Harnessing the Power of AI to Improve Detection, Monitoring, and Public Health Interventions for Japanese Encephalitis

• Published in: Biomedicines Vol. 13; no. 1; p. 42
• Main Authors: Xiao, Junhua, Kendal, Evie, Kwa, Faith A. A.
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2025; MDPI
• Subjects: Accuracy; Algorithms; Artificial intelligence; Blood; Climate change; Control; Diagnosis; Disease prevention; Encephalitis; Enzymes; Epidemics; Epidemiology; Genomes; Growth patterns; Health aspects; Immunization; Infection control; Infections; Japanese Encephalitis; Japanese Encephalitis Virus; JEV; Mathematical models; Methods; Morbidity; Population growth; Public health; Review; Sentinel health events; Technology application; Tropical diseases; Vaccination; Vector-borne diseases; Viruses; Australia; India; AI; Japanese Encephalitis Virus; JEV; Japanese Encephalitis; JE; artificial intelligence
• ISSN: 2227-9059; 2227-9059
• DOI: 10.3390/biomedicines13010042

Japanese Encephalitis (JE) is the leading cause of viral encephalitis in regions with endemic Japanese Encephalitis Virus (JEV) infections. Background/Objectives: The aim of this review is to consider the potential role of artificial intelligence (AI) to improve detection, monitoring and public health interventions for JE. Discussion: As climate change continues to impact mosquito population growth patterns, more regions will be affected by mosquito-borne diseases, including JE. Improving diagnosis and surveillance, while continuing preventive measures, such as widespread vaccination campaigns in endemic regions, will be essential to reduce morbidity and mortality associated with JEV. Conclusions: With careful integration, AI mathematical and mechanistic models could be useful tools for combating the growing threat of JEV infections globally.