Error model

[seabbs]

Currently, a negative binomial error model is used with a single overdispersion parameter. This is problematic because data with little truncation (and hence well known) is treated equally with data that is highly truncated (and hence poorly known). To some degree this uncertainty is modelled in the truncation distribution but having a single overdispersion parameter does allow additional model flexibility for which there is not much justification.

A potential option, that has been partially explored with quite poor results, would be to have an overdispersion parameter for each day of truncation. Alternatively, the simplest solution may be to assume a Poisson error model.

The effect of the single overdispersion can be seen in the posterior prediction plots where the model is quite uncertain about data with very little truncation even though these data should be very well known.

[seabbs]

Resolved by changing the form of the input data to be by reference date with counts by date of report.