当前位置： SCI文献检索 > 期刊下所有文献 > Forecasting COVID-19 daily cases using phone call data.

Forecasting COVID-19 daily cases using phone call data.

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.

journal_name

Appl Soft Comput

journal_title

Applied soft computing

authors

Rostami-Tabar B,Rendon-Sanchez JF

doi

10.1016/j.asoc.2020.106932

keywords:

["ARIMA","COVID-19","Call centres","Exponential smoothing","Probabilistic forecasting,","Regression","Time series forecasting"]

subject

Has Abstract

pub_date

2021-03-01 00:00:00

pages

106932

eissn

1568-4946

issn

1872-9681

pii

S1568-4946(20)30870-X

journal_volume

100

pub_type

杂志文章

相关文献

文献大全
  • Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network.

    abstract：:COVID-19, as an infectious disease, has shocked the world and still threatens the lives of billions of people. Recently, the detection of coronavirus (COVID-19) is a critical task for the medical practitioner. Unfortunately, COVID-19 spreads so quickly between people and approaches millions of people worldwide in few ...

    journal_title：Applied soft computing

    pub_type： 杂志文章

    doi：10.1016/j.asoc.2020.106906

    authors： Shaban WM,Rabie AH,Saleh AI,Abo-Elsoud MA

    更新日期：2021-02-01 00:00:00

  • Learning distinctive filters for COVID-19 detection from chest X-ray using shuffled residual CNN.

    abstract：:COVID-19 is a deadly viral infection that has brought a significant threat to human lives. Automatic diagnosis of COVID-19 from medical imaging enables precise medication, helps to control community outbreak, and reinforces coronavirus testing methods in place. While there exist several challenges in manually inferrin...

    journal_title：Applied soft computing

    pub_type： 杂志文章

    doi：10.1016/j.asoc.2020.106744

    authors： Karthik R,Menaka R,M H

    更新日期：2021-02-01 00:00:00

  • A novel extended approach under hesitant fuzzy sets to design a framework for assessing the key challenges of digital health interventions adoption during the COVID-19 outbreak.

    abstract：:In recent years, Digital Technologies (DTs) are becoming an inseparable part of human lives. Thus, many scholars have conducted research to develop new tools and applications. Processing information, usually in the form of binary code, is the main task in DTs, which is happening through many devices, including compute...

    journal_title：Applied soft computing

    pub_type： 杂志文章

    doi：10.1016/j.asoc.2020.106613

    authors： Mardani A,Saraji MK,Mishra AR,Rani P

    更新日期：2020-11-01 00:00:00

  • Composite Monte Carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction.

    abstract：:In the advent of the novel coronavirus epidemic since December 2019, governments and authorities have been struggling to make critical decisions under high uncertainty at their best efforts. In computer science, this represents a typical problem of machine learning over incomplete or limited data in early epidemic Com...

    journal_title：Applied soft computing

    pub_type： 杂志文章

    doi：10.1016/j.asoc.2020.106282

    authors： Fong SJ,Li G,Dey N,Crespo RG,Herrera-Viedma E

    更新日期：2020-08-01 00:00:00

  • A deep-learning based multimodal system for Covid-19 diagnosis using breathing sounds and chest X-ray images.

    abstract：:Covid-19 has become a deadly pandemic claiming more than three million lives worldwide. SARS-CoV-2 causes distinct pathomorphological alterations in the respiratory system, thereby acting as a biomarker to aid its diagnosis. A multimodal framework (Ai-CovScan) for Covid-19 detection using breathing sounds, chest X-ray...

    journal_title：Applied soft computing

    pub_type： 杂志文章

    doi：10.1016/j.asoc.2021.107522

    authors： Sait U,K V GL,Shivakumar S,Kumar T,Bhaumik R,Prajapati S,Bhalla K,Chakrapani A

    更新日期：2021-09-01 00:00:00

  • An oppositional-Cauchy based GSK evolutionary algorithm with a novel deep ensemble reinforcement learning strategy for COVID-19 diagnosis.

    abstract：:A novel coronavirus (COVID-19) has globally attracted attention as a severe respiratory condition. The epidemic has been first tracked in Wuhan, China, and has progressively been expanded in the entire world. The growing expansion of COVID-19 around the globe has made X-ray images crucial for accelerated diagnostics. ...

    journal_title：Applied soft computing

    pub_type： 杂志文章

    doi：10.1016/j.asoc.2021.107675

    authors： Jalali SMJ,Ahmadian M,Ahmadian S,Khosravi A,Alazab M,Nahavandi S

    更新日期：2021-11-01 00:00:00