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Tytuł artykułu

Analyzing and improving tools for supporting fighting against COVID-19 based on prediction models and contact tracing

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In the paper, we are analyzing and proposing an improvement to current tools and solutions for supporting fighting with COVID-19. We analyzed the most popular anti-covid tools and COVID prediction models. We addressed issues of secure data collection, prediction accuracy based on COVID models. What is most important, we proposed a solution for improving the prediction and contract tracing element in these applications. The proof of concept solution to support the fight against a global pandemic is presented, and the future possibilities for its development are discussed.
Rocznik
Strony
art. no. e137414
Opis fizyczny
Bibliogr. 34 poz., rys., tab.
Twórcy
  • Wroclaw University of Science and Technology, ul. Wybrzeże Stanisława Wyspiańskiego 27, 50-370 Wroclaw, Poland
  • Wroclaw University of Science and Technology, ul. Wybrzeże Stanisława Wyspiańskiego 27, 50-370 Wroclaw, Poland
Bibliografia
  • [1] V. Chamola, V. Hassija, V. Gupta, and M. Guizani, “A comprehensive review of the COVID-19 pandemic and the role of IoT, drones, AI, blockchain, and 5G in managing its impact,” IEEE Access, vol. 8, pp. 90225–90265, 2020.
  • [2] J. Stanley and J.S. Granick, “Aclu white paper: The limits of location tracking in an epidemic,” Am. Civ. Lib. Union, April 2020.
  • [3] G. Dartmann, H. Song, and A. Schmeink, Big data analytics for cyber-physical systems: machine learning for the internet of things. Elsevier, 2019.
  • [4] M. Zastrow, “South korea is reporting intimate details of COVID-19 cases: has it helped?” Nature, March 2020.
  • [5] Ł. Apiecionek, J. Czerniak, M. Romantowski, D. Ewald, B. Tsizh, H. Zarzycki, and W. Dobrosielski, “Authentication over internet protocol,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 68, no. 2, 2020.
  • [6] E. Talhi, J.-C. Huet, V. Fortineau, and S. Lamouri, “A methodology for cloud manufacturing architecture in the context of Industry 4.0,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 68, no. 2, pp. 271‒284, 2020.
  • [7] L. Madeyski, T. Lewowski, and B. Kitchenham, “OECD Recommendation’s draft concerning access to research data from public funding: A review,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 69, no. 1, p. e135401, 2021.
  • [8] A.I. Abubakar, K.G. Omeke, M. Öztürk, S. Hussain, and M.A. Imran, “The role of artificial intelligence driven 5G networks in COVID-19 outbreak: opportunities, challenges, and future outlook,” Front. Commun. Networks, vol. 1, p. 575065, 2020.
  • [9] D. Ingram and J. Ward, “Behind the global efforts to make a privacy-first coronavirus tracking app,” NBC News, April 2020.
  • [10] “Google API for exposure notifications,” [Online]. Available: https://developers.google.com/android/exposure-notifications. Accessed: 2020-11-06.
  • [11] “COVID-19 screening tool website, apple website,” [Online]. Available: https://covid19.apple.com/screening/. Accessed: 2020-11-07.
  • [12] “Center for disease control and prevention COVID-19 health bot website,” [Online]. Available: https://www.cdc.gov/code.html. Accessed: 2020-11-07.
  • [13] “Project decode – data driven covid detection,” [Online]. Available: https://antycovid.ipipan.waw.pl/slides/p1130-3-Gruca.pdf. Accessed: 2020-11-07.
  • [14] C. Menni, A. Valdes, and M. Freidin, “Real-time tracking of self-reported symptoms to predict potential COVID-19,” Nat. Med., April 2020.
  • [15] D. Hauser, A.O. Obeng, K. Fei, M.A. Ramos, and C.R. Horowitz, “Views of primary care providers on testing patients for genetic risks for common chronic diseases,” Health Aff., vol. 37, no. 5, pp. 793–800, 2018.
  • [16] T. Alashoor, S. Han, and R.C. Joseph, “Familiarity with big data, privacy concerns, and self-disclosure accuracy in social networking websites: an apco model,” Commun. Assoc. Inf. Syst., vol. 41, no. 1, p. 4, 2017.
  • [17] M. Macenaite, “From universal towards child-specific protection of the right to privacy online: Dilemmas in the EU General Data Protection Regulation,” New Media Soc., vol. 19, no. 5, pp. 765–779, 2017.
  • [18] N. Crepaz, T. Tang, G. Marks, M.J. Mugavero, L. Espinoza, and H.I. Hall, “Durable viral suppression and transmission risk potential among persons with diagnosed hiv infection: United states, 2012–2013,” Clin. Infect. Dis., vol. 63, no. 7, pp. 976– 983, 2016.
  • [19] A.A. Hussain, O. Bouachir, F. Al-Turjman, and M. Aloqaily, “AI techniques for COVID-19,” IEEE Access, vol. 8, pp. 128776‒128795, 2020.
  • [20] R. Vaishya, M. Javaid, I.H. Khan, and A. Haleem, “Artificial intelligence (AI) applications for COVID-19 pandemic,” Diabetes Metab. Syndr.: Clin. Res. Rev., 2020.
  • [21] H. Dai and B. Zhao, “Association of the infection probability of COVID-19 with ventilation rates in confined spaces,” in Build. Simul., vol. 13. Springer, 2020, pp. 1321–1327.
  • [22] J. Xie, Z. Tong, X. Guan, B. Du, H. Qiu, and A. S. Slutsky, “Critical care crisis and some recommendations during the COVID-19 epidemic in china,” Intensive Care Med., pp. 1‒4, 2020.
  • [23] C. Prunean, “System and method for amplitude pre-distortion optimization for GPS signal constant envelope transmission,” Oct. 1 2019, US Patent 10,432,447.
  • [24] P. Zhou, Y. Zheng, Z. Li, M. Li, and G. Shen, “IODetector: a generic service for indoor outdoor detection,” in Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems, ser. SenSys ’12. New York, NY, USA: Association for Computing Machinery, 2012, pp. 361–362. [Online]. Available: https://doi.org/10.1145/2426656.2426709.
  • [25] J.A. Quiña-Mera, E.R. Saransig-Perugachi, D.J. Trejo- España, M.E. Naranjo-Toro, and C.P. Guevara-Vega, “Automation of the barter exchange management in ecuador applying Google V3 API for geolocation,” in International Conference on Information Technology & Systems. Springer, 2019, pp. 210‒219.
  • [26] N. Ahmed, R.A. Michelin, W. Xue, S. Ruj, R. Malaney, S.S. Kanhere, A. Seneviratne, W. Hu, H. Janicke, and S.K. Jha, “A survey of COVID-19 contact tracing apps,” IEEE Access, vol. 8, pp. 134577–134601, 2020.
  • [27] S. Jeschke, C. Brecher, H. Song, and D. Rawat, “Industrial internet of things: Foundations, principles and applications,” Cham, Switzerland: Springer, pp. 1–715, 2017.
  • [28] “Emf explained 2.0,” [Online]. Available: http://www.emfexplained.info/pol/?id=25916. Accessed: 2020-12-02.
  • [29] Y. Sun, H. Song, A. J. Jara, and R. Bie, “Internet of things and big data analytics for smart and connected communities,” IEEE Access, vol. 4, pp. 766–773, 2016.
  • [30] J. Zhang, E. Björnson, M. Matthaiou, D.W.K. Ng, H. Yang, and D.J. Love, “Prospective multiple antenna technologies for beyond 5G,” IEEE J. Sel. Areas Commun., vol. 38, no. 8, pp. 1637–1660, 2020.
  • [31] Y. Siriwardhana, C. De Alwis, G. Gür, M. Ylianttila, and M. Liyanage, “The fight against the COVID-19 pandemic with 5G technologies,” IEEE Eng. Manage. Rev., vol. 48, no. 3, pp. 72–84, 2020.
  • [32] K. Shafique, B.A. Khawaja, F. Sabir, S. Qazi, and M. Mustaqim, “Internet of Things (IoT) for next-generation smart systems: A review of current challenges, future trends and prospects for emerging 5G-IoT scenarios,” IEEE Access, vol. 8, pp. 23022–23040, 2020.
  • [33] M. Otoom, N. Otoum, M. A. Alzubaidi, Y. Etoom, and R. Banihani, “An iot-based framework for early identification and monitoring of COVID-19 cases,” Biomed. Signal Process. Control, vol. 62, p. 102149, 2020.
  • [34] S. Jaafari, A. Alhasani, S.M. Almutairi et al., “Certain investigations on iot system for COVID-19,” in 2020 International Conference on Computing and Information Technology (ICCIT-1441). IEEE, 2020, pp. 1–4.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1381416a-b1e3-4f08-9b63-9c212af59324
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