AI for Public Health Surveillance: Using AI to Track and Predict Public Health Trends, Such as Infectious Disease Spread.

EasyChair Preprint 15122

Abstract

Artificial intelligence (AI) is revolutionizing public health surveillance by enhancing the ability to track and predict trends in infectious disease spread. This abstract discusses the transformative role of AI technologies in analyzing vast amounts of health data from diverse sources, including social media, electronic health records, and environmental data. By employing machine learning algorithms and predictive analytics, AI can identify patterns and correlations that may indicate emerging outbreaks, enabling timely interventions. The application of AI in public health surveillance not only improves the speed and accuracy of disease detection but also facilitates proactive decision-making by public health authorities. Additionally, AI tools can support resource allocation, optimize response strategies, and enhance community awareness. However, challenges such as data privacy, ethical considerations, and the need for robust validation of AI models must be addressed to ensure their effective and responsible use. This discussion highlights the potential of AI to strengthen public health systems and improve health outcomes through more effective surveillance and response strategies.

Keyphrases: AI in Public Health, Disease Surveillance Systems, Health Data Mining, Infectious Disease Tracking, Machine Learning in Health, Outbreak Prediction, Predictive Analytics, Real Time Data Analysis, Sentinel surveillance, epidemiological modeling

@booklet{EasyChair:15122,
  author    = {Adeyeye Barnabas},
  title     = {AI for Public Health Surveillance: Using AI to Track and Predict Public Health Trends, Such as Infectious Disease Spread.},
  howpublished = {EasyChair Preprint 15122},
  year      = {EasyChair, 2024}}