In the wake of the COVID-19 pandemic, the convergence of Artificial Intelligence (AI), Big Data, and in silico simulation has emerged as a cornerstone in pandemic surveillance and public health informatics. The integration of these disciplines is creating powerful tools for real-time data analysis, predictive modelling, and virtual simulations of disease dynamics. It facilitated real-time decision-making, optimized resource allocation, and enhanced disease surveillance. This transformative period witnessed AI's potential to reshape public health strategies, emphasizing its significance in future epidemic preparedness.

The primary objective of this Research Topic is to collate groundbreaking research and critical reviews that highlight AI's contributions during the COVID-19 era and its implications for future epidemic strategies. We aim to foster a comprehensive understanding of the pivotal AI-driven methodologies in the pandemic response and how these innovations can be harnessed for future health crises. By synthesizing lessons learned and charting the trajectory of AI and big data ecosystems in epidemic management, this issue seeks to provide a roadmap for integrating AI more seamlessly into global health strategies, ensuring that we are better equipped to tackle subsequent outbreaks with agility and precision.

This call for papers invites submissions that not only recognize the pivotal role of AI during the COVID-19 crisis but also expand on how AI when coupled with Big Data and in silico approaches, can revolutionize our preparedness and response to future public health emergencies. We are seeking groundbreaking research and insightful reviews that:
• Demonstrate AI's role in synthesizing Big Data for pandemic forecasting and real-time surveillance.
• Illustrate the impact of AI-driven simulations in understanding and managing epidemic spread.
• Detail the applications of AI in enhancing public health informatics and data-driven decision-making.
• Showcase in silico modelling as a tool for strategic resource distribution and policy formulation.
• Explore AI's capacity to integrate with Big Data for robust epidemiological monitoring.
• Investigate the use of AI and Big Data to provide mental health support during health crises.
• Examine how AI and Big Data can promote healthcare equity, especially in underserved areas.
• Address the ethical implications of employing AI and simulation in public health strategies.
• Discuss the interplay between AI, Big Data, and in silico techniques in strengthening digital health innovations.

The areas of interest comprise, but are not restricted to:
• AI-driven early warning mechanisms and risk evaluation;
• AI and big data ecosystems in advancing public health analytics and research;
• AI-enhanced resource distribution and strategic decision-making;
• AI's role in epidemiological monitoring and epidemic control;
• AI's application in crisis intervention and humanitarian assistance;
• AI's contribution to mental well-being and psychological aid during emergencies;
• AI's potential to ensure health parity and healthcare accessibility in underserved regions;
• The ethical, legal, and societal dimensions of AI's integration in global health and epidemic response;
• The synergy of AI with other digital health innovations during health crises.

Our aim is to compile a comprehensive volume that not only emphasizes the technological prowess of AI but also its practical deployment in conjunction with Big Data and in silico methods to offer a more nuanced, effective, and equitable global health response. We encourage contributions that provide not just theoretical frameworks but also empirical evidence of the benefits and challenges of these integrated approaches in public health domains.

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Frontiers in Artificial Intelligence

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