Country-Wise Forecast Model for the Effective Reproduction Number R_t of Coronavirus Disease
Abstract
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 of R_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 of R_t and its temporal evolution within a timeframe. With such models, we evaluate R_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 calculate R_t to serve as valuable input to support decision-making related to controlling the spread of SARS-CoV-2.
Additional Information
© 2020 Medina-Ortiz, Contreras, Barrera-Saavedra, Cabas-Mora and Olivera-Nappa. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Received: 02 May 2020; Accepted: 02 July 2020; Published: 17 July 2020. The authors gratefully acknowledge support from the Centre for Biotechnology and Bioengineering-CeBiB (PIA project FB0001, Conicyt, Chile). DM-O gratefully acknowledges Conicyt, Chile, for Ph.D. fellowship 21181435. DM-O greatly thanks Cristofer Quiroz for his support in the search for databases and related services for the development of forecast models. Data Availability Statement: Publicly available datasets were analyzed in this study. This data can be found here: https://www.worldometers.info/coronavirus/, https://www.gob.cl/coronavirus/cifrasoficiales/, and https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data. Author Contributions: DM-O and SC: conceptualization. DM-O: methodology. ÁO-N, DM-O, and SC: validation and writing, review, and editing. DM-O, SC, GC-M, and YB-S: investigation. ÁO-N and DM-O: supervision and project administration. ÁO-N: funding resources. All authors contributed to the article and approved the submitted version. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.Attached Files
Published - fphy-08-00304.pdf
Files
| Name | Size | Download all |
|---|---|---|
|
md5:0dabc9a14bbcdd2ce68575148ff208bd
|
1.2 MB | Preview Download |
Additional details
- Eprint ID
- 105167
- Resolver ID
- CaltechAUTHORS:20200831-105818791
- PIA FB0001
- Comisión Nacional de Investigación Científica y Tecnológica (CONICYT)
- Created
-
2020-09-08Created from EPrint's datestamp field
- Updated
-
2021-11-16Created from EPrint's last_modified field
- Caltech groups
- COVID-19