Predictive modeling of infectious disease outbreaks: harnessing artificial intelligence for early detection and response

Abstract

Background: Artificial intelligence (AI) has become a transformative instrument in public health to monitor and forecast infectious disease outbreaks. AI-based predictive models enable early detection and quick response mechanisms which present potential superiority over traditional systems. The research investigation sought to evaluate public health professionals' perceptions alongside their usage patterns and the associated benefits and concerns regarding AI integration for disease prediction.

Methods: Researchers implemented a qualitative approach among 100 public health stakeholders through structured questionnaires and thematic analysis. The quantitative measurements recorded frequency counts alongside tool usage metrics and perceived impacts while qualitative responses brought forward thematic concerns and participant expectations.

Results: The survey participants strongly agreed that AI technology enhances outbreak prediction accuracy (78%) and enables both fast outbreak detection (85%) and expedited resource reallocation (70%). The main issues which stakeholders brought up included privacy concerns about data (66%) and obscurity of algorithms (58%) and excessive machine-driven output dependency (47%). Among the AI tools employed machine learning models were the most prevalent followed by deep learning algorithms. Prediction models are used by 52.5% of respondents according to survey results. Stakeholders expressed positive views toward AI integration as 81% of them endorsed its implementation but approached it with the request to combine computer-generated forecasts with human expert analyses.

Conclusions: The study confirms the increasing acceptance and experienced value of AI when monitoring infectious disease. Despite the benefits, moral and operational challenges highlight the importance of transparent, human focused AI design. Future efforts should focus on improving equal access to trust, purpose and prediction tools.