A prospective evaluation of AI-augmented epidemiology to forecast COVID-19 in the USA and Japan

A prospective evaluation of AI-augmented epidemiology to forecast COVID-19 in the USA and Japan

Abstract The COVID-19 pandemic has highlighted the global need for reliable models of disease spread. We propose an AI-augmented forecast modeling framework that provides daily predictions of the expected number of confirmed COVID-19 deaths, cases, and hospitalizations during the following 4 weeks....

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Autores principales: Sercan Ö. Arık, Joel Shor, Rajarishi Sinha, Jinsung Yoon, Joseph R. Ledsam, Long T. Le, Michael W. Dusenberry, Nathanael C. Yoder, Kris Popendorf, Arkady Epshteyn, Johan Euphrosine, Elli Kanal, Isaac Jones, Chun-Liang Li, Beth Luan, Joe Mckenna, Vikas Menon, Shashank Singh, Mimi Sun, Ashwin Sura Ravi, Leyou Zhang, Dario Sava, Kane Cunningham, Hiroki Kayama, Thomas Tsai, Daisuke Yoneoka, Shuhei Nomura, Hiroaki Miyata, Tomas Pfister
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
Computer applications to medicine. Medical informatics
R858-859.7
Acceso en línea:https://doaj.org/article/5a7e3925daed42c8b7a7024387fa5045
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