We use epidemiological modelling and a large case incidence dataset to explain the upsurge.

We investigate several hypotheses for the upsurge in pertussis cases by fitting a suite of dynamic epidemiological models to incidence data from the National Notifiable Disease Surveillance System (NNDSS) between 1990—2009, as well as incidence data from a variety of sources from 1950—1989.

We investigate several hypotheses for the upsurge in pertussis cases by fitting a suite of epidemiological models to incidence data from the National Notifiable Disease Surveillance System (NNDSS) between 1990—2009.

Since the pertussis cases counted by NNDSS may be a proportion of the true number (and the output of the natural history epidemiological model is designed to simulate the true number) we can say that the observed number of cases follows an observation model so that annual counts of pertussis cases (0195,- (t)) are governed by a binomial distribution:

Epidemiological model diagram.

Uncertainty about parameters will be an issue for most epidemiological models , and we propose that multi-model ensembles should incorporate projections with different models and different parameterizations.

Yet, data quality will rarely be comparable to climate data, which highlights one of the major challenges for epidemiological modeling .

We argue that the framework we introduce here has great potential, and foresee that many of the questions addressed in epidemiological modeling would require further developments of the Bayesian model, structured to fit with the specific problem.

The predictive focus of epidemiological models can either be classified as forecasting or projecting [13].

These studies all used a single disease model projection, coupled to an ensemble of climate or weather forecasts and the use of structurally different epidemiological models are to our knowledge still rare.

localised culling) using a spatially-explicit, stochastic epidemiological model .

Default parameterisation of the underlying epidemiological model targets the spread of citrus canker in Florida by adapting the parameters of previous modelling studies, although these defaults can readily be altered to represent other model parameterisations or even patho-systems by the user of our front-end interface.

These data have allowed parsimonious, stochastic, spatially-explicit, epidemiological models to be fitted to the spread of citrus canker that track the disease status of individual host plants [11,12,24,25], and here we use a flexible extension of these models to analyse the effectiveness of the control scenarios we consider.

A principal challenge in using epidemiological models to inform policy is rendering the assumptions and outputs of models in forms that can be readily understood and interrogated by policy makers [27].