Artificial intelligence enhanced infectious disease surveillance - A call for global collaboration

Dear Editor,

In the ever-evolving landscape of infectious diseases, the emergence of novel pathogens and the resurgence of known threats underscore the urgent need for enhanced surveillance systems. Traditional methods, while indispensable, often encounter limitations in speed, scalability, and accuracy. Artificial Intelligence (AI) has emerged as a powerful tool in infectious disease surveillance, offering the potential to revolutionize early detection and response to emerging infections. The global collaboration in harnessing AI for infectious disease surveillance is imperative to effectively combat the shifting spectrum of infectious disease threats that are a reality in today's interconnected world [1]. The utilization of AI in infectious disease surveillance is not only about the technological advancements but also about the collaborative efforts of nations and organizations to share data, knowledge, and expertise to build a global defense against emerging infections [1].

AI-Enhanced Infectious Disease Surveillance helps in enhancing the detection, tracking, and management of infectious diseases by analyzing extensive datasets from diverse sources such as electronic health records, travel records, social media, and weather data to discern patterns indicative of potential outbreaks, facilitating early detection and swift response. It also aid though predictive modeling by incorporating factors like geography, population density, travel patterns, and environmental conditions, enables forecasting disease spread for proactive preparation. AI optimizes resource allocation during outbreaks, ensuring efficient distribution of medical personnel, vaccines, and diagnostic tests to areas in need. Automated contact tracing through AI analyzes digital records to identify potential exposure, breaking transmission chains. Additionally, AI aids in quicker and more accurate infectious disease diagnosis by analyzing medical images and data, ultimately revolutionizing disease tracking and management [Fig. 1].

Fig. 1.

An overview of AI-Enhanced infectious disease surveillance.

The increasing global trends in emerging infectious diseases have necessitated more efficient diagnostic methods and thorough surveillance, which have contributed to the rise in reported infectious diseases [2]. The use of rapid epidemic intelligence from internet-based sources has shown utility and potential in enhancing infectious disease surveillance, emphasizing the need for leveraging informal data through internet-based intelligence methods for rapid epidemic surveillance [3]. Furthermore, the big data era has brought about opportunities for faster and locally relevant infectious disease surveillance systems, with influenza serving as an exemplar of an emerging infection that has traditionally been considered a model system for surveillance and modeling [3].

AI-enabled prediction models, such as the SIRVD-DL (Susceptible, Infected, Recovered, Vaccinated, and Deceased – Deep Learning) model for COVID-19, have demonstrated significant improvements in single-day predictions and adaptability to short- and medium-term predictions, enhancing the interpretability and robustness of overall predictions [3,4]. The application of data science approaches to infectious disease surveillance has presented innovative methods that were not previously possible without the advancements in information and communications technology, highlighting the potential of AI in transforming surveillance systems. Additionally, internet search data with spatiotemporal analysis has been identified as a novel data source for monitoring diseases and epidemics at both spatial and temporal scales, further emphasizing the potential of AI in enhancing surveillance intelligence [5].

However, harnessing the full potential of AI requires overcoming formidable challenges. Data privacy concerns, ethical considerations around algorithms and bias, and limited access to high-quality data in resource-constrained regions necessitate responsible and collaborative approaches [3]. This is where global collaboration becomes paramount. Sharing data across borders, standardizing formats, and co-developing AI tools for disease surveillance like World Health Organization's Global Influenza Surveillance Network (GISRS) and World Bank's AI for Epidemic Preparedness (AI4EP) initiative demonstrate the power of collective action in developing AI solutions for global health challenges. Investing in training for health workers and establishing ethical frameworks around AI use will ensure trust and transparency in using this powerful technology [3,5].

Several strategies can be employed to explain practically how AI could help with surveillance without compromising the ethical and privacy issues. Firstly, AI systems can utilize federated learning, which allows algorithms to learn from decentralized data sources without transferring sensitive information to a central server. This approach helps maintain patient confidentiality while still enabling the aggregation of valuable insights across different regions. Secondly, AI can enhance surveillance by analyzing anonymized data from wearable devices and social media, detecting early signs of outbreaks without identifying individual users. For instance, monitoring aggregated data on health trends or mobility patterns can provide public health officials with timely alerts about potential outbreaks while respecting personal privacy. Lastly, implementing explainable AI techniques ensures transparency in how AI systems make decisions, fostering trust among stakeholders and addressing ethical concerns related to algorithmic biases and accountability [6].

Strict data governance protocols, including encryption, access controls, and transparent regulatory frameworks, can further ensure the ethical handling of health data. Additionally, establishing global ethical standards and privacy safeguards can promote responsible AI use, allowing collaboration without sacrificing individuals' rights. By prioritizing privacy-by-design principles and strict oversight, AI-driven surveillance systems can effectively manage outbreaks while maintaining the highest ethical standards.

In conclusion, the role of AI-enhanced infectious disease surveillance is pivotal, and global collaboration is essential for maximizing its potential. Leveraging AI for surveillance systems, such as prediction models and data science approaches, can significantly enhance early detection and response to infectious diseases. However, ethical considerations must be carefully integrated into the implementation of AI-enhanced surveillance systems to ensure responsible and trustworthy practices which are crucial for building a global defense against emerging infections.

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CRediT authorship contribution statement

Tarun Kumar Suvvari: Writing – review & editing, Writing – original draft, Project administration, Conceptualization. Venkataramana Kandi: Writing – review & editing, Writing – original draft, Supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Handling Editor: Patricia Schlagenhauf