Data from: Characterizing and comparing the seasonality of influenza-like illnesses and invasive pneumococcal diseases using seasonal waveforms

The seasonalities of influenza-like illnesses (ILIs) and invasive
pneumococcal diseases (IPDs) remain incompletely understood. Experimental
evidence indicates that influenza-virus infection predisposes to
pneumococcal disease, so that a correspondence in the seasonal patterns of
ILIs and IPDs might exist at the population level. We developed a method
to characterize seasonality by means of easily interpretable summary
statistics of seasonal shape—or seasonal waveforms. Non-linear
mixed-effects models were used to estimate those waveforms based on weekly
case reports of ILIs and IPDs in five regions spanning continental France
from July 2000 to June 2014. We found high variability of ILI seasonality,
with marked fluctuations of peak amplitudes and peak times, but a more
conserved epidemic duration. In contrast, IPD seasonality was best modeled
by a markedly regular seasonal baseline, punctuated by two winter peaks in
late December–early January and January–February. Comparing ILI and IPD
seasonal waveforms, we found indication of a small, positive correlation.
Direct models regressing IPDs on ILIs provided comparable results, even
though they estimated moderately larger associations. The method proposed
is broadly applicable to diseases with unambiguous seasonality and is
well-suited to analyze spatially or temporally grouped data, which are
common in epidemiology.