Modelling norovirus transmission and vaccination

Norovirus is thought to be responsible for a fifth of all acute gastroenteritis cases globally each year. The population level transmission dynamics of this very common virus are still poorly understood, in part because illness is under-reported. With vaccines undergoing clinical trials, there is a growing need for appropriate, empirically grounded models, to predict the likely impact of vaccination.

We developed a dynamic age-specific mathematical model of norovirus transmission and vaccination, informed by available data, particularly age-stratified time series case notification data. We introduce the use of a self-reporting Markov model to account for variation by age and over time in the statutory reporting of norovirus in Germany. We estimated the model using a sequential Monte Carlo particle filter. We then extended and applied our estimated model to investigate the potential impact of a range of immunisation strategies. We performed sensitivity analyses on the mode of vaccine action and other vaccine-related parameters.

We find that routine immunisation could reduce the incidence of norovirus by up to 70.5% even when those vaccines do not provide complete protection from disease. Furthermore, we find that the relative efficiency of alternative strategies targeting different age groups are dependant on the outcome we consider and are sensitive to assumptions on the mode of vaccine action. Strategies that target infants and toddler are more efficient in preventing infection but targeting older adults is preferable for preventing severe outcomes.

Our model provides a robust estimate of a dynamic transmission model for norovirus at the population level. Vaccination may be an effective strategy in preventing disease but further work is required to ascertain norovirus vaccine efficacy, its mode of action and to estimate the cost-effectiveness of immunisation against norovirus.