A Bayesian stochastic SIRS model with a vaccination strategy for the analysis of respiratory syncytial virus

  1. Marc Jornet-Sanz 1
  2. A. Corberán-Vallet 1
  3. F.J. Santonja 1
  4. R.J. Villanueva 2
  1. 1 Department of Statistics and Operational Research. Universitat de València
  2. 2 Institute for Multidisciplinary Mathematics. Universitat Politecnica de València
Revista:
Sort: Statistics and Operations Research Transactions

ISSN: 1696-2281

Año de publicación: 2017

Volumen: 41

Número: 1

Páginas: 159-176

Tipo: Artículo

DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Sort: Statistics and Operations Research Transactions

Resumen

Our objective in this paper is to model the dynamics of respiratory syncytial virus in the region of Valencia (Spain) and analyse the effect of vaccination strategies from a health-economic point of view. Compartmental mathematical models based on differential equations are commonly used in epidemiology to both understand the underlying mechanisms that influence disease transmission and analyse the impact of vaccination programs. However, a recently proposed Bayesian stochastic susceptible-infected-recovered-susceptible model in discrete-time provided an improved and more natural description of disease dynamics. In this work, we propose an extension of that stochastic model that allows us to simulate and assess the effect of a vaccination strategy that consists on vaccinating a proportion of newborns.

Referencias bibliográficas

  • Acedo, L., Dı́ez-Domingo, J., Moraño, J.A. and Villanueva, R.J. (2010). Mathematical modelling of respiratory syncytial virus (RSV): vaccination strategies and budget applications. Epidemiology and Infection, 138, 853–860.
  • Allen, L.J. (2008). An introduction to stochastic epidemic models. In Brauer, F., van den Driessche, P. and Wu, J. (Eds.) Mathematical Epidemiology. Springer, Berlin.
  • Anderson, L.J., Dormitzer, P.R., Nokes, D.J. Rappuoli, R. Roca, A. and Graham, B.S. (2013). Strategic priorities for respiratory syncytial virus (RSV) vaccine development. Vaccine, 31S, B209–B215.
  • Beeler, J.A. and Eichelberger, M.C. (2013). Influenza and respiratory syncytial virus (RSV) vaccines for infants: safety, immunogenicity, and efficacy. Microbial Pathogenesis, 55, 9–15.
  • Bjørnstad, O.T., Finkenstädt, B.F. and Grenfell, B.T. (2002). Dynamics of measles epidemics: estimating scaling of transmission rates using a time series SIR model. Ecological Monographs, 72, 169–184.
  • Boys, R.J. and Giles, P.R. (2007). Bayesian inference for stochastic epidemic models with time-inhomogeneous removal rates. Journal of Mathematical Biology, 55, 223–247.
  • Brauer, F. (2008). Compartmental models in epidemiology. In Brauer, F., van den Driessche, P. and Wu, J. (Eds.) Mathematical Epidemiology. Springer, Berlin.
  • Christensen, H., Hickmana, M., Edmunds, W.J. and Trotter, C.L. (2013). Introducing vaccination against serogroup B meningococcal disease: an economic and mathematical modelling study of potential impact. Vaccine, 31, 2638–2646.
  • Contreras, J.R. Vacuna contra el Virus Respiratorio Sincitial (VRS). Asociación Española de Vacunologı́a. Informe Técnico http://www.vacunas.org/vacuna-contra-el-virus-respiratorio-sincitialvrs/ [In Spanish. Accessed in: 26th July 2016].
  • Corberán-Vallet, A. and Santonja, F.J. (2014). A Bayesian SIRS model for the analysis of respiratory syncytial virus in the region of Valencia, Spain. Biometrical Journal, 56, 808–818.
  • Craig, A.P., Hanger, J., Loader, J., Ellis, W.A.H., Callaghan, J., Dexter, A., Jones, D., Beagley, K.W., Timms, P. and Wilson, D.P. (2014). A 5-year Chlamydia vaccination programme could reverse disease-related koala population decline: predictions from a mathematical model using field data. Vaccine, 32, 4163–4170.
  • Dudas, R.A. and Karron, R.A. (1998). Respiratory syncytial virus vaccines. Clinical Microbiology Reviews, 11, 430–439.
  • Eilers, P.H.C., Marx, B.D. and Durban, M. (2015). Twenty years of P-splines. SORT-Statistics and Operations Research Transactions, 39, 149–186.
  • Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models. Bayesian Analysis, 1, 515–533.
  • Gibson, G.J. and Renshaw, E. (1998). Estimating parameters in stochastic compartmental models using Markov chain methods. IMA Journal of Mathematics Applied in Medicine and Biology, 15, 19–40.
  • Higgings, D., Trujillo, C. and Keech, C. (2016). Advances in RSV vaccine research and development a global agenda. Vaccine, 34, 2870–2875.
  • Hogan, A.B., Glass, K., Moore, H.C. and Anderssen, R.S. (2016). Exploring the dynamics of respiratory syncytial virus (RSV) transmission in children. Theoretical Population Biology, 110, 78–85.
  • Jones, B.G., Sealy, R.E., Surman, S.L., Portner, A., Rusell, C.J., Slobod, K.S., Dormitzer, P.R., DeVincenzo, J. and Hurwitz, J.L. (2014) Sendai virus-based RSV vaccine protects against RSV challenge in an in vivo maternal antibody model. Vaccine, 32, 3264–3273.
  • Lunn, D.J., Thomas, A., Best, N. and Spiegelhalter, D. (2000). WinBUGS — a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing, 10, 325–337.
  • Ma, Z. and Li, J. (2009). Dynamical Modelling and Analysis of Epidemics. World Scientific, London.
  • O’Neill, P.D. (2002). A tutorial introduction to Bayesian inference for stochastic epidemic models using Markov chain Monte Carlo methods. Mathematical Biosciences, 180, 103–114.
  • Parra, A., Jiménez, C., Hernández, S., Garcı́a J.E. and Cardona, A.M. (2013). Bronquiolitis: artı́culo de revisión. Neumologı́a Pediátrica, 8, 95–101.
  • Pérez-Breva, L., Villanueva R.J., Villanueva-Oller, J., Acedo L., Santonja F.J., et al. (2014). Optimizing strategies for meningococcal C disease vaccination in Valencia (Spain). BMC Infectious Diseases, 14, 280.
  • Poletti, P., Merler, S., Ajelli, M., Manfredi, P., Munywoki, P.K., Nokes, D.J. and Melegaro, A. (2015). Evaluating vaccination strategies for reducing infant respiratory syncytial virus infection in lowincome settings. BMC Medicine, 13–49.
  • R Development Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, http://www.R-project.org.
  • Rao, Y. and McCabe, B. (2016). Real-time surveillance for abnormal events: the case of influenza outbreaks. Statistics in Medicine, 35, 2206–2220.
  • Roberts, J.N., Graham, B.S., Karron, R.A., Munoz, F.M., Falsey, A.R., Anderson, L.J., Marshall, V. Kim, S. and Beeler, J.A. (2016). Challenges and opportunities in RSV vaccine development: meeting report from FDA/NIH workshop. Vaccine, 34, 4843–4849.
  • Soetaert, K., Petzoldt, T. and Setzer, R.W. (2010). Solving differential equations in R: Package deSolve. Journal of Statistical Software, 33, 1–25.
  • Van Hoek, A.J., Melegarob, A., Zaghenid, E., Edmundsa, W.J. and Gayb, N. (2011). Modelling the impact of a combined varicella and zoster vaccination programme on the epidemiology of varicella zoster virus in England. Vaccine, 29, 2411–2420.
  • Vannia, T., Mendes, P., Fossa, A., Mesa-Friasa, M. and Legooda, R. (2012). Economic modelling assessment of the HPV quadrivalent vaccine in Brazil: a dynamic individual-based approach. Vaccine, 30, 4866–4871.
  • Weidemann, F., Dehnert, M., Koch, J., Wichmann, O. and Höhle, M. (2014). Modelling the epidemiological impact of rotavirus vaccination in Germany – A Bayesian approach. Vaccine, 32, 5250–5257.
  • World Health Organization. Using the Results of Modelling to Inform WHO Guidelines. http://www.who. int/kms/modelling_whoguidelines_2016/en/
  • Yu, Z., Liu, J., Wang, X., Zhu, X., Wang, D. and Han, G. (2016). Efficient vaccine distribution based on a hybrid compartmental model. PLoS One, 11, e0155416.
Fuente de los datos: Dialnet