Emergencies of zoonotic diseases, drivers, and the role of artificial intelligence in tracking the epidemic and pandemics

Abstract

Zoonotic illnesses are defined as diseases that can be transmitted from animals to humans through various channels. More than sixty percent of disease-causing organisms capable of affecting humans are classified as zoonoses. This category encompasses many parasites, including bacteria, algae, fungi, protozoa, and others. The emergence of zoonotic diseases is influenced by a variety of various elements that come into play. These include changes in the climate, the clearing out of forests, the illicit trade of goods, the use of agricultural methods that are not sustainable, the eradication of ecosystems, urbanization, and neutralization. Artificial intelligence is now a trendy subject of debate. This popularity may be attributed to artificial intelligence's accuracy and its capacity to predict zoonotic illnesses. In the course of the inquiry into zoonotic illnesses, the use of artificial intelligence has shown to be of considerable assistance. The goal of this study is to shed light on the elements that contribute to the appearance and reappearance of zoonotic diseases, as well as the role that artificial intelligence particularly some of the most significant machine learning techniques, such as support vector machine (SVM), logistic regression (LR), Bayesian network, Artificial Neural Networks, Fuzzy Clustering, Poisson Point Process and Deep Denoising Autoencoder to the fight against the various infectious diseases. In addition, this review will discuss the factors that contribute to the appearance and reappearance of zoonotic illnesses.