Linking models and data for infectious disease dynamics: rubella as a case-study
Seminar Room 1, Newton Institute
AbstractCo-authors: Bryan Grenfell (EEB, Princeton), Ottar Bjornstad (CIDD, Penn State), Justin Lessler (Bloomberg School of Public Health, Johns Hopkins), Andy Tatem (Geography, Southampton), Amy Wesolowski (Engineering and Public policy, Carnegie Mellon), Caroline Buckee (School of Public Health, Harvard) Following a general preamble on linking models and data from Bryan Grenfell; we move to a specific example with rubella, a directly transmitted and completely immunizing infection. It manifests as a mild disease in children, but infection of women during the first trimester of pregnancy can lead to birth of a child with Congenital Rubella Syndrome (CRS), which can include deafness, blindness and mental retardation. Since vaccination will raise the average age of infection, and thus concentrate infection into women of child bearing age, vaccination short of thresholds required for elimination may increase the burden of CRS. The relatively simple epidemiology of this infection means that generic models linking demography and epidemiology can take us a long way in terms of understanding the minimum levels of vaccination required to ensure a reduction in the CRS burden. However, increasingly resolved data-sets indicate the importance of stochastic dynamics for the burden of this infection. Here, I detail some of these patterns, and then point to areas where novel datasets may be essential to developing our ability to predict the consequences of rubella vaccination, including quantifying spatial heterogeneity in vaccination coverage and understanding human movement.
If it doesn't, something may have gone wrong with our embedded player.
We'll get it fixed as soon as possible.