COVID-19 in Latin America: Contrasting phylodynamic inference with epidemiological surveillance. (Molecular epidemiology of COVID-19 in Latin America)

BackgroundSARS-CoV-2 revealed important gaps in infectious disease surveillance. Molecular epidemiology can help monitoring and adapting traditional surveillance to surpass those limitations. This work aims to contrast data driven from traditional surveillance with parameters inferred from molecular epidemiology in Latin America (LATAM) MethodsWe obtained epidemiological data up to 4th June, 2020. We estimated Effective Reproductive Number (Re) and epidemic curves using maximum likelihood (ML). SARS-CoV-2 genomes were obtained from GISAID up to June 4th 2020. We aligned sequences, generated a ML phylogenetic tree, and ran a coalescent model Birth Death SIR. Phylodynamic analysis was performed for inferring Re, number of infections and date of introduction. FindingsA total of 1,144,077 cases were reported up to 4th June 2020. Countries with the largest cumulative cases were Chile, Peru and Panama. We found at least 18 different lineages circulating, with a predominance of B.1 and B.1.1. We inferred an underestimation of the daily incident cases. When contrasting observed and inferred Re, we did not find statistically significant differences except for Chile and Mexico. Temporal analysis of the introduction of SARS-CoV-2 suggested a detection lag of at least 21 days. InterpretationOur results support that epidemiological and genomic surveillance are two complementary approaches. Even with a low number of genomes proper estimations of Re could be performed. We suggest that countries, especially developing countries, should consider to add genomic surveillance to their systems for monitoring and adapting epidemiological control of SARS-CoV-2.