Forecasting COVID-19 daily cases using phone call data

Rostami-Tabar, Bahman ORCID: https://orcid.org/0000-0002-3730-0045 and Rendon-Sanchez, Juan F. 2021. Forecasting COVID-19 daily cases using phone call data. Applied Soft Computing 100 , 106932. 10.1016/j.asoc.2020.106932

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Abstract

The need to forecast COVID-19 related variables continues to be pressing as the epidemic unfolds. Different efforts have been made, with compartmental models in epidemiology and statistical models such as AutoRegressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS) or computing intelligence models. These efforts have proved useful in some instances by allowing decision makers to distinguish different scenarios during the emergency, but their accuracy has been disappointing, forecasts ignore uncertainties and less attention is given to local areas. In this study, we propose a simple Multiple Linear Regression model, optimised to use phone call data to forecast the number of daily confirmed cases. Moreover, we produce a probabilistic forecast that allows decision makers to better deal with risk. Our proposed approach outperforms ARIMA, ETS, Seasonal Naive, Prophet and a regression model without call data, evaluated by three point forecast error metrics, one prediction interval and two probabilistic forecast accuracy measures. The simplicity, interpretability and reliability of the model, obtained in a careful forecasting exercise, is a meaningful contribution to decision makers at local level who acutely need to organise resources in already strained health services. We hope that this model would serve as a building block of other forecasting efforts that on the one hand would help front-line personal and decision makers at local level, and on the other would facilitate the communication with other modelling efforts being made at the national level to improve the way we tackle this pandemic and other similar future challenges.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Business (Including Economics) Computer Science & Informatics
Publisher:	Elsevier
ISSN:	1568-4946
Date of First Compliant Deposit:	3 December 2020
Date of Acceptance:	18 November 2020
Last Modified:	30 Nov 2024 08:30
URI:	https://orca.cardiff.ac.uk/id/eprint/136777

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