UCLA

We investigated the extent to which a SEIR compartmental model, two Hawkes point process, each with a different trigger density function, and a recursive point process could characterize the transmission dynamics of invasive Streptococcus pneumoniae. All models were parameterized using surveillance data from Florida between 2010 to 2014. The maximum likelihood estimates of the parameters were calculated, and weekly counts were predicted using a thinning technique for the point processes and adaptive tau-leaping method for the SEIR model. Results suggest that the point processes performed better than the SEIR model. When comparing goodness of fit and prediction errors between the point processes, the recursive point process appeared to perform reasonably well on both. The recursive point process had an RMSE almost as small as the Hawkes with power-law decaying trigger density, which had the lowest RMSE, and the highest log-likelihood of all the models that were evaluated.