Transmission Dynamics of Avian Influenza in Human Populations: Integrating Artificial Intelligence as a Critical Predictive Parameter
Authors
Vinoba Bhave University, Hazaribag - 825301, India (India)
Vinoba Bhave University, Hazaribag - 825301, India (India)
Article Information
DOI: 10.51584/IJRIAS.2026.110200163
Subject Category: Social Media
Volume/Issue: 11/2 | Page No: 1739-1752
Publication Timeline
Submitted: 2026-03-01
Accepted: 2026-03-06
Published: 2026-03-23
Abstract
Avian influenza remains a significant zoonotic threat due to its rapid viral evolution, sporadic spillover into human populations, and potential to trigger large-scale outbreaks. Traditional surveillance systems often detect emerging infections only after substantial transmission has occurred, highlighting the need for predictive analytical tools capable of early outbreak detection. In this study, we investigate the transmission dynamics of avian influenza in human populations by integrating artificial intelligence–based data analysis with mathematical epidemic modeling. Historical human case data reported by the World Health Organization and the Centers for Disease Control and Prevention from 2003 to 2024 were analyzed using AI-assisted smoothing, regression-based forecasting, and scenario-based simulations to identify long-term epidemiological patterns and potential future trajectories. To provide a theoretical foundation for these empirical observations, we formulate a SEIR-type compartmental model incorporating an artificial intelligence control parameter that represents enhanced surveillance and intervention capability. Using the next-generation matrix method, the basic reproduction number is derived and analytical results are established for the stability of the disease-free equilibrium. The analysis demonstrates that improvements in AI-supported surveillance reduce the effective transmission rate and consequently decrease the reproduction number, leading to epidemic suppression when a critical threshold is exceeded. Simulation results further illustrate how enhanced surveillance and early detection can significantly alter outbreak trajectories under various epidemiological scenarios. These findings highlight the potential of integrating artificial intelligence with mathematical epidemiology to strengthen early warning systems, improve outbreak preparedness, and support One Health strategies for the control of avian influenza
Keywords
Avian influenza; Transmission dynamics; Zoonotic spillover
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References
1. World Health Organization. Avian Influenza: Key Facts. WHO Press; 2003. [Google Scholar] [Crossref]
2. Centers for Disease Control and Prevention. Highly Pathogenic Avian Influenza: Human Cases Overview. CDC; 2004. [Google Scholar] [Crossref]
3. Writing Committee of WHO Consultation. Avian influenza A(H5N1) infection in humans. N Engl J Med. 2005. [Google Scholar] [Crossref]
4. Peiris JSM et al. Emergence of avian influenza viruses in humans. Lancet Infect Dis. 2004. [Google Scholar] [Crossref]
5. Kandun IN et al. Human infection with avian influenza A(H5N1) virus. Emerg Infect Dis. 2006. [Google Scholar] [Crossref]
6. Webster RG et al. H5N1 influenza—continuing evolution and spread. Nat Rev Microbiol. 2006. [Google Scholar] [Crossref]
7. Yang W et al. Machine learning for infectious disease surveillance. Lancet Digit Health. 2020. [Google Scholar] [Crossref]
8. Xu B et al. Early detection of epidemics using digital surveillance tools. PLoS One. 2017. [Google Scholar] [Crossref]
9. Hu H et al. AI-based modeling for influenza prediction. Sci Rep. 2019. [Google Scholar] [Crossref]
10. Wong J et al. Biomarker signatures in severe avian influenza. J Clin Invest. 2018. [Google Scholar] [Crossref]
11. Imai M et al. Genetic mutations enabling airborne transmission of avian influenza. Nature. 2012. [Google Scholar] [Crossref]
12. Cowling BJ et al. Gaps in global influenza surveillance. Bull WHO. 2019. [Google Scholar] [Crossref]
13. Schrauwen EJA et al. Adaptive evolution of avian influenza viruses. PLoS Pathog. 2016. [Google Scholar] [Crossref]
14. Herfst S et al. Receptor-binding adaptation in avian influenza. Cell Host Microbe. 2014. [Google Scholar] [Crossref]
15. de Jong MD et al. Fatal avian influenza and cytokine dysregulation. PLoS Med. 2006. [Google Scholar] [Crossref]
16. To KKW et al. Host immunity against emerging influenza viruses. Clin Microbiol Rev. 2015. [Google Scholar] [Crossref]
17. Neumann G et al. Reassortment and evolution of avian influenza. Virology. 2010. [Google Scholar] [Crossref]
18. Yamada S et al. Mutations enhancing mammalian transmissibility. Nature. 2006. [Google Scholar] [Crossref]
19. Gilbert M et al. Ecological drivers of avian influenza spread. Proc Natl Acad Sci USA. 2008. [Google Scholar] [Crossref]
20. Uyeki TM. Human infection susceptibility to avian influenza. J Infect Dis. 2009. [Google Scholar] [Crossref]
21. Sun Y et al. Interferon inhibition by avian influenza. J Virol. 2012. [Google Scholar] [Crossref]
22. Topol EJ. AI in clinical medicine. Nat Med. 2019. [Google Scholar] [Crossref]
23. Chawla NV et al. AI-driven patient risk stratification. Bioinformatics. 2020. [Google Scholar] [Crossref]
24. Li KS et al. Live bird markets and zoonotic spillover. J Virol. 2004. [Google Scholar] [Crossref]
25. Abdel-Ghafar AN et al. Epidemiology of H5N1 in exposed populations. N Engl J Med. 2008. [Google Scholar] [Crossref]
26. Shaman J et al. Environmental persistence of influenza viruses. PLoS Pathog. 2010. [Google Scholar] [Crossref]
27. Hayden FG. Human-to-human transmission of avian influenza. Clin Infect Dis. 2014. [Google Scholar] [Crossref]
28. Lipsitch M et al. Silent transmission dynamics. Proc R Soc B. 2016. [Google Scholar] [Crossref]
29. Chen H et al. Viral adaptation in human airway cells. Nat Commun. 2019. [Google Scholar] [Crossref]
30. Fang LQ et al. Market and mobility effects on transmission. Int J Infect Dis. 2015. [Google Scholar] [Crossref]
31. Santillana M et al. Digital signals for influenza prediction. Proc Natl Acad Sci USA. 2015. [Google Scholar] [Crossref]
32. Balcan D et al. Mobility networks and influenza spread. J R Soc Interface. 2009. [Google Scholar] [Crossref]
33. Bedford T et al. Genomic epidemiology of influenza. eLife. 2015. [Google Scholar] [Crossref]
34. Leal Neto OB et al. Mobile health surveillance in low-resource areas. Trop Med Int Health. 2016 [Google Scholar] [Crossref]
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