Cargando…
Predicting COVID-19 Cases in South Korea Using Stringency and Niño Sea Surface Temperature Indices
Most coronavirus disease 2019 (COVID-19) models use a combination of agent-based and equation-based models with only a few incorporating environmental factors in their prediction models. Many studies have shown that human and environmental factors play huge roles in disease transmission and spread,...
Autores principales: | Necesito, Imee V., Velasco, John Mark S., Jung, Jaewon, Bae, Young Hye, Yoo, Younghoon, Kim, Soojun, Kim, Hung Soo |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9204014/ https://www.ncbi.nlm.nih.gov/pubmed/35719622 http://dx.doi.org/10.3389/fpubh.2022.871354 |
Ejemplares similares
-
Understanding chaos in COVID-19 and its relationship to stringency index: Applications to large-scale and granular level prediction models
por: Necesito, Imee V., et al.
Publicado: (2022) -
Scrub Typhus Incidence Modeling with Meteorological Factors in South Korea
por: Kwak, Jaewon, et al.
Publicado: (2015) -
Future Climate Data from RCP 4.5 and Occurrence of Malaria in Korea
por: Kwak, Jaewon, et al.
Publicado: (2014) -
COVID-19 Variants and Transfer Learning for the Emerging Stringency Indices
por: Sohail, Ayesha, et al.
Publicado: (2022) -
Effects of Social Mobility and Stringency Measures on the COVID-19 Outcomes: Evidence From the United States
por: Sun, Jianmin, et al.
Publicado: (2021)