Innovative software systems for managing the impact of the COVID-19 pandemic

Affiliations

¹ School of Electronic Engineering and Computer Science Queen Mary University of London London UK.
² FLOW, KTH Royal Institute of Technology Stockholm Sweden.
³ School of Engineering, Information Technology and Physical Sciences Federation University Ballarat Australia.
⁴ Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India.

Editorial

Sukhpal Singh Gill et al. Softw Pract Exp. 2022 Apr.

. 2022 Apr;52(4):821-823.

doi: 10.1002/spe.3023. Epub 2021 Sep 5.

¹ School of Electronic Engineering and Computer Science Queen Mary University of London London UK.
² FLOW, KTH Royal Institute of Technology Stockholm Sweden.
³ School of Engineering, Information Technology and Physical Sciences Federation University Ballarat Australia.
⁴ Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India.

No abstract available

References

1. Mohan S, John A, Abugabah A, et al. An approach to forecast impact of Covid‐19 using supervised machine learning model. Softw Pract Exp. 2022;52(4):824–840. 10.1002/spe.2969 - DOI - PMC - PubMed
1. Abid A, Cheikhrouhou S, Kallel S, Jmaiel M. NovidChain: blockchain‐based privacy‐preserving platform for COVID‐19 test/vaccine certificates. Softw Pract Exp. 2022;52(4):841–867. 10.1002/spe.2983 - DOI - PMC - PubMed
1. Singh A, Kaur A, Dhillon A, Ahuja S, Vohra H. Software system to predict the infection in COVID‐19 patients using deep learning and web of things. Softw Pract Exp. 2022;52(4):868–886. 10.1002/spe.3011 - DOI - PMC - PubMed
1. Maiti M, Vuković D, Mukherjee A, Paikarao PD, Yadav JK. Advanced data integration in banking, financial, and insurance software in the age of COVID‐19. Softw Pract Exp. 2022;52(4):887–903. 10.1002/spe.3018 - DOI - PMC - PubMed