Country-wise forecast model for the effective reproduction number Rt of coronavirus disease
Author
dc.contributor.author
Medina Ortiz, David
Author
dc.contributor.author
Silva Contreras, Sebastián Jesús
Author
dc.contributor.author
Barrera Saavedra, Yasna
Author
dc.contributor.author
Cabas Moras, Gabriel
Author
dc.contributor.author
Olivera Nappa, Álvaro
Admission date
dc.date.accessioned
2020-11-10T14:07:53Z
Available date
dc.date.available
2020-11-10T14:07:53Z
Publication date
dc.date.issued
2020
Cita de ítem
dc.identifier.citation
Frontiers in Physics July 2020 | Volume 8 | Article 304
es_ES
Identifier
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10.3389/fphy.2020.00304
Identifier
dc.identifier.uri
https://repositorio.uchile.cl/handle/2250/177633
Abstract
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Due to the particularities of SARS-CoV-2, public health policies have played a crucial role in the control of the COVID-19 pandemic. Epidemiological parameters for assessing the stage of the outbreak, such as the Effective Reproduction Number (R-t), are not always straightforward to calculate, raising barriers between the scientific community and non-scientific decision-making actors. The combination of estimators ofR(t)with elaborated Machine Learning-based forecasting techniques provides a way to support decision-making when assessing governmental plans of action. In this work, we develop forecast models applying logistic growth strategies and auto-regression techniques based on Auto-Regressive Integrated Moving Average (ARIMA) models for each country that records information about the COVID-19 outbreak. Using the forecast for the main variables of the outbreak, namely the number of infected (I), recovered (R), and dead (D) individuals, we provide a real-time estimation ofR(t)and its temporal evolution within a timeframe. With such models, we evaluateR(t)trends at the continental and country levels, providing a clear picture of the effect governmental actions have had on the spread. We expect this methodology of combining forecast models for raw data to calculateR(t)to serve as valuable input to support decision-making related to controlling the spread of SARS-CoV-2.
es_ES
Patrocinador
dc.description.sponsorship
Centre for Biotechnology and Bioengineering-CeBiB (PIA project, Conicyt, Chile)
FB0001
Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)
21181435