Once models had been fitted, we attempted to enhance model parsimony through model reduction, removing terms that had no influence on pneumococcal case counts, in a manner that minimized Akaike's information criterion (AIC), with final models representing those that best balanced parsimony and fit [18] . In models in which viral exposures were evaluated using either total influenza counts or influenza A counts, strong and statistically significant associations were observed between influenza burden, UV index, and relative humidity; we explored multiplicative interaction terms using categorical variables representing the highest quintile ranks of these exposures [19] . As noted above the 2004 and 2009 influenza seasons were outliers with respect to timing and amplitude, and we also performed sensitivity analyses in which models were reconstructed excluding these years.