4.6 Article

Demographic buffering: titrating the effects of birth rate and imperfect immunity on epidemic dynamics

Journal

JOURNAL OF THE ROYAL SOCIETY INTERFACE
Volume 12, Issue 104, Pages -

Publisher

ROYAL SOC
DOI: 10.1098/rsif.2014.1245

Keywords

partial immunity; susceptible-infected-recovered-susceptible; acute infection; demography; birth rate; epidemiology

Funding

  1. US Department of Homeland Security [HSHQDC-12-C-00058]
  2. Bill and Melinda Gates Foundation
  3. EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT [P2CHD047879] Funding Source: NIH RePORTER

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Host demography can alter the dynamics of infectious disease. In the case of perfectly immunizing infections, observations of strong sensitivity to demographic variation have been mechanistically explained through analysis of the susceptible-infected-recovered (SIR) model that assumes lifelong immunity following recovery from infection. When imperfect immunity is incorporated into this framework via the susceptible-infected-recovered-susceptible (SIRS) model, with individuals regaining full susceptibility following recovery, we show that rapid loss of immunity is predicted to buffer populations against the effects of demographic change. However, this buffering is contrary to the dependence on demography recently observed for partially immunizing infections such as rotavirus and respiratory syncytial virus. We show that this discrepancy arises from a key simplification embedded in the SIR(S) framework, namely that the potential for differential immune responses to repeat exposures is ignored. We explore the minimum additional immunological information that must be included to reflect the range of observed dependencies on demography. We show that including partial protection and lower transmission following primary infection is sufficient to capture more realistic reduced levels of buffering, in addition to changes in epidemic timing, across a range of partially and fully immunizing infections. Furthermore, our results identify key variables in this relationship, including R-0.

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