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Theoretical Properties of an Adaptive Spatial Hierarchical Bayesian SEIR Model for HIV Transmission Dynamics

Mulati Omukoba Nyukuri John Sirengo Lucy Chikamai

Subject area: Science,Engineering and Technology  ·  Area of research: Statistics

Abstract

This study establishes theoretical foundations of an adaptive spatial hierarchical Bayesian SEIR model for HIV transmission dynamics. We prove existence and uniqueness of solutions, derive equilibrium conditions, analyze stability properties, and establish convergence guarantees. The enhanced SEIR model incorporates spatial heterogeneity through time-varying connectivity weights and employs hierarchical Bayesian methods for robust parameter estimation. Key results include global existence of nonnegative solutions, stability conditions for disease-free equilibrium, derivation of spatial basic reproduction number R? = ?(FV??), and geometric ergodicity of MCMC estimation. Stability analysis demonstrates global asymptotic stability when R? ? 1. These theoretical properties provide rigorous mathematical foundations for HIV control strategies in resource-limited settings.

Keywords

HIV Modeling, SEIR Dynamics, Spatial Epidemiology, Bayesian Inference

References

[1] Anderson, R. M., & May, R. M. (1991). Infectious diseases of humans: dynamics and control. Oxford University Press.

[2] Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2014). Hierarchical modeling and analysis for spatial data (2nd ed.). Chapman and Hall/CRC.

[3] Blangiardo, M., & Cameletti, M. (2015). Spatial and spatio-temporal Bayesian models with R-INLA. Wiley.

[4] Cuadros, D. F., Tanser, F., Venkataramani, A., Vandormael, A., & Bärnighausen, T. (2017). Spatial network connectivity and HIV prevalence in rural KwaZulu-Natal, South Africa. Scientific Reports, 7(1), 9090.

[5] Giorgi, E., Sesay, S. S., Terlouw, D. J., & Diggle, P. J. (2020). Combining data from multiple sources for health surveillance in sub-Saharan Africa. International Statistical Review, 88(2), 462-486.

[6] Lawson, A. B. (2022). Using R for Bayesian spatial and spatio-temporal health modeling. Chapman and Hall/CRC.

[7] Meyer, S., Held, L., & Höhle, M. (2017). Spatio-temporal analysis of epidemic phenomena using the R package surveillance. Journal of Statistical Software, 77(11), 1-55.

[8] National AIDS Control Council (NACC). (2022). Kenya HIV estimates report 2022. Government of Kenya.

[9] Rue, H., Martino, S., & Chopin, N. (2009). Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society: Series B, 71(2), 319-392.

[10] UNAIDS. (2022). Global AIDS update 2022. Joint United Nations Programme on HIV/AIDS.

How to cite this paper

Mulati Omukoba Nyukuri, John Sirengo, Lucy Chikamai "Theoretical Properties of an Adaptive Spatial Hierarchical Bayesian SEIR Model for HIV Transmission Dynamics" Iconic Research And Engineering Journals Volume 9 Issue 1 2025 Page 1134-1137
Mulati Omukoba Nyukuri, John Sirengo, Lucy Chikamai "Theoretical Properties of an Adaptive Spatial Hierarchical Bayesian SEIR Model for HIV Transmission Dynamics" Iconic Research And Engineering Journals, vol. 9, no. 1, Jul. 2025
Mulati Omukoba Nyukuri, John Sirengo, Lucy Chikamai (2025). Theoretical Properties of an Adaptive Spatial Hierarchical Bayesian SEIR Model for HIV Transmission Dynamics. Iconic Research And Engineering Journals, 9(1).
Mulati Omukoba Nyukuri, John Sirengo, Lucy Chikamai "Theoretical Properties of an Adaptive Spatial Hierarchical Bayesian SEIR Model for HIV Transmission Dynamics" Iconic Research And Engineering Journals, vol. 9, no. 1, Jul. 2025.
@article{1709801,
      author = {Mulati Omukoba Nyukuri, John Sirengo, Lucy Chikamai},
      title = {Theoretical Properties of an Adaptive Spatial Hierarchical Bayesian SEIR Model for HIV Transmission Dynamics},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {1},
      pages = {1134-1137},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709801.pdf},
      abstract = {This study establishes theoretical foundations of an adaptive spatial hierarchical Bayesian SEIR model for HIV transmission dynamics. We prove existence and uniqueness of solutions, derive equilibrium conditions, analyze stability properties, and establish convergence guarantees. The enhanced SEIR model incorporates spatial heterogeneity through time-varying connectivity weights and employs hierarchical Bayesian methods for robust parameter estimation. Key results include global existence of nonnegative solutions, stability conditions for disease-free equilibrium, derivation of spatial basic reproduction number R? = ?(FV??), and geometric ergodicity of MCMC estimation. Stability analysis demonstrates global asymptotic stability when R? ? 1. These theoretical properties provide rigorous mathematical foundations for HIV control strategies in resource-limited settings.},
      keywords = {HIV Modeling, SEIR Dynamics, Spatial Epidemiology, Bayesian Inference},
      month = {July},
  }