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Seasonal influenza : modelling approaches to capture immunity propagation
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Hill, Edward M., Petrou, Stavros, de Lusignan, Simon, Yonova, Ivelina and Keeling, Matt J. (2019) Seasonal influenza : modelling approaches to capture immunity propagation. PLoS Computational Biology, 15 (10). e1007096. doi:10.1371/journal.pcbi.1007096
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Official URL: http://dx.doi.org/10.1371/journal.pcbi.1007096
Abstract
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.
| Item Type: | Journal Article | ||||||||||||
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| Subjects: | R Medicine > RA Public aspects of medicine R Medicine > RC Internal medicine |
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| Divisions: | Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Clinical Trials Unit Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Health Sciences Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) Faculty of Science, Engineering and Medicine > Science > Mathematics Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School |
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| Library of Congress Subject Headings (LCSH): | Influenza, Influenza -- Prevention, Influenza vaccines | ||||||||||||
| Journal or Publication Title: | PLoS Computational Biology | ||||||||||||
| Publisher: | Public Library of Science | ||||||||||||
| ISSN: | 1553-7358 | ||||||||||||
| Official Date: | 28 October 2019 | ||||||||||||
| Dates: |
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| Volume: | 15 | ||||||||||||
| Number: | 10 | ||||||||||||
| Article Number: | e1007096 | ||||||||||||
| DOI: | 10.1371/journal.pcbi.1007096 | ||||||||||||
| Status: | Peer Reviewed | ||||||||||||
| Publication Status: | Published | ||||||||||||
| Reuse Statement (publisher, data, author rights): | This report is independent research funded by the National Institute for Health Research (NIHR) (Policy Research Programme, Infectious Disease Dynamic Modelling in Health Protection, 027/0089). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. | ||||||||||||
| Access rights to Published version: | Open Access | ||||||||||||
| RIOXX Funder/Project Grant: |
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