Publicación:
Use of SARS-CoV-2 genomes to estimate the effective reproductive number (RT) in Peru during march – april 2020 [Uso de genomas de SARS-CoV-2 para la estimación del número reproductivo efectivo (RT) en Perú durante marzo y abril del 2020]
Use of SARS-CoV-2 genomes to estimate the effective reproductive number (RT) in Peru during march – april 2020 [Uso de genomas de SARS-CoV-2 para la estimación del número reproductivo efectivo (RT) en Perú durante marzo y abril del 2020]
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Fecha
2021
Autores
Romero P.E.
Sánchez-Yupari M.
Montero S.
Tsukayama P.
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Editor
Instituto Nacional de Salud
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Abstracto
The understanding of COVID-19, caused by the SARS-CoV-2, is essential to improve evidence-based public health policies. The effective reproductive number (Rt) in Peru was estimated using information from 113 complete genomes sequenced by the Instituto Nacional de Salud del Perú (INS), available in the GISAID public database. The Rt trend during March and April of 2020 was found to be similar to results from other epidemiological reports. The Rt decreased during the first two weeks of March. Its lowest value was reported during the week after the quarantine began. The Rt increased moderately after the second week of April. The implication of early decisions taken to mitigate the transmission are discussed. Genomic surveillance will be necessary to understand the transmission and evolution of SARS-CoV-2 in Peru, and will complement the epidemiological information. © 2021, Instituto Nacional de Salud. All rights reserved.
Descripción
Este trabajo fue financiado por el Fondo Nacional de Desarrollo Cient?fico y Tecnol?gico y de Innovaci?n Tecnol?gica (Fondecyt-Per?) en el marco del ?Proyecto de Mejoramiento y Ampliaci?n de los Servicios del Sistema Nacional de Ciencia, Tecnolog?a e Innovaci?n Tecnol?gica? [N?mero de contrato 34?2019-FONDECYT-BM-INC. INV.], y por el CONCYTEC-FONDECYT en el marco del concurso ?Proyectos Especiales: Respuesta al COVID-19 2020-01? [n?mero de contrato 046-2020-FONDECYT].
Palabras clave
Surveillance (source: MeSH NLM),
Bayesian analysis,
COVID-19,
Epidemiological models,
Genomics,
Molecular epidemiology,
Nucleotide databases,
Peru,
Phyloge-nomics,
SARS-CoV-2