Public health surveillance and outbreak preparedness for mpox.

To address the complex relationship between environment and health, public-health professionals have recognized the benefits of building enduring interdisciplinary partnerships and of developing innovative Environmental and Public Health Tracking (EPHT) systems. In specific, EPHT can increase understanding of environmental health threats; improve comparability of risks between different areas of the world; enable transparency and trust among citizens and institutions; and inform preventive decision making. EPHT does so by sharing data and knowledge; and by identifying and supporting best practices. In France, the concept of EPHT builds on the observation that our changing environment creates new risks that require both specific surveillance of the link between exposure and health indicators, and syndromic surveillance (SyS) of sentinel health indicators. A specific surveillance of air pollution and health has been informing French policies on air pollution for 23 years. France has also coordinated the European Apheis and Aphekom specific-surveillance projects. Using routine pollution and health data, these projects succeeded by being built on a Europe-wide collaborative network that facilitates decision-making on local and national levels. In addition, since the 2003 heat wave France has developed syndromic surveillance for detecting the health impacts of new threats as diverse as environmental phenomena and emerging infectious diseases. France also coordinated the Triple-S project, which inventoried SyS activities in Europe; and produced guidelines for human and veterinary SyS in Member States and a proposal for a European SyS strategy. Examples of the complementarity between specific and SyS in environmental health in France include: heat and cold waves, air pollution, flooding, storms and industrial accidents. In today's world of open data, EPHT shows great promise for contributing to better informing decision makers and the population on environmental-health risks. Key messages Environmental and Public Health Tracking (EPHT) systems and enduring interdisciplinary partnerships provide an innovative way to address the complex relationship between environment and health. France has been in the forefront of applying innovative EPHT (Environmental and Public Health Tracking) by using complementary specific and syndromic (SyS) surveillance systems.

The One Health concept is a comprehensive approach that highlights the interconnectedness of human, animal, and environmental health. Understanding these interrelationships is essential for effectively managing and mitigating emerging health threats. This approach emphasizes that health issues arise from a complex web of interactions involving humans, animals, and ecosystems. Emerging health threats, such as zoonotic diseases (e.g., mpox, Ebola, avian influenza), antibiotic resistance, and environmental degradation, pose significant risks to global health. Zoonotic diseases, which are transmitted between animals and humans, highlight the direct links between animal and human health. The increase in antibiotic resistance, driven by overuse in both human medicine and livestock farming, leads to the rise of resistant pathogens. Environmental issues like climate change, deforestation, and pollution further exacerbate these threats by altering habitats, disrupting ecosystems, and affecting disease vectors. Addressing these interconnected challenges requires a unified approach that integrates human, animal, and environmental considerations. For example, climate change can shift the distribution of disease vectors like mosquitoes, leading to the spread of diseases such as malaria and dengue fever. Likewise, deforestation can heighten the risk of zoonotic spillover by bringing humans into closer contact with wildlife. 1. To tackle these complex challenges, several measures are crucial: • Integrated surveillance systems Developing systems that monitor human, animal, and environmental health data collectively can improve early detection of outbreaks. Sharing information across sectors is a prerequisite for effective identification of emerging threats. • Strengthening veterinary and public health collaboration Joint research, surveillance, and outbreak management efforts can lead to a more coordinated approach to health emergencies. • Promoting One Health education and training Educating professionals in various fields fosters a collaborative approach to health issues. Training programmes can build a shared understanding of health interconnections. • Implementing sustainable environmental practices Adopting sustainable practices to address environmental degradation helps reduce risks associated with habitat destruction and pollution. • International collaboration and agreements facilitate resource sharing, information exchange, and best practices. 2. To enhance public health strategies within the One Health framework: • Enhance interdisciplinary research Promote research on the intersections of human, animal, and environmental health to understand how changes in one area affect others and to develop more effective interventions. • Foster community engagement Engage communities to improve the adoption of preventive measures and build resilience against health threats through public awareness and community-based programs. Leverage technology and innovation Innovations can offer valuable insights such as data analytics, remote sensing, and genomic surveillance. In our upcoming plenary session, experts from veterinary and public health sciences will present practical examples on One Health collaboration in Europe and discuss how to address challenges in implementing the approach. Moderator Ricardo Mexia Chair of the 17th EPH Conference 2024, President EUPHA Infectious diseases control section Keynote speaker Lorena Boix Deputy Director-General, Directorate General for Health and Food Safety (DG SANTE), European Commission Speakers/Panellists Stef Bronzwaer Cross-Agency One Health Task Force, European Food Safety Authority (EFSA) Barbara Häsler Royal Veterinary College, UK Susana Guedes Pombo Chief Veterinary Officer, Portugal, President World Organization for Animal Health (WOAH) Eva Zažímalová Member of European Commission's Group of Chief Scientific Advisors, Professor of Plant Anatomy and Physiology, Charles University, Czech Republic

Progress toward Implementation of Integrated Systems for Surveillance of HIV Infection and Morbidity in the United States

The COVID-19 pandemic accelerated the development of AI-driven tools to improve public health surveillance and outbreak management. While AI programs have shown promise in disease surveillance, they also present issues such as data privacy, prejudice, and human-AI interactions. This sixth session of the of the WHO Pandemic and Epidemic Intelligence Innovation Forum examines the use of Artificial Intelligence (AI) in public health by collecting the experience of key global health organizations, such the Boston Children's Hospital, the Global South AI for Pandemic & Epidemic Preparedness & Response (AI4PEP) network, Medicines Sans Frontières (MSF), and the University of Sydney. AI's utility in clinical care, particularly in diagnostics, medication discovery, and data processing, has resulted in improvements that may also benefit public health surveillance. However, the use of AI in global health necessitates careful consideration of ethical issues, particularly those involving data use and algorithmic bias. As AI advances, particularly with large language models, public health officials must develop governance frameworks that stress openness, accountability, and fairness. These systems should address worldwide differences in data access and ensure that AI technologies are tailored to specific local needs. Ultimately, AI's ability to improve healthcare efficiency and equity is dependent on multidisciplinary collaboration, community involvement, and inclusive AI designs in ensuring equitable healthcare outcomes to fit the unique demands of global communities.

Heterogeneity and Interoperability in Local Public Health Information Systems.

The emergence and resurgence of pathogens have led to significant global health challenges. Wastewater surveillance has historically been used to track water-borne or fecal-orally transmitted pathogens, providing a sensitive means of monitoring pathogens within a community. This technique offers a comprehensive, real-time, and cost-effective approach to disease surveillance, especially for diseases that are difficult to monitor through individual clinical screenings. This narrative review examines the current state of knowledge on wastewater surveillance, emphasizing important findings and techniques used to detect potential pathogens from wastewater. It includes a review of literature on the detection methods, the pathogens of concern, and the challenges faced in the surveillance process. Wastewater surveillance has proven to be a powerful tool for early warning and timely intervention of infectious diseases. It can detect pathogens shed by asymptomatic and pre-symptomatic individuals, providing an accurate population-level view of disease transmission. The review highlights the applications of wastewater surveillance in tracking key pathogens of concern, such as gastrointestinal pathogens, respiratory pathogens, and viruses like SARS-CoV-2. The review discusses the benefits of wastewater surveillance in public health, particularly its role in enhancing existing systems for infectious disease surveillance. It also addresses the challenges faced, such as the need for improved detection methods and the management of antimicrobial resistance. The potential for wastewater surveillance to inform public health mitigation strategies and outbreak response protocols is emphasized. Wastewater surveillance is a valuable tool in the fight against infectious diseases. It offers a unique perspective on the spread and evolution of pathogens, aiding in the prevention and control of disease epidemics. This review underscores the importance of continued research and development in this field to overcome current challenges and maximize the potential of wastewater surveillance in public health.

Early detection of clusters of health conditions is essential to proactive clinical and public health interventions. Effective intervention strategies require real-time insights into the health needs of the communities. Artificial Intelligence (AI) systems have emerged as a promising avenue to detect patterns in health indicators at an individual and population level. The purpose of this paper is to describe the novel expanded application of AI to detect clusters in health conditions and community health needs to facilitate real-time intervention and prevention strategies. Case-use examples demonstrate the capabilities of AI to harness a variety of data to improve health outcomes in conditions ranging from infectious diseases, non-communicable diseases, and mental health disorders. AI systems have been utilized in syndromic surveillance to detect cases of infectious diseases prior to laboratory-confirmed diagnosis. These AI systems can analyze data from healthcare facilities, laboratories, and online self-reported symptoms to detect potential outbreaks and facilitate timely vaccination, resource allocation and public health messaging to mitigate the spread of disease. Similarly, the spread of vector-borne diseases can be anticipated through the analysis of historical data, weather reports and incidence of disease to identify areas to deploy vector control measures. In the area of mental health, AI algorithms can analyze diverse data sources such as social media posts, emergency hotline calls, emergency department visits, and hospital admissions to identify clusters related to mental health issues including overdoses, suicides, and burnout. The timely detection of such clusters enables prompt intervention, facilitating deployment of targeted mental health support services and community outreach programs to address these issues in a targeted and proactive manner. Identifying trends and characteristics in chronic disease data can guide screening and intervention strategies in real time. Similarly, AI can enhance pharmacovigilance by identifying previously unknown patterns in adverse drug reactions to inform regulatory bodies, healthcare providers and researchers in efforts to provide data-driven, real-time patient safeguards. By harnessing data from air-quality monitors, health records, and meteorology reports, AI systems identify correlations between environmental factors and health issues to empower efforts to address specific environmental health risks. These case-use examples illustrate the potential for AI to serve as a valuable tool to facilitate real-time, data-driven insights to inform proactive clinical and public health intervention strategies. Ongoing challenges in harnessing AI technology for public health surveillance include data privacy, accessing quality data from diverse data sets, and establishing effective communication channels between AI systems and public health authorities. The use of anonymized data to detect clusters and identify the health needs of health regions is a potential strategy to mitigate these challenges. Available resources are limited and must be deployed in a targeted, informed, and timely manner to be most effective. The integration of AI into an expanded all-risks approach to syndromic surveillance represents the next step in identifying and responding to clusters of health-related events in a proactive manner that aligns with community needs while upholding ethical standards and privacy considerations.

Artificial Intelligence (AI) has revolutionized various sectors, and public health surveillance is no exception. With the increasing frequency of global health threats, there is a growing reliance on AI-driven predictive analytics to detect, monitor, and respond to disease outbreaks in real-time. This paper explores the effectiveness, challenges, and ethical implications of integrating AI into public health surveillance systems. The research adopts a cross-disciplinary perspective involving medical professionals, epidemiologists, and public health administrators to holistically examine AI’s role in early outbreak detection, pattern recognition, and health data management. A mixed-methods approach was employed, incorporating quantitative data from structured questionnaires and qualitative insights through case studies. Participants were drawn from government health departments, research institutions, and AI solution vendors. The questionnaire consisted of five key questions assessing AI adoption, trustworthiness, security, predictive capability, and ethical oversight. The responses indicated a high degree of optimism regarding AI's predictive strengths, with over 80% of respondents agreeing that AI improved outbreak forecasting. However, concerns regarding data privacy, algorithmic transparency, and ethical governance remain prevalent, particularly among public health administrators. A comparative case study of two regional surveillance systems—one in Kerala and another in Madhya Pradesh—further illustrates the disparity in AI readiness and policy frameworks. Kerala’s system, which leverages AI for real- time dengue and COVID-19 tracking, showed efficient and ethically sound practices. In contrast, Madhya Pradesh's implementation faced delays, data inconsistencies, and ethical ambiguity due to insufficient regulatory backing and infrastructure. The study highlights that while AI holds immense potential in enhancing surveillance efficacy, its deployment must be accompanied by robust ethical guidelines, stakeholder education, and context-specific adaptation. Without these, AI tools risk eroding public trust, misrepresenting vulnerable populations, and exacerbating existing health inequalities. Therefore, policy frameworks must prioritize transparency, fairness, and inclusivity in AI development and deployment. The paper concludes with strategic recommendations for ethical AI integration into public health systems, emphasizing interdisciplinary collaboration, continuous monitoring, and community-level engagement to realize the full promise of AI in healthcare surveillance.

Rarely does one get to be a participant in the frontier of a new discipline or area of research. But since 9/11 the field of syndromic surveillance has provided public health researchers and practitioners just such an opportunity. Some of the $5 billion in funds awarded by the Centers for Disease Control and Prevention (CDC) during fiscal years 2002– 2007 to states, territories, and several large local jurisdictions to enhance public health capacities to detect, respond to, and recover from bioterrorism events have been allocated to expanding or improving surveillance systems [1]. These expansions have included developing public health capabilities in the area of biosurveillance and its sub-field, syndromic surveillance. The aim of these new systems is to use near ‘real-time’ data and automated tools to detect and characterize outbreaks (natural or intentional) before conventional methods. Several systems have become popular for use by state and local health jurisdictions in conducting these syndromic surveillance activities, including the Early Aberration Reporting System (EARS), BioSense, and the Electronic Surveillance System for the Early Notification of Community Based Epidemics (ESSENCE II) [2--4]. In the initial years of implementing syndromic surveillance systems, state and local health departments used the applications primarily to detect bioterrorism-related events [5]. However, due to developing needs, the public health community has applied biosurveillance more broadly to other situations, including influenza and fire-related illness surveillance, as well as to affirm the absence of outbreaks after a disaster [6]. In Monterey County, California, we have had similar needs and particularly wanted a program that we could use for local purposes. We began using EARS in 2005 because of its flexibility for developing syndrome definitions and applied it to a variety of situations, from daily ongoing surveillance for influenza-like illness to emerging situations such as respiratory syndromes potentially associated with an aerial spraying of pesticide (Monterey County, unpublished data). The field is young, and Fricker (this issue) is timely in his advocacy for the standardization of terms and methods used for biosurveillance and particularly syndromic surveillance. Public health practitioners should support such standardization and have outlined areas for further research and evaluation [7]. An equally pressing issue is the statistical methods that are currently used for biosurveillance. Fricker does an admirable job of summarizing how methodologies from the field of industrial statistical process control have been adapted for use in detecting attacks by terrorists on the health of a population. A comprehensive understanding of bioterrorism is relatively recent in the literature and, for research purposes, the number of modern bioterrorist events that resulted in actual cases have been few [8]. However, there has been an apparent recent increase in the use of biological agents with 40 of 56 confirmed criminal cases and 19 of 27 confirmed terrorist cases in the 20th century occurring in the 1990s [8]. While these provide a relative paucity of events for modeling purposes and for developing effective syndromic surveillance methods, they underscore the necessity of surveillance methods that can be used to improve time to event detection and to help ensure that a bioterrorist attack is detected. This is likely why there appears to be general support in public health for an expansion of syndromic and other public health surveillance systems. The majority of public health jurisdictions surveyed in the United States use some form of syndromic surveillance [5]. Internationally, the World Health Organization revised the International Health Regulations in 2007 to include the concept of syndromic surveillance as part of an expanded traditional disease notification system. May et al. [9] provide a review of its uses in developing nations. There is a national recognition of the potential usefulness and importance for developing syndromic surveillance systems. In December 2009, proposed regulations were released by the Centers for Medicare and Medicaid Services in the United States defining ‘meaningful use’ of electronic health records (EHR). In addition, the Office of the National Coordinator for Health Information Technology released an interim final rule describing the required certification standards

This study explores the transformative role of artificial intelligence (AI) in environmental health and public safety within the USA, focusing on pollution monitoring, emergency response, and sustainable practices for public. With the growing challenges posed by climate change, pollution, and emerging public health threats, the integration of Artificial Intelligence (AI) in environmental health and public safety strategies has become imperative. This comprehensive review explores the diverse array of AI applications implemented in the United States to address environmental issues and enhance public safety measures. The paper analyzes the multifaceted role of AI across various domains, including air and water quality monitoring, disease surveillance, disaster response, and infrastructure resilience. The advancements in AI technologies that have revolutionized data collection, analysis, and prediction in environmental health are examined. Machine learning algorithms, sensor networks, and satellite imagery are examined as tools for real-time monitoring and early detection of environmental hazards. Additionally, the paper investigates the integration of AI in public health surveillance systems, showcasing how predictive analytics and data-driven models contribute to the identification and containment of infectious diseases. Furthermore, the study sheds light on the incorporation of AI in disaster management, emphasizing the role of predictive modeling and risk assessment in optimizing emergency response strategies. The implementation of smart city technologies and intelligent infrastructure systems is discussed, highlighting how AI contributes to enhancing public safety and minimizing the impact of natural disasters. The review also critically evaluates the ethical, legal, and privacy considerations associated with the widespread adoption of AI in environmental health and public safety initiatives. It addresses concerns related to data security, algorithmic biases, and the need for transparent and accountable governance frameworks. Through an in-depth analysis of case studies, policies, and initiatives, this review provides insights into the successes and challenges of AI implementation in the USA. It concludes with recommendations for future research directions and policy considerations to ensure the responsible and effective integration of AI technologies in safeguarding environmental health and public safety. The findings presented in this review contribute to the broader discourse on leveraging AI for sustainable and resilient communities in the face of evolving environmental and public health challenges.

To assess how integration of HIV surveillance and field services might influence surveillance data and linkage to care metrics. We used HIV surveillance and field services data from King County, Washington, to assess potential impact of misclassification of prior diagnoses on numbers of new diagnoses. The relationship between partner services and linkage to care was evaluated with multivariable log-binomial regression models. Of the 2842 people who entered the King County HIV Surveillance System in 2010 to 2015, 52% were newly diagnosed, 41% had a confirmed prior diagnosis in another state, and 7% had an unconfirmed prior diagnosis. Twelve percent of those classified as newly diagnosed for purposes of national HIV surveillance self-reported a prior HIV diagnosis that was unconfirmed. Partner services recipients were more likely than nonrecipients to link to care within 30 days (adjusted risk ratio [RR] = 1.10; 95% confidence interval [CI] = 1.03, 1.18) and 90 days (adjusted RR = 1.07; 95% CI = 1.01, 1.14) of diagnosis. Integration of HIV surveillance, partner services, and care linkage efforts may improve the accuracy of HIV surveillance data and facilitate timely linkage to care.

Background The integration of artificial intelligence (AI) into public health surveillance is revolutionizing how health threats are monitored, predicted, and managed. Traditional surveillance systems often face challenges such as reporting delays, limited scalability, and inefficiencies in real‐time response. Leveraging approaches such as machine learning (ML), deep learning (DL), and natural language processing (NLP), AI enables the analysis of extensive and diverse datasets, facilitating the generation of timely and actionable insights for disease prevention and control. Aim This review aimed to systematically explore how AI is utilized to enhance public health surveillance through real‐time data analytics and disease prediction. Methods An extensive literature search was performed using five major databases: PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar to identify relevant studies published between 2010 and 2025. Eligible studies applied AI methodologies to real‐time surveillance, utilized structured or unstructured health‐related data, and reported predictive or prescriptive outcomes. All the studies included were evaluated for methodological rigor, and the results were thematically synthesized. Results Thirty‐nine studies met the inclusion criteria. The majority employed ML and DL models such as Random Forests (RFs), while others incorporated NLP for analyzing text‐based data. AI systems were utilized for descriptive monitoring, predictive modeling of disease outbreaks, and prescriptive analytics to support resource allocation. Real‐time analytics demonstrated high accuracy and timeliness in forecasting disease trends, particularly for conditions such as COVID‐19, influenza, and dengue. Hybrid models that combined multiple AI techniques further enhanced predictive performance. Conclusion AI–driven surveillance systems hold considerable promise for transforming public health monitoring. They enable faster detection, improved forecasting, and more efficient public health responses. However, challenges remain, including data standardization, ethical governance, and infrastructure disparities. Addressing these barriers is essential for equitable, scalable AI implementation in global health surveillance.

Background: Artificial Intelligence (AI) is increasingly revolutionizing public health surveillance, particularly in regions with constrained healthcare infrastructure. This scoping review examines the application of AI in public health surveillance across Africa, identifying existing implementations, challenges, and opportunities. AI technologies such as machine learning, natural language processing, and predictive analytics enhance epidemic intelligence by analyzing vast datasets from diverse sources, including electronic health records, social media, and environmental sensors. These AI-driven tools provide early warnings for outbreaks, improve disease surveillance, and facilitate timely public health responses. Methods: A systematic search of databases, including Pubmed, Google Scholar, Researchgate, Web of Science, Scopus, Scientific Research, African Journal of Health Informatics, International Journal of Infectious Diseases, ScienceDirect, African Journal of Biotechnology, PloS One, The Lancet, JMIMR, BMJ, and BMC. The search covers publications from January 2010 to February 2025, spanning for 15 years. A total of 1411 articles. An additional 44 records were identified through other sources after removing 201 duplicates; 1254 unique articles were screened based on titles and abstracts. One thousand one hundred and twenty-seven (1127) records were excluded as they did not meet the inclusion criteria. Then, 127 full-text articles were assessed for eligibility, and 54 full-text articles were excluded for various reasons: study in non-African location (52) Not focused on AI applications (12), challenges and opportunities, Insufficient Data (9) Finally, 54 studies were included in the qualitative synthesis. Results: Following a rigorous selection process, 54 studies were included in the qualitative synthesis. Most studies (83.33%) were published as peer-reviewed journal articles, while technical reports and theses were less common, with five (9.26%) and four (7.41%) studies, respectively. The primary focus of these studies varied: 39 (72.22%) explored AI applications in disease detection and prediction, 25 (46.30%) examined AI applications in disease surveillance, 18 (33.33%) highlighted challenges in AI adoption for healthcare, and 15 (27.78%) focused on real-time surveillance and reporting in Africa. Findings reveal that AI is actively utilized in African public health systems for disease prediction, outbreak surveillance, and resource allocation. However, several challenges hinder its full potential, including inadequate infrastructure, data privacy concerns, limited access to high-quality datasets, and a shortage of AI-trained healthcare professionals. Despite these barriers, AI presents great opportunities for strengthening health security in Africa by improving diagnostic accuracy, optimizing healthcare interventions, and enhancing real-time epidemiological analysis. Conclusion: Artificial intelligence presents a transformative opportunity for health surveillance in Africa, particularly in diagnostics and disease prediction. AI-powered tools, such as mobile diagnostic applications and predictive models, enhance healthcare accessibility in resource-limited settings by analyzing vast datasets for early disease detection. Successful implementations, including AI-driven malaria mapping and tuberculosis detection through chest X-ray analysis, HIV, cholera, Ebola, measles, Zika virus, and malaria, enabling targeted screening interventions, personalized treatment plans, and efficient resource allocation, demonstrate AI’s potential to improve public health outcomes. Despite challenges such as infrastructure limitations and data privacy concerns, AI continues to revolutionize disease monitoring and response. By leveraging machine learning for targeted interventions and efficient resource allocation, AI holds promise for a future of more proactive and effective healthcare across the continent of Africa.

BackgroundEarly threat detection and situational awareness are vital to achieving a comprehensive and accurate view of health-related events for federal, state, and local health agencies. Key to this are public health and syndromic surveillance systems that can analyze large data sets to discover patterns, trends, and correlations of public health significance. In 2020, Department of Veterans Affairs (VA) evaluated its public health surveillance system and identified areas for improvement.MethodsUsing the Centers for Disease Control and Prevention (CDC) Guidelines for Evaluating Public Health Surveillance Systems, we assessed the ability of the Praedico Surveillance System to perform public health surveillance for a variety of health issues and evaluated its performance compared to an enterprise data solution (VA Corporate Data Warehouse), legacy surveillance system (VA ESSENCE) and a national, collaborative syndromic surveillance platform (CDC NSSP BioSense).ResultsReview of system attributes found that the system was simple, flexible, and stable. Representativeness, timeliness, sensitivity, and Predictive Value Positive were acceptable but could be further improved. Data quality issues and acceptability present challenges that potentially affect the overall usefulness of the system.ConclusionsPraedico is a customizable surveillance and data analytics platform built on big data technologies. Functionality is straightforward, with rapid query generation and runtimes. Data can be graphed, mapped, analyzed, and shared with key decision makers and stakeholders. Evaluation findings suggest that future development and system enhancements should focus on addressing Praedico data quality issues and improving user acceptability. Because Praedico is designed to handle big data queries and work with data from a variety of sources, it could be enlisted as a tool for interdepartmental and interagency collaboration and public health data sharing. We suggest that future system evaluations include measurements of value and effectiveness along with additional organizations and functional assessments.

The increasing prevalence of illnesses, particularly Salmonella infections, presents a significant public health challenge. Traditional surveillance and outbreak management methods are resource-intensive and often must catch up to real-time occurrences. This chapter explores the application of artificial intelligence (AI) within a systems thinking framework to revolutionize public health surveillance and outbreak response for Salmonella. By harnessing AI-driven tools for data analysis, early detection, source attribution, and intervention planning, public health agencies can enhance their capacity to prevent and mitigate Salmonella outbreaks. This chapter discusses the potential of AI-driven systems to transform the landscape of public health. The chapter proposes AI as a holistic approach integrating technology, data, and human expertise for more effective Salmonella outbreak control based on actual life outbreaks and the historical contexts of the of a real outbreak event.