Early Insights from Statistical and Mathematical Modeling of Key Epidemiologic Parameters of COVID-19
ContributorsBiggerstaff, Matthew; Cowling, Benjamin J.; Cucunubá, Zulma M.; Dinh, Linh; Ferguson, Neil M.; Gao, Huizhi ; Hill, Verity ; Imai, Natsuko ; Johansson, Michael A.; Kada, Sarah ; Morgan, Oliver; Pastore y Piontti, Ana; Polonsky, Jonathan Aaron; Prasad, Pragati Venkata; Quandelacy, Talia M.; Rambaut, Andrew; Tappero, Jordan W.; Vandemaele, Katelijn A.; Vespignani, Alessandro; Warmbrod, K. Lane; Wong, Jessica Y.; for the WHO COVID-19 Modelling Parameters Group
Published inEmerging infectious diseases, vol. 26, no. 11, e201074
Publication date2020-11
Abstract
Keywords
- 2019 novel coronavirus disease
- COVID-19
- SARS-CoV-2
- World Health Organization
- Coronavirus
- Epidemiological parameters
- Mathematical modeling
- Severe acute respiratory syndrome coronavirus 2
- Viruses
- Zoonoses
Affiliation Not a UNIGE publication
Funding
- Medical Research Council - [MC_PC_19012]
- UK Research and Innovation - MRC Centre for Global Infectious Disease Analysis [MR/R015600/1]
Citation (ISO format)
BIGGERSTAFF, Matthew et al. Early Insights from Statistical and Mathematical Modeling of Key Epidemiologic Parameters of COVID-19. In: Emerging infectious diseases, 2020, vol. 26, n° 11, p. e201074. doi: 10.3201/eid2611.201074
Main files (1)
Article (Published version)
Secondary files (1)
Identifiers
- PID : unige:178489
- DOI : 10.3201/eid2611.201074
- PMID : 32917290
- PMCID : PMC7588530
Commercial URLhttps://wwwnc.cdc.gov/eid/article/26/11/20-1074_article
ISSN of the journal1080-6040