Systematic Review on Citizen Science and Artificial Intelligence for Vector-Borne Diseases

Abstract

Vector-borne diseases (VBDs) pose a significant threat to public health globally. VBDs are a significant public health concern worldwide, with infections such as Malaria, Dengue Fever, Zika Virus, and Lyme Disease posing a threat to global health security. There is a need for innovative and effective strategies to control these diseases. One potential solution lies in the integration of citizen science and artificial intelligence technologies. Citizen science, which involves the participation of volunteers in scientific research, can greatly contribute to data collection and monitoring efforts for vector-borne diseases. Artificial intelligence can enhance the analysis of this data, leading to improved disease surveillance, prediction, and control strategies. Citizen Science involves active public participation in scientific research, data collection, and analysis, while AI and Machine Learning (ML) techniques offer powerful tools for processing and interpreting large datasets. By leveraging the power of citizen science and artificial intelligence, we can harness the collective efforts of volunteers and advanced technology to better understand, track, and mitigate the spread of vector-borne diseases. Through the combination of citizen science and artificial intelligence, a more comprehensive and efficient approach can be taken to gather data on vector-borne diseases, analyze the data, and inform public health interventions. This systematic review aims to explore the role of citizen science and artificial intelligence in addressing the challenges associated with vector-borne diseases. It will examine the existing literature on the use of citizen science and artificial intelligence in vector-borne disease research, including their applications, benefits, and limitations, in order to provide insights and recommendations for future research and public health strategies.