tetano
Editor, Senior Moderator
PLoS Comput Biol. 2019 Oct 28;15(10):e1007096. doi: 10.1371/journal.pcbi.1007096. [Epub ahead of print] [h=1]Seasonal influenza: Modelling approaches to capture immunity propagation.[/h]
Hill EM[SUP]1,[/SUP][SUP]2[/SUP], Petrou S[SUP]3,[/SUP][SUP]4[/SUP], de Lusignan S[SUP]4,[/SUP][SUP]5[/SUP], Yonova I[SUP]5,[/SUP][SUP]6[/SUP], Keeling MJ[SUP]1,[/SUP][SUP]2,[/SUP][SUP]7[/SUP].
[h=3]Author information[/h] 1 Zeeman Institute: Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, United Kingdom. 2 Mathematics Institute, University of Warwick, Coventry, United Kingdom. 3 Warwick Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, United Kingdom. 4 Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom. 5 Royal College of General Practitioners, London, United Kingdom. 6 Department of Clinical and Experimental Medicine, University of Surrey, Guildford, United Kingdom. 7 School of Life Sciences, University of Warwick, Coventry, United Kingdom.
[h=3]Abstract[/h] Seasonal influenza poses serious problems for global public health, being a significant contributor to morbidity and mortality. In England, there has been a long-standing national vaccination programme, with vaccination of at-risk groups and children offering partial protection against infection. Transmission models have been a fundamental component of analysis, informing the efficient use of limited resources. However, these models generally treat each season and each strain circulating within that season in isolation. Here, we amalgamate multiple data sources to calibrate a susceptible-latent-infected-recovered type transmission model for seasonal influenza, incorporating the four main strains and mechanisms linking prior season epidemiological outcomes to immunity at the beginning of the following season. Data pertaining to nine influenza seasons, starting with the 2009/10 season, informed our estimates for epidemiological processes, virological sample positivity, vaccine uptake and efficacy attributes, and general practitioner influenza-like-illness consultations as reported by the Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC). We performed parameter inference via approximate Bayesian computation to assess strain transmissibility, dependence of present season influenza immunity on prior protection, and variability in the influenza case ascertainment across seasons. This produced reasonable agreement between model and data on the annual strain composition. Parameter fits indicated that the propagation of immunity from one season to the next is weaker if vaccine derived, compared to natural immunity from infection. Projecting the dynamics forward in time suggests that while historic immunity plays an important role in determining annual strain composition, the variability in vaccine efficacy hampers our ability to make long-term predictions.
PMID: 31658250 DOI: 10.1371/journal.pcbi.1007096
Hill EM[SUP]1,[/SUP][SUP]2[/SUP], Petrou S[SUP]3,[/SUP][SUP]4[/SUP], de Lusignan S[SUP]4,[/SUP][SUP]5[/SUP], Yonova I[SUP]5,[/SUP][SUP]6[/SUP], Keeling MJ[SUP]1,[/SUP][SUP]2,[/SUP][SUP]7[/SUP].
[h=3]Author information[/h] 1 Zeeman Institute: Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, United Kingdom. 2 Mathematics Institute, University of Warwick, Coventry, United Kingdom. 3 Warwick Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, United Kingdom. 4 Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom. 5 Royal College of General Practitioners, London, United Kingdom. 6 Department of Clinical and Experimental Medicine, University of Surrey, Guildford, United Kingdom. 7 School of Life Sciences, University of Warwick, Coventry, United Kingdom.
[h=3]Abstract[/h] Seasonal influenza poses serious problems for global public health, being a significant contributor to morbidity and mortality. In England, there has been a long-standing national vaccination programme, with vaccination of at-risk groups and children offering partial protection against infection. Transmission models have been a fundamental component of analysis, informing the efficient use of limited resources. However, these models generally treat each season and each strain circulating within that season in isolation. Here, we amalgamate multiple data sources to calibrate a susceptible-latent-infected-recovered type transmission model for seasonal influenza, incorporating the four main strains and mechanisms linking prior season epidemiological outcomes to immunity at the beginning of the following season. Data pertaining to nine influenza seasons, starting with the 2009/10 season, informed our estimates for epidemiological processes, virological sample positivity, vaccine uptake and efficacy attributes, and general practitioner influenza-like-illness consultations as reported by the Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC). We performed parameter inference via approximate Bayesian computation to assess strain transmissibility, dependence of present season influenza immunity on prior protection, and variability in the influenza case ascertainment across seasons. This produced reasonable agreement between model and data on the annual strain composition. Parameter fits indicated that the propagation of immunity from one season to the next is weaker if vaccine derived, compared to natural immunity from infection. Projecting the dynamics forward in time suggests that while historic immunity plays an important role in determining annual strain composition, the variability in vaccine efficacy hampers our ability to make long-term predictions.
PMID: 31658250 DOI: 10.1371/journal.pcbi.1007096