Colombia's first confirmed SARS-CoV-2 infection was reported on March 6, 2020, in a traveler returning from Milan, Italy. By April 2022, over 6 million infections and 135,000 deaths had been reported. The epidemic was characterized by four pandemic waves with exponential case growth. Various mitigation strategies, including mobility restrictions, mask use advice, physical distancing, and vaccination, were implemented. However, the continued transmission suggests these measures may have been insufficient, potentially due to changes in the virus's transmissibility. SARS-CoV-2 genetic diversity, categorized using lineages and the Pango nomenclature system, has revealed variants with enhanced transmissibility, virulence, or immune evasion. The WHO labels high-risk variants as "Variants of Concern" (VOC), including Alpha, Beta, Gamma, Delta, and Omicron. Genomic surveillance is crucial for identifying and understanding the dynamics of these variants. While global surveillance has provided valuable data, fine-scale analyses at the national level are needed to understand local transmission dynamics, particularly for variants like Mu (B.1.621), initially prevalent in Colombia but only sporadically elsewhere. This study aimed to analyze the epidemiological dynamics of SARS-CoV-2 in Colombia during the first two years of the pandemic using genomic data, focusing on the temporal patterns and transmission characteristics of circulating variants. This study also aimed to investigate the correlation between these genomic data and non-pharmaceutical interventions (NPIs), as well as the overall evolution of the virus during the first two years of the pandemic in Colombia.

The literature extensively documents the global spread and evolution of SARS-CoV-2, highlighting the emergence of Variants of Concern (VOCs) with increased transmissibility and immune evasion. Studies have shown the importance of genomic surveillance in tracking variant spread and informing public health responses. Several studies used phylogenetic methods to estimate the transmission dynamics and effective reproductive number (R) of different variants. However, detailed, fine-scale genomic epidemiological analyses at the national level remain limited, particularly in some regions like Colombia. There is a gap in understanding how local context, including the implementation of NPIs and vaccination programs, influence the dynamics of different variants. This study aimed to fill this gap by providing a comprehensive analysis of SARS-CoV-2 genomic data in Colombia, which will be important in shaping national and regional pandemic management strategies.

This study utilized a combination of novel genomic sequencing data and publicly available data from GISAID. Nasopharyngeal swabs were collected from 10,674 residents in Bogotá, Cali, and Córdoba state. RNA was extracted, and sequencing libraries were prepared using the ARTIC Network protocol. A total of 610 novel sequences were generated, supplemented by 13,444 sequences downloaded from GISAID, resulting in a total of 14,049 complete SARS-CoV-2 genomes. Bayesian phylogenetic analyses were conducted using BEAST software, estimating the time of introduction, effective reproductive number (R), and effective population size (Ne) for the ten most prevalent variants. The analysis incorporated mobility data and stringency index data to assess the influence of non-pharmaceutical interventions (NPIs) on variant dynamics. The study divided the pandemic period into four phases based on epidemiological waves and the implementation of control measures, analyzing each phase separately. Phylogenetic trees were constructed to visualize the evolutionary relationships among the variants. The correlation between the changes in effective reproductive numbers (R) and several factors such as mobility, stringency index, vaccination rates, and mask use were studied to quantify the impact of interventions on the different variants.

The analysis revealed 188 SARS-CoV-2 Pango lineages circulating in Colombia during the first two years of the pandemic. The dominant variant fluctuated considerably, with shifts between variants of different transmissibilities. The Mu variant (B.1.621) showed the highest potential for transmission. The study detailed the dynamics of ten predominant variants across four distinct pandemic waves, analyzing their effective reproductive numbers (R) and effective population sizes (Ne). The researchers observed significant correlations between R_e changes and various factors like mobility, stringency index, and vaccination rates, suggesting the influence of public health interventions. For instance, the Mu variant's high R_e, alongside its partial immune escape, might explain its dominance despite the prior introduction of Alpha and Delta variants. The timing of variant emergence, the duration of their circulation, and their relative prevalence were mapped across Colombia's 32 states. The study noted higher sequencing efforts in more populated states like Antioquia, Cundinamarca, and Valle del Cauca. The analysis also showed that the high transmission and effective population sizes of each variant could be linked to increased mobility and reduced stringency in the implementation of control measures. The study identified the significant influence of mobility, vaccination coverage, and stringency index on the effective reproductive number for many of the variants considered.

This study provides a detailed genomic epidemiological analysis of SARS-CoV-2 variants in Colombia during the first two years of the pandemic, offering insights into transmission dynamics and the impact of public health interventions. The findings emphasize the importance of continuous genomic surveillance in tracking the evolution and spread of novel SARS-CoV-2 variants. The significant fluctuations in dominant variants underscore the need for adaptable public health strategies. The observed correlations between R_e and various factors highlight the effectiveness of NPIs in mitigating transmission. The study's findings are relevant to informing future pandemic preparedness and response strategies. The methods used in this study could be adapted for other countries facing similar challenges in managing infectious disease outbreaks. The study also highlights the importance of international collaboration in genomic surveillance, as evidenced by the use of GISAID data.

This study highlights the dynamic evolution of SARS-CoV-2 in Colombia, emphasizing the continuous need for genomic surveillance to inform public health interventions. The study demonstrates the importance of integrating genomic data with epidemiological and public health data. The identified correlations between variant dynamics and control measures suggest the effectiveness of these interventions. Continued research could focus on further exploring the interplay between specific mutations, immune escape, and the efficacy of vaccines against emerging variants. Enhanced genomic sequencing efforts and data sharing are also crucial for improving real-time monitoring of SARS-CoV-2 evolution.

The study's reliance on publicly available data from GISAID might introduce biases related to sampling and data reporting inconsistencies across different regions of Colombia. The analysis focuses on the ten most prevalent variants, potentially overlooking the contributions of less frequent lineages to the overall transmission dynamics. The study's conclusions are primarily based on associations, not establishing definitive causal relationships between factors and variant spread. While mobility and stringency index data provide important context, other factors might affect variant dynamics.