In vivo channel characterization for dengue virus infection

Pal, Saswati and Islam, Nabiul and Misra, Sudip and Balasubramaniam, Sasitharan (2019) In vivo channel characterization for dengue virus infection. In: Proceedings of the 6th ACM International Conference on Nanoscale Computing and Communication, NANOCOM 2019 :. Proceedings of the 6th ACM International Conference on Nanoscale Computing and Communication, NANOCOM 2019 . Association for Computing Machinery, Inc, IRL. ISBN 9781450368971

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Abstract

Dengue, a mosquito-borne viral disease, poses a global threat owing to the unavailability of any specific therapeutics. Since prevention is only restricted to vector control, a clear understanding of Dengue Virus (DENV) transmission within an infected host is essential. The dynamics of DENV transmission addressed in light of molecular communication paradigm is promising in providing crucial information accounting for disease control that can lead to development of novel approaches to clear the virus infection. In this work, we model the DENV transmission inside the body from the point of a mosquito bite to the targeted organs as a communication system. Based on the physiological processes involved in the transmission of DENV through the layers of skin and vascular systems, we identify and propose a channel model. By considering the dynamics of virus transmission through the channel, we analyze and calculate different channel phenomena, such as path loss and channel noise, and obtain an analytical expression for the capacity of the proposed channel model. The uncertainty in signal transmission is modeled and evaluated owing to the innate and adaptive immune response in the channel. We performed in-silico experiments for validation and provided numerical analysis for the channel characteristics. Our analysis revealed that the attenuation offered in the cutaneous channel does not result in significant signal loss. We also observed that the variations in the channel capacity is not substantially affected by the injection probabilities of the virus.

Item Type:	Book Section
Additional Information:	Funding Information: This work was supported in part by the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant 713567, in part by Science Foundation Ireland via the CONNECT Research Centre under Grant 13/RC/2077. Funding Information: This work was supported in part by the European Union's Horizon 2020 research and innovation programme under the Marie Sk?odowska-Curie Grant 713567, in part by Science Foundation Ireland via the CONNECT Research Centre under Grant 13/RC/2077. Publisher Copyright: © 2019 Association for Computing Machinery.
Uncontrolled Keywords:	/dk/atira/pure/subjectarea/asjc/1700/1703
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Depositing User:	Admin SSL
Date Deposited:	19 Oct 2022 23:14
Last Modified:	01 Aug 2023 04:50
URI:	http://repository-testing.wit.ie/id/eprint/4762

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