Abstract
In this paper is demonstrated that the morphology of infection’s curve is a consequence of the entropic behavior of macro-systems that are entirely dependent on the nonlinearity of social dynamics. Thus in the ongoing pandemic the so-called curve of cases would acquire an exponential morphology as consequence of the human mobility and the intensity of randomness that it exhibits still under social distancing and other types of social protection adopted in most countries along the first wave of spreading of Covid-19.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Here one can mention the aerial transportation as an important vector to increase the infection in a intercontinental way.
References
Wu, F., et al.: A new coronavirus associated with human respiratory disease in China. Nature 579, 265–269 (2020)
Leung, K., Wu, J.T., Liu, D., Leung, G.M.: First-wave COVID-19 transmissibility and severity in China outside Hubei after control measures, and second-wave scenario planning: a modelling impact assessment. Lancet 395(1023325), 1382–1393 (2020)
Gonzalez-Parra, G., Arenas, A.J., Aranda, D.F., Segovia, L.: Modeling the epidemic waves of AH1N1/09 influenza around the world. Spat. Spatio-Temporal Epidemiol. 2(4), 219–226 (2011)
Ortiz-Prado, E., et al.: Clinical, molecular, and epidemiological characterization of the SARS-CoV-2 virus and the Coronavirus Disease 2019 (COVID-19), a comprehensive literature review. Diagn. Microbiol. Infect. Dis. 98(1), 115094 ( (2020)
Chia, W.N., et al.: Serological differentiation between COVID-19 and SARS infections. Emerg. Microbes Infect. 9(1), 1497–1505 (2020)
Jaynes, E.T.: Information theory and statistical mechanics. Phys. Rev. 106, 620 (1957). Published 15 May 1957
Bekenstein, J.D.: Entropy content and information flow in systems with limited energy. Phys. Rev. D 30, 1669 (1984). Published 1 October 1984
Tikochinsky, Y., Tishby, N.Z., Levine, R.D.: Alternative approach to maximum-entropy inference. Phys. Rev. A 30, 2638 (1984). Published 1 November 1984
Lindgren, K.: Microscopic and macroscopic entropy. Phys. Rev. A 38, 4794 (1988). Published 1 November 1988
Crutchfield, J.P., Young, K.: Inferring statistical complexity. Phys. Rev. Lett. 63, 105 (1989). Published 10 July 1989
Nieto-Chaupis, H.: Macrophage-inspired nanorobots to fast recognition of bacteria and virus through electric forces and fields patterns inside of an internet of bio-nano things network. J. Phys. Conf. Ser. 1310 (2018). Applied Nanotechnology and Nanoscience International Conference (ANNIC: 22–24 October 2018. Langenbeck Virchow Haus, Berlin, Germany
Nieto-Chaupis, H.: The Feynman path integral to characterize and anticipate bacteria chemotaxis in a host healthy body. J. Phys. Conf. Ser. 1310 (2018). Applied Nanotechnology and Nanoscience International Conference (ANNIC: 22–24 October 2018. Langenbeck Virchow Haus, Berlin, Germany
Cirillo, P., Taleb, N.N.: Tail risk of contagious diseases. Nat. Phys. 16, 606–613 (2020)
Morse, S.S.: The origins of new viral diseases. J. Environ. Sci. Health, Part C 9, 2 (1991)
Tian, H., Xu, B.: Persistence and transmission of avian influenza A (H5N1): virus movement, risk factors and pandemic potential. Ann. GIS 21(1), 55–68 (2015)
Nieto-Chaupis, H.: Feynman-theory-based algorithm for an efficient detaining of worldwide outbreak of AH1N1 virus. In: 2019 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON)
Mei, X., et al.: Artificial intelligence-enabled rapid diagnosis of patients with COVID-19. Nat. Med. 26, 1224–1228 (2020). 19 May 2020
Tuli, S., Tuli, S., Tuli, R., Gill, S.S.: Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing. IoT 11, 100222 (2020)
Yadav, M., Perumal, M., Srinivas, M.: Analysis on novel coronavirus (COVID-19) using machine learning methods. Chaos, Solitons Fractals 139, 110050 (2020)
Bachtiger, P., Peters, N.S., Walsh, S.L.F.: Machine learning for COVID-19–asking the right questions, The Lancet Digital Health. In press, corrected proof Available online 10 July 2020
Habersaat, K.B.: Ten considerations for effectively managing the COVID-19 transition. Nat. Hum. Behav. 4, 677–687 (2020)
https://en.wikipedia.org wiki COVID-19 pandemic by country and territory
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Nieto-Chaupis, H. (2021). Accelerated Virus Spread Driven by Randomness in Human Behavior. In: Nakano, T. (eds) Bio-Inspired Information and Communications Technologies. BICT 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 403. Springer, Cham. https://doi.org/10.1007/978-3-030-92163-7_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-92163-7_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-92162-0
Online ISBN: 978-3-030-92163-7
eBook Packages: Computer ScienceComputer Science (R0)