ARTIFICIAL INTELLIGENCE IN EPIDEMIOLOGY
TRANSFORMING DISEASE SURVEILLANCE AND PUBLIC HEALTH
DOI:
https://doi.org/10.58395/gvq1t911Keywords:
Artificial Intelligence, Machine Learning, Disease Modeling, Outbreak Detection, Public Health SurveillanceAbstract
Introduction: Epidemiology studies the distribution and determinants of health events in populations to control and prevent diseases. The integration of artificial intelligence (AI) has recently transformed epidemiology by enabling analysis of complex, large-scale data to improve disease surveillance, prediction, and decision-making.
Aim: To summarize recent advances in AI applications within epidemiology.
Materials and Methods: A structured search of major databases identified English-language studies from 2018 to 2025. Relevant articles on AI techniques for modeling, prediction, outbreak detection, and integration with traditional methods were included. \
Results and Discussion: AI’s role in epidemiology has evolved from early machine learning to advanced deep learning and natural language processing, enhancing outbreak tracking, disease modeling, geospatial visualization, diagnosis, and public sentiment analysis. Integration of AI with mechanistic models has improved forecasting and intervention assessments by capturing complex transmission dynamics and adapting to real-time data. AI-driven tools outperform traditional methods in predictive accuracy, enabling earlier detection of diseases. AI also processes large, heterogeneous datasets, uncovering non-linear relationships and supporting causal inference. Challenges remain, including data bias, privacy concerns, and the opacity of “black box” models. Addressing these requires ethical frameworks, transparency, and interdisciplinary collaboration. The expanding AI epidemiology market, driven by globalization, climate change, and big data, offers opportunities for improved public health responsiveness. Future research should focus on standardizing validation, integrating biological and social factors, and ensuring transparency.
Conclusion: AI has transformative potential for epidemiology, but responsible use depends on overcoming ethical, technical, and structural challenges through collaborative governance to promote health equity and public trust.
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References
1. Crichton N, Mulhall A. Epidemiology and health. In: Health Studies: An Introduction. Singapore: Springer Singapore; 2022. p. 49-83. http://doi.org/10.1007/978-981-16-2149-9_3
2. Centers for Disease Control and Prevention [Internet]. Lesson 1: Introduction to Epidemiology. Section 1: Definition of Epidemiology. Atlanta (GA): CDC; [cited 2025 Jul 7]. Available from: https://archive.cdc.gov/www_cdc_gov/csels/dsepd/ss1978/lesson1/section1.html
3. Encyclopaedia Britannica [Internet]. Epidemiology. Chicago (IL): Britannica; [cited 2025 Jul 7]. Available from: https://www.britannica.com/science/epidemiology
4. Hamilton AJ, Strauss AT, Martinez DA, Hinson JS, Levin S, Lin G, et al. Machine learning and artificial intelligence: applications in healthcare epidemiology. Antimicrob Steward Healthc Epidemiol. 2021 Oct 7;1(1):e28. http://doi.org/10.1017/ash.2021.192
5. Anjaria P, Asediya V, Bhavsar P, Pathak A, Desai D, Patil V. Artificial Intelligence in Public Health: Revolutionizing Epidemiological Surveillance for Pandemic Preparedness and Equitable Vaccine Access. Vaccines (Basel). 2023 Jun 26;11(7):1154. http://doi.org/10.3390/vaccines11071154
6. Bibault JE, Xing L. Apports de l’Intelligence Artificielle en épidémiologie [Artificial Intelligence in epidemiology]. Cancer Radiother. 2021 Oct;25(6-7):627-629. French. http://doi.org/10.1016/j.canrad.2021.06.004
7. Thiébaut R, Thiessard F; Section Editors for the IMIA Yearbook Section on Public Health and Epidemiology Informatics. Artificial Intelligence in Public Health and Epidemiology. Yearb Med Inform. 2018 Aug;27(1):207-210. http://doi.org/10.1055/s-0038-1667082
8. Aldousari E, Kithinji D. Artificial intelligence and health information: A bibliometric analysis of three decades of research. Health Inform J. 2024 Jul-Sep;30(3):14604582241283969. http://doi.org/10.1177/14604582241283969
9. Chen S, Yu J, Chamouni S, Wang Y, Li Y. Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions. BMC Med. 2024 Sep 2;22(1):354. http://doi.org/10.1186/s12916-024-03566-x
10. Aamo Iorliam KB. A Scoping Literature Review of Artificial Intelligence in Epidemiology: Uses, Applications, Challenges and Future Trends. J Comput Theor Appl. 2024 Apr;2(2):204-228. http://doi.org/10.62411/jcta.10350
11. Lyutsova E, Pavlova K, Gospodinova M, Radkova D. The integration of artificial intelligence in malaria control and surveillance. Scripta Scientifica Medica. 2025;55(2). http://doi.org/10.14748/ssm.v55i2.9934
12. Pastor-Escuredo D, Olmos LE. The landscape of Artificial Intelligence Models and Applications for Epidemic Outbreaks. Med Res Arch. 2022;10(10). http://doi.org/10.18103/mra.v10i10.3175
13. Ye Y, Pandey A, Bawden C, Sumsuzzman DM, Rajput R, Shoukat A, et al. Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges. Nat Commun. 2025 Jan 10;16(1):581. http://doi.org/10.1038/s41467-024-55461-x
14. Abdulkareem M, Petersen SE. The Promise of AI in Detection, Diagnosis, and Epidemiology for Combating COVID-19: Beyond the Hype. Front Artif Intell. 2021 May 14;4:652669. http://doi.org/10.3389/frai.2021.652669
15. Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc. 2020 Oct;92(4):807-812. http://doi.org/10.1016/j.gie.2020.06.040.
16. GlobeNewswire [Internet]. Artificial Intelligence in Epidemiology Research 2024-2030 Featuring Alphabet, Bayer, Clarivate Analytics, Cognizant, eClinicalWorks, Epic Systems, Intel, Komodo Health, Koninklijke Philip. 2025 Mar 3 [cited 2025 Jul 7]. Available from: https://www.globenewswire.com/news-release/2025/03/03/3035535/28124/en/Artificial-Intelligence-in-Epidemiology-Research-2024-2030-Featuring-Alphabet-Bayer-Clarivate-Analytics-Cognizant-eClinical-Works-Epic-Systems-Intel-Komodo-Health-Koninklijke-Philip.html
17. Kraemer MUG, Tsui JLH, Chang SY, et al. Artificial intelligence for modelling infectious disease epidemics. Nature. 2025;638:623-635. http://doi.org/10.1038/s41586-024-08564-w
18. Zhao AP, Li S, Cao Z, Hu PJ, Wang J, Xiang Y, et al. AI for science: Predicting infectious diseases. J Saf Sci Resil. 2024 Jun;5(2):130-146. http://doi.org/10.1016/j.jnlssr.2024.02.002
19. Du H, Zhao Y, Zhao J, et al. Advancing real-time infectious disease forecasting using large language models. Nat Comput Sci. 2025;5:467-480. http://doi.org/10.1038/s43588-025-00798-6
20. Goncalves AR, Pico JC, Hu Y, Schlessinger D, Greene J, O'Suilleabhain L, et al. AI-Enabled Diagnostic Prediction within Electronic Health Records to Enhance Biosurveillance and Early Outbreak Detection. medRxiv [Pre-print]. 2025 May 16:2025.05.14.25327606. http://doi.org/10.1101/2025.05.14.25327606
21. Goh KH, Wang L, Yeow AYK, Poh H, Li K, Yeow JJL, et al. Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare. Nat Commun. 2021 Jan 29;12(1):711. http://doi.org/10.1038/s41467-021-20910-4
22. Olawade DB, Wada OJ, David-Olawade AC, Kunonga E, Abaire O, Ling J. Using artificial intelligence to improve public health: a narrative review. Front Public Health. 2023 Oct 26;11:1196397. http://doi.org/10.3389/fpubh.2023.1196397
23. European Centre for Disease Prevention and Control (ECDC). ECDC Digital Roadmap 2022−2027 [Internet]. Stockholm: ECDC; 2022 [cited 2025 Sep 1]. Available from: https://www.ecdc.europa.eu/sites/default/files/documents/ECDC-Digital-Roadmap-2022-2027_final_with_covers.pdf
24. Gawande MS, Zade N, Kumar P, Gundewar S, Weerarathna IN, Verma P. The role of artificial intelligence in pandemic responses: from epidemiological modeling to vaccine development. Mol Biomed. 2025 Jan 3;6(1):1. http://doi.org/10.1186/s43556-024-00238-3
25. Serghiou S, Rough K. Deep Learning for Epidemiologists: An Introduction to Neural Networks. Am J Epidemiol. 2023 Nov 3;192(11):1904-1916. http://doi.org/10.1093/aje/kwad107
26. Theodosiou AA, Read RC. Artificial intelligence, machine learning and deep learning: Potential resources for the infection clinician. J Infect. 2023 Oct;87(4):287-294. http://doi.org/10.1016/j.jinf.2023.07.011
27. Uc Castillo JL, Marín Celestino AE, Martínez Cruz DA, Tuxpan Vargas J, Ramos Leal JA, Morán Ramírez J. A systematic review of Machine Learning and Deep Learning approaches in Mexico: challenges and opportunities. Front Artif Intell. 2025 Jan 7;7:1479855. http://doi.org/10.3389/frai.2024.1479855
28. Feng W, Luo Y. Preeclampsia and its prediction: traditional versus contemporary predictive methods. J Matern Fetal Neonatal Med. 2024 Dec;37(1):2388171. http://doi.org/10.1080/14767058.2024.2388171
29. Shafi M, Shabir S, Jan S, Wani ZA, Rather MA, Beigh YA, et al. The role of artificial intelligence in detecting avian influenza virus outbreaks: A review. Open Vet J. 2025 May;15(5):1880-1894. http://doi.org/10.5455/OVJ.2025.v15.i5.4
30. Setegn GM, Dejene BE. Explainable AI for Symptom-Based Detection of Monkeypox: a machine learning approach. BMC Infect Dis. 2025 Mar 26;25(1):419. http://doi.org/10.1186/s12879-025-10738-4
31. Khalifa M, Albadawy M. AI in diagnostic imaging: Revolutionising accuracy and efficiency. Comput Methods Programs Biomed Update. 2024;5:100146. http://doi.org/10.1016/j.cmpbup.2024.100146
32. Shelke YP, Badge AK, Bankar NJ. Applications of Artificial Intelligence in Microbial Diagnosis. Cureus. 2023 Nov 24;15(11):e49366. http://doi.org/10.7759/cureus.49366
33. Lewin S, Chetty R, Ihdayhid AR, Dwivedi G. Ethical Challenges and Opportunities in Applying Artificial Intelligence to Cardiovascular Medicine. Can J Cardiol. 2024 Oct;40(10):1897-1906. http://doi.org/10.1016/j.cjca.2024.06.029
34. Chen Y, Clayton EW, Novak LL, Anders S, Malin B. Human-Centered Design to Address Biases in Artificial Intelligence. J Med Internet Res. 2023 Mar 24;25:e43251. http://doi.org/10.2196/43251
35. Williamson SM, Prybutok V. Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare. Appl Sci. 2024;14(2):675. http://doi.org/10.3390/app14020675
36. Kiseleva A, Kotzinos D, De Hert P. Transparency of AI in Healthcare as a Multilayered System of Accountabilities: Between Legal Requirements and Technical Limitations. Front Artif Intell. 2022 May 30;5:879603. http://doi.org/10.3389/frai.2022.879603
37. Morley J, Machado CCV, Burr C, Cowls J, Joshi I, Taddeo M, et al. The ethics of AI in health care: A mapping review. Soc Sci Med. 2020 Sep;260:113172. http://doi.org/10.1016/j.socscimed.2020.113172
38. Weiner EB, Dankwa-Mullan I, Nelson WA, Hassanpour S. Ethical challenges and evolving strategies in the integration of artificial intelligence into clinical practice. PLOS Digit Health. 2025 Apr 8;4(4):e0000810. http://doi.org/10.1371/journal.pdig.0000810
39. Al-Hwsali A, Alsaadi B, Abdi N, Khatab S, Alzubaidi M, Solaiman B, et al. Scoping Review: Legal and Ethical Principles of Artificial Intelligence in Public Health. Stud Health Technol Inform. 2023 Jun 29;305:640-643. http://doi.org/10.3233/SHTI230579
40. Castaño Castaño S. La inteligencia artificial en Salud Pública: oportunidades, retos éticos y perspectivas futuras [Artificial intelligence in Public Health: opportunities, ethical challenges and future perspectives]. Rev Esp Salud Publica. 2025 Mar 26;99:e202503017. Spanish.
41. Rodríguez-González A, Zanin M, Menasalvas-Ruiz E. Public Health and Epidemiology Informatics: Can Artificial Intelligence Help Future Global Challenges? An Overview of Antimicrobial Resistance and Impact of Climate Change in Disease Epidemiology. Yearb Med Inform. 2019 Aug;28(1):224-231. http://doi.org/10.1055/s-0039-1677910
42. Pizzulli VA, Telesca V, Covatariu G. Analysis of Correlation between Climate Change and Human Health Based on a Machine Learning Approach. Healthcare (Basel). 2021 Jan 17;9(1):86. http://doi.org/10.3390/healthcare9010086
43. Liu J, Lai S, Rai AA, Hassan A, Mushtaq RT. Exploring the Potential of Big Data Analytics in Urban Epidemiology Control: A Comprehensive Study Using CiteSpace. Int J Environ Res Public Health. 2023 Feb 22;20(5):3930. http://doi.org/10.3390/ ijerph20053930
44. Jiao Z, Ji H, Yan J, Qi X. Application of big data and artificial intelligence in epidemic surveillance and containment. Intell Med. 2023 Feb;3(1):36-43. http://doi.org/10.1016/j.imed.2022.10.003
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