Application of artificial intelligence in study of epidemiological patterns of vector-borne diseases

Abstract

With the intensification of climate change, emerging and re-emerging vector-borne diseases have become increasingly active worldwide, posing a continuous threat to human health. These diseases, mainly transmitted by arthropod vectors such as mosquitoes, ticks, and mites, have epidemiological processes that are generally influenced by natural environmental conditions, human factors, and biological factors, exhibiting pronounced regional distribution characteristics. In recent years, artificial intelligence (AI) technologies, driven by big data and algorithms, have developed rapidly, providing new opportunities for studying epidemiological patterns of vector-borne diseases. Based on a review of the spatiotemporal epidemiological characteristics of various common vector-borne diseases, this study reviews the development of AI-enabled research from three dimensions: data acquisition, analysis of influencing factors, and epidemic risk early warning, while also summarizing the challenges faced in this field. Finally, this study offers prospects for the application of AI in the study of epidemiological patterns of vector-borne diseases.