Data-driven mechanistic framework with stratified immunity and effective transmissibility for COVID-19 scenario projections

Scenario-based modeling frameworks have been widely used to support policy-making at state and federal levels in the United States during the COVID-19 response. While custom-built models can be used to support one-off studies, sustained updates to projections under changing pandemic conditions requi...

全面介紹

書目詳細資料

Principais autores:	Przemyslaw Porebski, Srinivasan Venkatramanan, Aniruddha Adiga, Brian Klahn, Benjamin Hurt, Mandy L. Wilson, Jiangzhuo Chen, Anil Vullikanti, Madhav Marathe, Bryan Lewis
格式:	Artigo
語言:	Inglês
出版:	Elsevier 2024-06-01
叢編:	Epidemics
主題:	COVID-19 Scenario projections Compartmental model Transmission dynamics Variant characteristics Population immunity
在線閱讀:	http://www.sciencedirect.com/science/article/pii/S1755436524000227
標簽:	添加標簽 沒有標簽, 成為第一個標記此記錄!