A deep learning algorithm for modeling and forecasting of COVID-19 in five worst affected states of India

被引：30

作者：

Farooq, Junaid ^{[1
]}

Bazaz, Mohammad Abid ^{[1
]}

机构：

[1] Natl Inst Technol Srinagar, Dept Elect Engn, Srinagar, Jammu & Kashmir, India

来源：

ALEXANDRIA ENGINEERING JOURNAL | 2021年 / 60卷 / 01期

关键词：

Covid-19; Incremental learning; ANN; Forecasting; TRANSMISSION DYNAMICS; INFLUENZA;

D O I：

10.1016/j.aej.2020.09.037

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In this paper, deep learning is employed to propose an Artificial Neural Network (ANN) based online incremental learning technique for developing an adaptive and non-intrusive analytical model of Covid-19 pandemic to analyze the temporal dynamics of the disease spread. The model is able to intelligently adapt to new ground realities in real-time eliminating the need to retrain the model from scratch every time a new data set is received from the continuously evolving training data. The model is validated with the historical data and a forecast of the disease spread for 30-days is given in the five worst affected states of India. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University.

引用

页码：587 / 596

页数：10

共 21 条

[1]

Alahari, 2018, LECT NOTES COMPUTER

[2]

Bouchachia A, 2007, IEEE INT CONF FUZZY, P1811

[3]

Brauer F, 2008, LECT NOTES MATH, V1945, P19

[4] Transmission of influenza: Implications for control in health care settings [J].

Bridges, CB ;

Kuehnert, MJ ;

Hall, CB .

CLINICAL INFECTIOUS DISEASES, 2003, 37 (08) :1094-1101

[5] A 'small-world-like' model for comparing interventions aimed at preventing and controlling influenza pandemics [J].

Carrat, Fabrice ;

Luong, Julie ;

Lao, Herve ;

Salle, Anne-Violaine ;

Lajaunie, Christian ;

Wackernagel, Hans .

BMC MEDICINE, 2006, 4 (1)

[6] A Bayesian MCMC approach to study transmission of influenza:: application to household longitudinal data [J].

Cauchemez, S ;

Carrat, F ;

Viboud, C ;

Valleron, AJ ;

Boëlle, PY .

STATISTICS IN MEDICINE, 2004, 23 (22) :3469-3487

[7] Modeling influenza epidemics and pandemics: insights into the future of swine flu (H1N1) [J].

Coburn, Brian J. ;

Wagner, Bradley G. ;

Blower, Sally .

BMC MEDICINE, 2009, 7

[8] Multisystem Inflammatory Syndrome in US Children and Adolescents [J].

Feldstein, Leora R. ;

Rose, Erica B. ;

Horwitz, Steven M. ;

Collins, Jennifer P. ;

Newhams, Margaret M. ;

Son, Mary Beth F. ;

Newburger, Jane W. ;

Kleinman, Lawrence C. ;

Heidemann, Sabrina M. ;

Martin, Amarilis A. ;

Singh, Aalok R. ;

Li, Simon ;

Tarquinio, Keiko M. ;

Jaggi, Preeti ;

Oster, Matthew E. ;

Zackai, Sheemon P. ;

Gillen, Jennifer ;

Ratner, Adam J. ;

Walsh, Rowan F. ;

Fitzgerald, Julie C. ;

Keenaghan, Michael A. ;

Alharash, Hussam ;

Doymaz, Sule ;

Clouser, Katharine N. ;

Giuliano, John S. ;

Gupta, Anjali ;

Parker, Robert M. ;

Maddux, Aline B. ;

Havalad, Vinod ;

Ramsingh, Stacy ;

Bukulmez, Hulya ;

Bradford, Tamara T. ;

Smith, Lincoln S. ;

Tenforde, Mark W. ;

Carroll, Christopher L. ;

Riggs, Becky J. ;

Gertz, Shira J. ;

Daube, Ariel ;

Lansell, Amanda ;

Coronado Munoz, Alvaro ;

Hobbs, Charlotte V. ;

Marohn, Kimberly L. ;

Halasa, Natasha B. ;

Patel, Manish M. ;

Randolph, Adrienne G. .

NEW ENGLAND JOURNAL OF MEDICINE, 2020, 383 (04) :334-346

[9]

Gepperth A., 2016, ESANN 2016 24 EUR S

[10] Contribution to the mathematical theory of epidemics [J].

Kermack, WO ;

McKendrick, AG .

PROCEEDINGS OF THE ROYAL SOCIETY OF LONDON SERIES A-CONTAINING PAPERS OF A MATHEMATICAL AND PHYSICAL CHARACTER, 1927, 115 (772) :700-721

← 1 2 3 →