Eco-epidemiology of prey and competitive predator species in the SEI model
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2024.15.spl-2.37Keywords:
Eco-epidemiological, Susceptible exposed infected model, Predator-prey relationship, Disease transmission, Population dynamics.Dimensions Badge
Issue
Section
License
Copyright (c) 2025 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The ecological epidemiology structure explores the relationship between disease and competitiveness in a predator-prey [22] (Vijaya S, J. J, 2017) structure. We create a mathematical model that includes a susceptible (S), exposed (E), and infected (I)[28][22] (Vijaya S, J.J, 2017)( S.P.Bera, A. M, 2015) subpopulation of prey, as well as a competing predator. The model Examines how disease transmission, predation rates, and natural population dynamics affect structure stability. The findings provide insights into illness prevalence and population levels, which could help researchers better understand disease outbreaks and the function of predators in disease control. Further studies should examine spatial aspects, environmental consequences, and predator behaviors.Abstract
How to Cite
Downloads
Similar Articles
- M. Iniyan, A. Banumathi, Brower blowfish nash secured stochastic neural network based disease diagnosis for medical WBAN in cloud environment , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- S. Vanaja, Hari Ganesh S, Application of data mining and machine learning approaches in the prediction of heart disease – A literature survey , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Shri Prakash, Sunil Kumar, Population Dynamics of Sarus Crane (Grus antigone antigone, Linn.) in and around Alwara Lake of district - Kaushambi (U.P.), India , The Scientific Temper: Vol. 7 No. 1&2 (2016): THE SCIENTIFIC TEMPER
- Temesgen A. Asfaw, Deep learning hyperparameter’s impact on potato disease detection , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- C. Muruganandam, V. Maniraj, A Self-driven dual reinforcement model with meta heuristic framework to conquer the iot based clustering to enhance agriculture production , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- S. Sindhu, L. Arockiam, A lightweight selective stacking framework for IoT crop recommendation , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Finney D. Shadrach, Harsshini S, Darshini R, Grapevine leaf species and disease detection using DNN , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Engida Admassu, Classifying enset based on their disease tolerance using deep learning , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Lakshminarayani A, A Shaik Abdul Khadir, A blockchain-integrated smart healthcare framework utilizing dynamic hunting leadership algorithm with deep learning-based disease detection and classification model , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Olivia C. Gold, Jayasimman Lawrence, Enhanced LSTM for heart disease prediction in IoT-enabled smart healthcare systems , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.
Most read articles by the same author(s)
- V. Manikandabalaji, R. Sivakumar, V. Maniraj, A framework for diabetes diagnosis based on type-2 fuzzy semantic ontology approach , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- V. Manikandabalaji, R. Sivakumar, V. Maniraj, A novel approach using type-II fuzzy differential evolution is proposed for identifying and diagnosis of diabetes using semantic ontology , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper