JAL) (Figure 4). Of interest, the C1d and C3d clades represent two independent introduction and spread events of the AY.26 sub-lineage into the country. C1d comprises genomes from Mexico sampled from the north (>60%; BCS, SIN, JAL), followed by central (CMX) and south-eastern states (VER, ROO, and YUC) (Figure 4). The MRCA of the C1d dates to May 2021, denoting a clade persistence of 6 months (from May 2021 to November 2021). Comparably, the C3d comprises genomes from Mexico mostly sampled from the north (37%; SIN, BCS, and SON). Comparably, the MRCA of the C3d dates to April 2021, denoting a clade persistence of 7 months (from April 2021 to November 2021) (Figure 4).

For the largest clades identified, C5d comprises viral genomes assigned as AY.100 (44%), to the parental B.1.617.2 (40%), and to the AY.113 (12%). Within this clade, we observe that the AY.100 and B.1.617.2 genomes are separated by a central sub-cluster of AY.113-assigned genomes (Figure 4). Approximately 70% of the genome sequences within C5d were sampled from Mexico (mostly assigned as AY.100 and AY.113), whilst 30% were sampled from the USA (mostly assigned as B.1.617.2). The majority of the ‘Mexico’ genomes are positioned basally and distally within the clade, sampled from all 32 states, but predominantly from north, centre and southern regions (>50%; represented by CHH, DUR, NLE, CMX, MEX, CAM, YUC, TAB, CHP, and ROO; Figure 4). Thus, the C5d represents the most genetically diverse and geographically widespread clade identified in Mexico. The MRCA of the C5d dates to May, denoting a clade persistence of up to 6 months (from May 2021 to November 2021). C6d is the second largest clade identified, comprising viral genomes from Mexico assigned as AY.20, mostly collected from central region of the country (>60%; represented by CMX, MEX, MOR, MIC, and HID) (Figure 4). Thus, contrasting to C5d, C6d denotes an extended transmission chain with a geographic distribution mainly restricted to central Mexico. The MRCA of the C6d clade dates to April, displaying a clade persistence of seven months (from April 2021 to November 2021).

Our results reveal contrasting epidemiological and evolutionary dynamics between virus lineages circulating in Mexico during the first year of the epidemic, with some identifiable patterns. Both the B.1.1.222 and B.1.1.519 lineages likely originated in Mexico, characterized by single clades denoting extended sustained transmission for over a year. During this time, both lineages dominated in Mexico, and were seeded back and forth between Mexico and the USA, but never dominated across the USA. Thus, the number of publicly available viral genomes from each country reflects sequencing disparities that contrast with the lineage-specific epidemiological patterns observed across regions, highlighting the need to leverage genomic surveillance efforts across neighboring nations using joint strategies (Hill et al., 2021).

Further similarities were observed for the P.1 and B.1.1.7 lineages, for which peaks in genome sampling frequencies coincided with a decrease in cases following the second wave of infection. We further confirm that P.1 and B.1.1.7 did not dominate in Mexico Taboada et al., 2021; Zárate et al., 2022, in contrast to what was observed in other countries such as the UK and USA (Vöhringer et al., 2021; Faria et al., 2021; Washington et al., 2021). Similar observations were independently made for other Latin American countries (some with better genome representation than others, like Brazil Faria et al., 2021), suggesting that the overall epidemiological dynamics of B.1.1.7 in Latin America may have differed substantially from that observed in the USA and UK (Vöhringer et al., 2021; Faria et al., 2021; Washington et al., 2021). Such differences could be explained partly by competition between lineages, exemplified in Mexico by the regional co-circulation of B.1.1.7, P.1, and B.1.1.519. Nonetheless, a lack of representative number of viral genomes for most of these countries prevents exploring such hypothesis at a larger scale, and further highlights the need to strengthen genomic epidemiology-based surveillance. However, the overall evolutionary dynamics observed for these VOC are comparable to those reported in other countries (Vöhringer et al., 2021; Faria et al., 2021; Washington et al., 2021). As an example, in the USA, the earliest introductions reported for the B.1.1.7 lineage were synchronous to those observed in Mexico (occurring between October and November 2020) and were also characterized by a few extended transmissions chains with a distribution often constrained to specific states (Washington et al., 2021). Thus, as drawn from our study, the successful spread of a given virus lineage does not seem to be linked to a higher number of introduction events, but rather to the extent and distribution of transmission chains, with size likely reflecting virus transmissibility (Grenfell et al., 2004).

In Mexico, the introduction and spread of the B.1.1.7, P.1, and B.1.617.2 lineages was characterized by multiple introduction events, resulting in a few successful extended local transmission chains. The epidemiological and evolutionary dynamics of the three VOC show that not only did these coincide temporally, but also revealed multiple and independent transmission chains corresponding to different lineages (and sub-lineages) spreading across the same geographic regions. Our results further revealed several clades belonging to different virus lineages distributed within the south region of the country, suggesting that this area has played a key role in the spread of SARS-CoV-2. Of notice, such pattern is comparable to what has been observed for arboviral epidemics in Mexico (Gutierrez et al., 2023; Thézé et al., 2018). Despite differences in the transmission mechanism between SARS-CoV-2 and arboviruses, we speculate that common epidemiological patterns may have emerged in Mexico due to the dependence of vector populations on human behavior and mobility patterns (Gutierrez et al., 2023; Thézé et al., 2018). Virus transmission rates may also vary within specific regions due to population density coupled with social factors (for example, unregulated migration across borders). Jointly, these observations indicate that the southern region of Mexico (represented by the states of Chiapas, Yucatán, and Quintana Roo) may be a common virus entry and seeding point, emphasising the need for an enhanced virus surveillance in states that share borders with neighboring countries, and highlights the importance of devising social behavior-informed tailored surveillance strategies applied to specific states (i.e. sub-region-specific surveillance).

In general, an increasing growth rate (Rt; defined as the instantaneous reproductive number that measures how an infection multiplies Royal Statistical Society, 2021) observed for different SARS-CoV-2 lineages dominating across specific regions can be partially explained by a fluctuating virus genetic background (i.e. emerging mutations that impact viral fitness) (Vöhringer et al., 2021; Faria et al., 2021; Washington et al., 2021). In light of our results, relative to the parental B.1.1.222 lineage, B.1.1.519 displayed only two amino acid changes within the Spike protein: T478K and P681H (Rodríguez-Maldonado et al., 2021). Mutation T478K locates within the Receptor Binding Domain (RBD), with a potential impact in antibody-mediated neutralization (Liu et al., 2021; Wilhelm et al., 2021). On the other hand, mutation P681H is located upstream the furin cleavage site, and falls within an epitope signal hotspot (Haynes et al., 2021). Thus, it may enhance virus entry (Lubinski et al., 2021), reduce antibody-mediated recognition (Haynes et al., 2021), and confer Type I interferon resistance (Lista et al., 2021). We speculate that at least one of these mutations may have contributed to the dominance of B.1.1.519 over B.1.1.222 by increasing the Rt, as has been observed for other SARS-CoV-2 subpopulations (Martin et al., 2021; Escalera-Zamudio et al., 2023; Dolan et al., 2018). In agreement with this observation, in Mexico, an Rt of 2.9 was estimated for B.1.1.519 compared to an Rt of 1.93 estimated for B.1.1.222 (both calculated during epidemic week 2020–46, coinciding with the early expansion of B.1.1.519 in the country) (Cedro-Tanda et al., 2021).

Notably, the mutations observed for B.1.1.519 were not exclusive to the lineage, as P681H emerged later and independently in B.1.1.7 (and to a lesser extent in P.1, corresponding to 5% of all sampled genomes) (Naveca et al., 2021; Hodcroft, 2021b), whilst mutation T478K subsequently appeared in B.1.617.2 (Liu et al., 2021; Lubinski et al., 2021; Lista et al., 2021). Although assessing the impact of emerging mutations on lineage-specific fitness requires experimental validation, data derived from the natural virus population evidences amino acid changes at site 681 of the Spike protein have been predominantly fixed in VOC, with some mutations likely to yield an evolutionary advantage (Liu et al., 2021; Lubinski et al., 2021; Lista et al., 2021; Hodcroft and Neher, 2021; Pond, 2021; Hodcroft, 2021a). Thus, we propose that a somewhat ‘shared’ genetic background between the B.1.1.519 and B.1.1.7 lineages (as represented by mutation P681H) may have limited the spread of B.1.1.7 across the country. In this context, our finding suggest that the specific dominance and replacement patterns observed in Mexico were driven (to some extent) by lineage-specific mutations impacting growth rate, with competition between virus lineages at a local scale playing an important role.

Nonetheless, lineage-specific replacement and dominance patterns are likely to be shaped by the immune landscape of the local host population (Gupta and Anderson, 1999). In Mexico, relatively widespread and constant exposure levels to genetically similar virus subpopulations for extended periods of time (represented by the B.1.1.222 and B.1.1.519 lineages) may have yielded consistently increasing immunity levels in a somewhat still naïve population (with a nationwide seroprevalence of ~33.5% estimated by December 2020 Basto-Abreu et al., 2022; Muñoz-Medina et al., 2021). As more genetically divergent virus lineages were introduced and began to spread across the country (represented by B.1.1.7 and P.1, and later by B.1.617.2), a shift in the local immune landscape is likely to have occurred, impacted by a viral genetic background prompting (a partial) evasion of the existing immune responses. Supporting this observation, a vaccination rate of above 50% was only reached after December 2021 (Ritchie, 2020; Bhatia et al., 2020), suggesting that immunity levels during the first year of the epidemic mostly depended on virus pre-exposure levels.

In the context of human movement related to the spread of SARS-CoV-2 in Mexico, the fluctuating mobility patterns we observed for the country were consistent with a decrease in cases following the second and third waves of infection, likely reflecting changes in the color-coded system regulating travel restrictions leveraged by the risk of infection (Gobierno de México, 2021b). However, contrasting to our expectations on viral diffusion processes to be associated with local human mobility patterns, the geographic distances and overall human mobility trends observed within Mexico did not correlate with the virus diffusion patterns inferred (represented by B.1.617.2). As geographic distances and human mobility cannot be considered potential predictors of SARS-CoV-2 spread in Mexico, viral diffusion could be explained (to some extent) by human movement across borders. Taking this into consideration, it has been proposed that the spread of SARS-CoV-2 in Mexico is linked to human mobility across USA (for example, see Zárate et al., 2022), as we further evidence in this study by the transmission patterns observed for the B.1.1.222 and the B.1.1.519 lineages at an international scale. However, some of the virus diffusion patterns we observed are also congruent with human migration routes from South and Central America, supporting the notion that SARS-CoV-2 spread in Mexico has been impacted by epidemics within neighboring regions, and further underlines the need to investigate the potential role of irregular migration on virus spread across geographic regions (Kraemer et al., 2020b; IOM Global Migration Data Analysis Centre, 2021; Varela Huerta, 2018; París-Pombo, 2016).

Limitations of our study include uncertainty in determining source locations for virus introduction events into the country (for most lineages), restricted by regional genome sampling biases (Kalkauskas et al., 2021; De Maio et al., 2015). This is further impacted by (i) an uneven genome sampling across foreign locations and within the country, and (ii) by a poor viral genome representation for many countries in Central and South America (GISAID, 2008; Gutierrez et al., 2021). Such biases are also likely to affect the viral diffusion reconstructions we present, likely rendering them incomplete. However, as SARS-CoV-2 genome sampling and sequencing in Mexico has been sufficient, we are still able to robustly quantify and characterize lineage-specific transmission chains. It is worth highlighting that a differential proportion of cumulative viral genomes sequenced per state does not necessarily mirror the geographic distribution and extension of the transmission chains identified, but rather represents a fluctuating intensity in virus genome sampling and sequencing through time. This underscores the importance of conducting phylogenetic inference-based analyses to explore viral spread, as opposed to relying solely on estimates derived from genome data frequency across time and space. A more homogeneous sampling across the country is unlikely to impact our main findings, but could (i) help pinpoint additional clades we are currently unable to detect, (ii) provide further details on the geographic distribution of clades across other regions of the country, and (iii) deliver a higher resolution for the viral spread reconstructions we present. Overall, our study prompts the need to better understand the impact of land-based migration across national borders, and encourages joint virus surveillance efforts in the Americas.

Virus genome IDs and GISAID accession numbers for the sequences used in each dataset are provided in the Supplementary file 1 file. All genomic and epidemiological data supporting the findings of this study is publicly available from GISAID/GenBank, from the Ministry Of Health Mexico (Gobierno de Mexico - Dirección General de Epidemiología, 2021), and/or from the 'Our World in Data' coronavirus pandemic web portal (Ritchie, 2020). For the GISAID data used, the corresponding acknowledgement table is available on the 'GISAID Data Acknowledgement Locator' under the EPI_SET_20220405qd and EPI_SET_20220215at keys (GISAID, 2008). Our bioinformatic pipeline implementing a migration data and phylogenetically-informed sequence subsampling approach is publicly available at https://github.com/rhysinward/Mexico_subsampling (copy archived at Inward, 2022).

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Author details

1. Hugo G Castelán-Sánchez

   1. Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex), Mexico City, Mexico
   2. Programa de Investigadoras e Investigadores por México, Consejo Nacional de Ciencia y Tecnología, Mexico City, Mexico

   Contribution

   Formal analysis, Methodology, Writing – review and editing

   Contributed equally with

   Luis Delaye, Rhys PD Inward, Simon Dellicour and Bernardo Gutierrez

   Competing interests

   No competing interests declared

2. Luis Delaye

   1. Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex), Mexico City, Mexico
   2. Departamento de Ingeniería Genética, CINVESTAV-Unidad Irapuato, Guanajuato, Mexico

   Contribution

   Formal analysis, Methodology, Writing – review and editing

   Contributed equally with

   Hugo G Castelán-Sánchez, Rhys PD Inward, Simon Dellicour and Bernardo Gutierrez

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4193-2720

3. Rhys PD Inward

   Department of Biology, University of Oxford, Oxford, United Kingdom

   Contribution

   Data curation, Methodology, Writing – review and editing

   Contributed equally with

   Hugo G Castelán-Sánchez, Luis Delaye, Simon Dellicour and Bernardo Gutierrez

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0016-661X

4. Simon Dellicour

   1. Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium
   2. Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium

   Contribution

   Formal analysis, Validation, Investigation, Writing – review and editing

   Contributed equally with

   Hugo G Castelán-Sánchez, Luis Delaye, Rhys PD Inward and Bernardo Gutierrez

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9558-1052

5. Bernardo Gutierrez

   1. Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex), Mexico City, Mexico
   2. Department of Biology, University of Oxford, Oxford, United Kingdom

   Contribution

   Formal analysis, Methodology

   Contributed equally with

   Hugo G Castelán-Sánchez, Luis Delaye, Rhys PD Inward and Simon Dellicour

   Competing interests

   No competing interests declared

6. Natalia Martinez de la Vina

   Department of Biology, University of Oxford, Oxford, United Kingdom

   Contribution

   Data curation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

7. Celia Boukadida

   1. Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex), Mexico City, Mexico
   2. Centro de Investigación en Enfermedades Infecciosas, Instituto Nacional de Enfermedades Respiratorias, Mexico City, Mexico

   Contribution

   Data curation, Visualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1744-0083

8. Oliver G Pybus

   1. Department of Biology, University of Oxford, Oxford, United Kingdom
   2. Department of Pathobiology, Royal Veterinary College, London, United Kingdom

   Contribution

   Supervision, Validation

   Competing interests

   No competing interests declared

9. Guillermo de Anda Jáuregui

   1. Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex), Mexico City, Mexico
   2. Programa de Investigadoras e Investigadores por México, Consejo Nacional de Ciencia y Tecnología, Mexico City, Mexico
   3. Instituto Nacional de Medicina Genómica, Mexico City, Mexico

   Contribution

   Data curation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

10. Plinio Guzmán

    Astronomer LTD, Mexico City, Mexico

    Contribution

    Data curation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

11. Marisol Flores-Garrido

    1. Escuela Nacional de Estudios Superiores, Universidad Nacional Autónoma de México, Mexico City, Mexico
    2. Departamento de Ciencias de la Computación, CINVESTAV-IPN, Mexico City, Mexico

    Contribution

    Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

12. Óscar Fontanelli

    1. Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex), Mexico City, Mexico
    2. Departamento de Ingeniería Genética, CINVESTAV-Unidad Irapuato, Guanajuato, Mexico

    Contribution

    Data curation, Formal analysis, Investigation

    Competing interests

    No competing interests declared

13. Maribel Hernández Rosales

    1. Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex), Mexico City, Mexico
    2. Departamento de Ingeniería Genética, CINVESTAV-Unidad Irapuato, Guanajuato, Mexico

    Contribution

    Data curation, Formal analysis

    Competing interests

    No competing interests declared

14. Amilcar Meneses

    1. Escuela Nacional de Estudios Superiores, Universidad Nacional Autónoma de México, Mexico City, Mexico
    2. Departamento de Ciencias de la Computación, CINVESTAV-IPN, Mexico City, Mexico

    Contribution

    Data curation, Investigation

    Competing interests

    No competing interests declared

15. Gabriela Olmedo-Alvarez

    1. Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex), Mexico City, Mexico
    2. Departamento de Ingeniería Genética, CINVESTAV-Unidad Irapuato, Guanajuato, Mexico

    Contribution

    Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

16. Alfredo Heriberto Herrera-Estrella

    1. Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex), Mexico City, Mexico
    2. Laboratorio de expresión génica y desarrollo en hongos, CINVESTAV-Unidad Irapuato, Irapuato, Mexico

    Contribution

    Investigation, Visualization, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

17. Alejandro Sánchez-Flores

    1. Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex), Mexico City, Mexico
    2. Unidad Universitaria de Secuenciación Masiva y Bioinformática, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Chamilpa, Mexico

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

18. José Esteban Muñoz-Medina

    1. Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex), Mexico City, Mexico
    2. Coordinación de Calidad de Insumos y Laboratorios Especializados, Instituto Mexicano del Seguro Social, Mexico City, Mexico

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-1289-4457

19. Andreu Comas-García

    1. Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex), Mexico City, Mexico
    2. Facultad de Medicina y Centro de Investigación en Ciencias de la Salud y Biomedicina, Universidad Autónoma de San Luis Potosí, San Luis Potosí, Mexico

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

20. Bruno Gómez-Gil

    1. Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex), Mexico City, Mexico
    2. Centro de Investigación en Alimentación y Desarrollo-CIAD, Unidad Regional Mazatlán en Acuicultura y Manejo Ambiental, Sinaloa, Mexico

    Contribution

    Conceptualization

    Competing interests

    No competing interests declared

21. Selene Zárate

    1. Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex), Mexico City, Mexico
    2. Posgrado en Ciencias Genómicas, Universidad Autónoma de la Ciudad de México, Mexico City, Mexico

    Contribution

    Data curation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-1034-204X

22. Blanca Taboada

    1. Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex), Mexico City, Mexico
    2. Departamento de Genética del Desarrollo y Fisiología Molecular, Universidad Nacional Autónoma de México, Cuernavaca, Mexico

    Contribution

    Data curation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-1896-5962

23. Susana López

    1. Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex), Mexico City, Mexico
    2. Departamento de Genética del Desarrollo y Fisiología Molecular, Universidad Nacional Autónoma de México, Cuernavaca, Mexico

    Contribution

    Conceptualization, Writing – review and editing

    Competing interests

    No competing interests declared

24. Carlos F Arias

    1. Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex), Mexico City, Mexico
    2. Departamento de Genética del Desarrollo y Fisiología Molecular, Universidad Nacional Autónoma de México, Cuernavaca, Mexico

    Contribution

    Conceptualization, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-3130-4501

25. Moritz UG Kraemer

    1. Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex), Mexico City, Mexico
    2. Department of Biology, University of Oxford, Oxford, United Kingdom

    Contribution

    Conceptualization, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

26. Antonio Lazcano

    1. Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex), Mexico City, Mexico
    2. Facultad de Ciencias, Universidad Nacional Autónoma de Méxic, Mexico City, Mexico

    Contribution

    Conceptualization, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

27. Marina Escalera Zamudio

    1. Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex), Mexico City, Mexico
    2. Department of Biology, University of Oxford, Oxford, United Kingdom

    Contribution

    Conceptualization, Data curation, Formal analysis, Supervision, Validation, Investigation, Methodology, Writing - original draft, Project administration, Writing – review and editing

    For correspondence

    marina.escalerazamudio@zoo.ox.ac.uk

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-4773-2773

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