Literature DB >> 35534142

Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review.

Carmela Comito1, Clara Pizzuti2.   

Abstract

The outbreak of novel corona virus 2019 (COVID-19) has been treated as a public health crisis of global concern by the World Health Organization (WHO). COVID-19 pandemic hugely affected countries worldwide raising the need to exploit novel, alternative and emerging technologies to respond to the emergency created by the weak health-care systems. In this context, Artificial Intelligence (AI) techniques can give a valid support to public health authorities, complementing traditional approaches with advanced tools. This study provides a comprehensive review of methods, algorithms, applications, and emerging AI technologies that can be utilized for forecasting and diagnosing COVID-19. The main objectives of this review are summarized as follows. (i) Understanding the importance of AI approaches such as machine learning and deep learning for COVID-19 pandemic; (ii) discussing the efficiency and impact of these methods for COVID-19 forecasting and diagnosing; (iii) providing an extensive background description of AI techniques to help non-expert to better catch the underlying concepts; (iv) for each work surveyed, give a detailed analysis of the rationale behind the approach, highlighting the method used, the type and size of data analyzed, the validation method, the target application and the results achieved; (v) focusing on some future challenges in COVID-19 forecasting and diagnosing.
Copyright © 2022 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Artificial intelligence; COVID-19; Deep learning; Diagnosing; Forecasting; Machine learning

Mesh:

Year:  2022        PMID: 35534142      PMCID: PMC8958821          DOI: 10.1016/j.artmed.2022.102286

Source DB:  PubMed          Journal:  Artif Intell Med        ISSN: 0933-3657            Impact factor:   7.011


  74 in total

1.  Prediction of the number of COVID-19 confirmed cases based on K-means-LSTM.

Authors:  Shashank Reddy Vadyala; Sai Nethra Betgeri; Eric A Sherer; Amod Amritphale
Journal:  Array (N Y)       Date:  2021-08-21

2.  A Novel Intelligent Computational Approach to Model Epidemiological Trends and Assess the Impact of Non-Pharmacological Interventions for COVID-19.

Authors:  Jinchang Ren; Yijun Yan; Huimin Zhao; Ping Ma; Jaime Zabalza; Zain Hussain; Shaoming Luo; Qingyun Dai; Sophia Zhao; Aziz Sheikh; Amir Hussain; Huakang Li
Journal:  IEEE J Biomed Health Inform       Date:  2020-12-04       Impact factor: 5.772

3.  Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea.

Authors:  Mohammed A A Al-Qaness; Ahmed A Ewees; Hong Fan; Laith Abualigah; Mohamed Abd Elaziz
Journal:  Int J Environ Res Public Health       Date:  2020-05-18       Impact factor: 3.390

4.  Prediction for the spread of COVID-19 in India and effectiveness of preventive measures.

Authors:  Anuradha Tomar; Neeraj Gupta
Journal:  Sci Total Environ       Date:  2020-04-20       Impact factor: 7.963

5.  Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks.

Authors:  Amal I Saba; Ammar H Elsheikh
Journal:  Process Saf Environ Prot       Date:  2020-05-20       Impact factor: 6.158

6.  Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches.

Authors:  İsmail Kırbaş; Adnan Sözen; Azim Doğuş Tuncer; Fikret Şinasi Kazancıoğlu
Journal:  Chaos Solitons Fractals       Date:  2020-06-13       Impact factor: 5.944

7.  Forecasting Covid-19 Dynamics in Brazil: A Data Driven Approach.

Authors:  Igor Gadelha Pereira; Joris Michel Guerin; Andouglas Gonçalves Silva Júnior; Gabriel Santos Garcia; Prisco Piscitelli; Alessandro Miani; Cosimo Distante; Luiz Marcos Garcia Gonçalves
Journal:  Int J Environ Res Public Health       Date:  2020-07-15       Impact factor: 3.390

8.  Utilization of machine-learning models to accurately predict the risk for critical COVID-19.

Authors:  Dan Assaf; Ya'ara Gutman; Yair Neuman; Gad Segal; Sharon Amit; Shiraz Gefen-Halevi; Noya Shilo; Avi Epstein; Ronit Mor-Cohen; Asaf Biber; Galia Rahav; Itzchak Levy; Amit Tirosh
Journal:  Intern Emerg Med       Date:  2020-08-18       Impact factor: 3.397

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  2 in total

1.  Hybrid learning method based on feature clustering and scoring for enhanced COVID-19 breath analysis by an electronic nose.

Authors:  Shidiq Nur Hidayat; Trisna Julian; Agus Budi Dharmawan; Mayumi Puspita; Lily Chandra; Abdul Rohman; Madarina Julia; Aditya Rianjanu; Dian Kesumapramudya Nurputra; Kuwat Triyana; Hutomo Suryo Wasisto
Journal:  Artif Intell Med       Date:  2022-05-17       Impact factor: 7.011

2.  Epidemic forecasting based on mobility patterns: an approach and experimental evaluation on COVID-19 Data.

Authors:  Maria Pia Canino; Eugenio Cesario; Andrea Vinci; Shabnam Zarin
Journal:  Soc Netw Anal Min       Date:  2022-08-18
  2 in total

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