Systematic Literature Review of Models Used in the Epidemiological Analysis of Bovine Infectious Diseases

Abstract

There are different bovine infectious diseases that show economic losses and social problems in various sectors of the economy. Most of the studies are focused on some diseases (for example, tuberculosis, salmonellosis, and brucellosis), but there are few studies on other diseases which are not officially controlled but also have an impact on the economy. This work is a systematic literature review on models (as a theoretical scheme, generally in mathematical form) used in the epidemiological analysis of bovine infectious diseases in the dairy farming sector. In this systematic literature review, criteria were defined for cattle, models, and infectious diseases to select articles on Scopus, IEEE, Xplorer, and ACM databases. The relations between the found models (model type, function and the proposed objective in each work) and the bovine infectious diseases, and the different techniques used and the works over infectious disease in humans, are presented. The outcomes obtained in this systematic literature review provide the state-of-the-art inputs for research on models for the epidemiological analysis of infectious bovine diseases. As a consequence of these outcomes, this work also presents an approach of EiBeLec, which is an adaptive and predictive system for the bovine ecosystem, combining a prediction model that uses machine-learning techniques and an adaptive model that adapts the information presented to end users.

A comparative study of ensemble machine learning algorithms for brucellosis disease prediction

“…At present machine learning technique is a very popular technique to find out the risk factors (Tito et al 2023) which are more responsible for spreading of disease. In clinical settings, machine-learning algorithms such as data mining techniques (Arifuzzaman et al 2021) are being used as a valuable tool for risk assessment and clinical decisionmaking (Ballesteros-Ricaurte et al 2022;Rushd et al 2021). The objective of this paper is to find out the best technique for predicting brucella disease.…”