COVID-19 cases prediction with negative group delays digital function

Blaise Ravelo; Mathieu Guerin; Habachi Bilal; Sylcolin Rakotonandrasana; Wenceslas Rahajandraibe

doi:10.18203/issn.2454-2156.IntJSciRep20233166

Authors

Blaise Ravelo Nanjing University of Science and Technology, Nanjing, Jiangsu, China
Mathieu Guerin Aix-Marseille University, Marseille, France
Habachi Bilal Aix-Marseille University, Marseille, France
Sylcolin Rakotonandrasana Islamic University of Madinah, Engineering college, Madinah, Saudi Arabia
Wenceslas Rahajandraibe Aix-Marseille University, Marseille, France

DOI:

https://doi.org/10.18203/issn.2454-2156.IntJSciRep20233166

Keywords:

Prediction, COVID-19, Integrated-circuit, Time-advance

Abstract

The negative group delay (NGD) is an uncommon function enabling to propagate arbitrary waveform signals with time-advance behavior. The counterintuitive NGD function was initially experimented for anticipating typically fast and short duration electronic signals in micro- and milli-second time scale. The application of NGD function to large time scale signal attracts more and more the attention of data processing engineer. This paper aims to investigate on the ability of NGD function to predict time- dependent social data with someday time-advances. As practical case of study, an innovative application of NGD function for predicting disease cases is treated. The digital circuit theory enabling to understand the low-pass (LP) NGD canonical TF and the characterization approach is established. It is shown in which condition the first order difference equation represents a LP-NGD circuit. Then, the design method of typical LP-NGD predictor as numerical circuit is introduced in function of the expected time-advance. The NGD predictor time-variation property is theoretically initiated. The NGD time-advance varied from -7 days to -1/2 days is investigated with deterministic data prediction processing from 5-months bi- exponential waveform data. The predicted data with time-advance of about -4 days was confirmed by analytical computation and simulation. The LP-NGD digital predictor feasibility is validated with monthly COVID-19 randomly arbitrary data by computed and virtually tested results. It was investigated with sensitivity analysis that the prediction performance is better when the input signal is smoothed enough. As expected, prediction result showing very good correlation with input data is demonstrated.

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References

Chauhan S. Comprehensive review of coronavirus disease 2019 (COVID-19). Biomed J. 2020;43(4):334-40.

What four coronaviruses from history can tell us about COVID-19. Available at: https://www. newscientist.com/article/mg24632800-700-what-four-coronaviruses-from-history-can-tell-us-about-covid-19. Accessed on 27, April, 2023.

Gomez-Barrero M, Drozdowski P, Rathgeb C, Patino J, Todisco M, Nautsch A. Biometrics in the Era of COVID-19: Challenges and Opportunities. IEEE Tran. Technol Society. 2022;3(4):307-22.

Charumilind S, Craven M, Lamb J, Sabow A, Wilson M. When will the COVID-19 pandemic end? an update. McKinsey and Company, Healthcare Systems and Services Practice, Nov. 2021. Available at: https://www.mckinsey.com/~/media/mckinsey /industries/healthcare%20systems%20and%20services/our%20insights/when%20will%20the%20covid%2019%20pandemic%20end/nov%202020/when-will-the- covid-19-pandemic-end-an-update-vf.pdf. Accessed on 27 April, 2023.

Michael K, Abbas R. What Happens to COVID-19 Data After the Pandemic? Socio-Technical Lessons. IEEE Tran. Technol Society. 2022;3(4):242-7.

Re-open EU. 2022. Available at: https://reopen.europa.eu/. Accessed on 27, April, 2023.

Hohma E, Burnell R, Corrigan CC, Luetge C. Individuality and Fairness in Public Health Surveillance Technology: A Survey of User Perceptions in Contact Tracing Apps. IEEE Tran. Technol Society. 2022;3(4):300-6.

Naudé W. Artificial Intelligence Against COVID-19: An Early Review, Rochester, NY, USA. 2020.

Fareed W, Abdul Salam A, Akram U, Alam M. Deep Framework for Predicting COVID-19 and Related Lung diseases using CXR Images. Proc. 2022 2nd Int. Conf. Digital Futures and Transformative Technologies (ICoDT2), Rawalpindi, Pakistan. 2022;1-7.

Kc K, Yin Z, Wu M, Wu Z. Evaluation of deep learning-based approaches for COVID-19 classification based on chest X-ray images. Signal Image Video Processing. 2021;15(5):959-66.

Cohen JP, Morrison P, Dao L, Roth K, Duong TQ, Ghassemi M. COVID-19 image data collection: Prospective predictions are the future. J Machine Learning Biomed Imaging. 2020;2:1-38.

Allahabadi H. Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients. IEEE Tran. Technol Society. 2022;3(4):272-89.

Naudé W. Artificial intelligence vs COVID-19: Limitations constraints and pitfalls. AI Soc. 2020;35:761-5.

A. Narin. Detection of COVID-19 Patients with Convolutional Neural Network Based Features on Multi-class X-ray Chest Images. Proc. 2020 Medical Technologies Congress (TIPTEKNO). 2020;1-4.

Wang L, Lin ZQ, Wong A. COVID-net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest x-ray images. Scientific Rep. 2020;10(1):1-12.

Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I et al. Deep convolutional neural networks for computer-aided detection: CNN architectures dataset characteristics and transfer learning. IEEE Tran. Med Imaging. 2016;35(5):1285-98.

Tan M, Le Q. Efficient net: Rethinking model scaling for convolutional neural networks. Int Conf Machine Learning. 2019;6105-14.

Gao L, Wei F, Yan Z, Ma J, Xia J. A study of objective prediction for summer precipitation patterns over eastern China based on a multinomial logistic regression model. Atmosphere. 2019;10(4):1-18.

Manandhar S, Dev S, Lee YH, Meng YS, Winkler S. A data-driven approach for accurate rainfall prediction. IEEE Tran. Geoscience and Remote Sensing. 2019;57(11):9323-31.

Elbagoury BM, Zaghow M, Salem ABM, Schrader T. Mobile AI Stroke Health App: A Novel Mobile Intelligent Edge Computing Engine based on Deep Learning models for Stroke Prediction Research and Industry Perspective. Proc. 2021 IEEE 20th Int. Conf. Cognitive Informatics and Cognitive Computing (ICCI*CC), Banff, AB, Canada. 2021;39-52.

LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-44 .

Ethics Guidelines for Trustworthy AI. 2020. Available at: https://op.europa.eu/en/publication-detail/-/publication/d3988569-0434-11ea-8c1f-01aa75ed71a1. Accessed on 25 April, 2023.

Robertson LJ, Munoz A, Michael K. Managing Technological Vulnerability of Urban Dwellers: Analysis, Trends, and Solutions. IEEE Tran. Technol Society. 2020;1(1):48-59.

Solli D, Chiao RY, Hickmann JM. Superluminal effects and negative group delays in electronics, and their applications. Phys Rev E. 2002;66(056601):1-12.

Hymel C, Stubbers RA, Brandt ME. Temporally Advanced Signal Detection: A Review of the Technology and Potential Applications. IEEE CAS Magazine. 2011;11(3):10-25.

Ravelo B, Lalléchère S, Thakur A, Saini A, Thakur P. Theory and circuit modelling of baseband and modulated signal delay compensations with low- and band-pass NGD effects. Int J Electron Commun. 2016;70(9):1122-7.

Voss HU. Signal prediction by anticipatory relaxation dynamics. Phys Rev E. 2016;93(3):(030201R):1-5.

Mitchell MW, Chiao RY. Causality and Negative Group-delays in a Simple Bandpass Amplifier. Am J Phys. 1998;66:14-9.

Mitchell MW, Chiao RY. Negative Group-delay and ‘Fronts’ in a Causal Systems: An Experiment with Very Low Frequency Bandpass Amplifiers. Phys Lett A. 1997;230:133-8.

Nakanishi T, Sugiyama K, Kitano M. Demonstration of Negative Group-delays in a Simple Electronic Circuit. Am J Phys. 2002;70(11):1117-21.

Kitano M, Nakanishi T, Sugiyama K. Negative Group-delay and Superluminal Propagation: An Electronic Circuit Approach. IEEE J. Sel. Top. in Quantum Electron. 2003;9(1):43-51.

Munday JN, Henderson RH. Superluminal Time Advance of a Complex Audio Signal. Appl Phys Lett. 2004;85:503-4.

Ravelo B. Similitude between the NGD function and filter gain behaviours. Int J Circ Theor Appl. 2014;42(10):1016-32.

Ravelo B. First-order low-pass negative group delay passive topology. Electronics Letters. 2016;52(2):124-6.

Ravelo B. Elementary NGD IIR/FIR Systems. IJSPS. 2014;2(2):132-8

Ravelo B, Guerin M, Rahajandraibe W, Gies V, Rajaoarisoa L, Lalléchère S. Low-Pass NGD Numerical Function and STM32 MCU Emulation Test, IEEE Tran. Industrial Electronics. 2022;39(8):8346-55.

Ravelo B, Guerin M, Frnda J, Rajaoarisoa L, Rahajandraibe W. Thermal Wave Variation Anticipation under Minute Scale Time-Advance with Low-Pass NGD Digital Circuit. IEEE Access. 2022;10(1):127654-66.

Ravelo B, Guerin M, Rahajandraibe W, Rajaoarisoa L. All-pass NGD FIR original study for sensor failure detection application. IEEE Tran. Industrial Electronics. 2023;70(9):9561-71.

Johns Hopkins Coronavirus Resource Center COVID-19 data. Available at: https://coronavirus.jhu.edu/map.html. Accessed on 25 April, 2023.