A simple model for fitting mild, severe, and known cases during an epidemic with an application to the current SARS-CoV-2 pandemic

One of the major difficulties with modelling an ongoing epidemic is that often data is limited or incomplete, making it hard to estimate key epidemic parameters and outcomes (e.g. attack rate, peak time, reporting rate, reproduction number). In the current study, we present a model for data-fitting...

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Detalles Bibliográficos
Autores principales: Betti, Matthew I., Heffernan, Jane M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: KeAi Publishing 2021
Materias:
Special issue on Modelling and Forecasting the 2019 Novel Coronavirus (2019-nCoV) Transmission; Edited by Prof. Carlos Castillo-Chavez, Prof. Gerardo Chowell-Puente, Prof. Ping Yan, Prof. Jianhong Wu
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7833529/
https://www.ncbi.nlm.nih.gov/pubmed/33521406
http://dx.doi.org/10.1016/j.idm.2021.01.002
Descripción
Sumario:One of the major difficulties with modelling an ongoing epidemic is that often data is limited or incomplete, making it hard to estimate key epidemic parameters and outcomes (e.g. attack rate, peak time, reporting rate, reproduction number). In the current study, we present a model for data-fitting limited infection case data which provides estimates for important epidemiological parameters and outcomes. The model can also provide reasonable short-term (one month) projections. We apply the model to the current and ongoing COVID-19 outbreak in Canada both at the national and provincial/territorial level.

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