Este articulo es un Preprint
Los preprints son informes de investigación preliminares que no han sido certificados por revisión por pares. No deben considerarse para guiar la práctica clínica o los comportamientos relacionados con la salud y no deben publicarse en los medios como información establecida.
Los preprints publicados en línea permiten a los autores recibir comentarios rápidamente, y toda la comunidad científica puede evaluar de forma independiente el trabajo y responder adecuadamente. Estos comentarios se publican junto con los preprints para que cualquiera pueda leer y servir como una revisión pospublicación.
Estimating the infection fatality risk of COVID-19 in New York City, March 1-May 16, 2020
Preprint
en En
| PREPRINT-MEDRXIV
| ID: ppmedrxiv-20141689
ABSTRACT
During March 1-May 16, 2020, 191,392 laboratory-confirmed COVID-19 cases were diagnosed and reported and 20,141 confirmed and probable COVID-19 deaths occurred among New York City (NYC) residents. We applied a network model-inference system developed to support the Citys pandemic response to estimate underlying SARS-CoV-2 infection rates. Based on these estimates, we further estimated the infection fatality risk (IFR) for 5 age groups (i.e. <25, 25-44, 45-64, 65-74, and 75+ years) and all ages overall, during March 1-May 16, 2020. We estimated an overall IFR of 1.45% (95% Credible Interval 1.09-1.87%) in NYC. In particular, weekly IFR was estimated as high as 6.1% for 65-74 year-olds and 17.0% for 75+ year-olds. These results are based on more complete ascertainment of COVID-19-related deaths in NYC and thus likely more accurately reflect the true, higher burden of death due to COVID-19 than previously reported elsewhere. It is thus crucial that officials account for and closely monitor the infection rate and population health outcomes and enact prompt public health responses accordingly as the pandemic unfolds.
cc_no
Texto completo:
1
Colección:
09-preprints
Base de datos:
PREPRINT-MEDRXIV
Tipo de estudio:
Experimental_studies
/
Prognostic_studies
/
Rct
Idioma:
En
Año:
2020
Tipo del documento:
Preprint