The Design of Epi-RPNN for the Analysis of Bacteriophage Infection Model

Abstract

In this study, we examine the analysis with accuracy based on intelligent computing for the bacteriophage infection model frequently employed in epidemiology. The microbiological is an interesting phenomenon known as bacteriophage infection or phage infection. Bacteriophages are viruses that target and infect bacteria specifically, then use the bacteria as hosts for their own replication. By injecting their genetic material into the bacterial cell, these phages cause the host cell’s machinery to be redirected in order to produce more phages, which ultimately causes the lysis or obliteration of the bacterial host. The derivation of the basic reproduction number and numerical simulations are conducted through a new machine learning approach called the random projection neural network (RPNN) method. The accuracy and robustness of our methodology are examined through a comparison of the results with numerical solvers ode23t and ode15s available in MATLAB. Moreover, the data testing, training and validation of mean square error are examined through performance, training test, error histogram, regression and fitness plots.

Conflict of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Funding

The research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Data Fabrication/Falsification Statement

The author(s) declare that no data has been fabricated, falsified, or manipulated in this study.

Participant Consent

The authors confirm that Informed consent was obtained from all participants, and confidentiality was duly maintained.

Copyright and Licensing

For all articles published in the NIJEC journal, Copyright (c) of this study is with author(s).