A technical development in analyzing seasonal data is also notable. Since the observation of an infected individual is not independent of other infectious individuals, direct application of the generalized linear model has not been appropriate to date. One approach to resolve this issue is to employ a Bayesian method, explicitly dealing with dependence in a Poisson regression model [59] , which is, Interdisciplinary Perspectives on Infectious Diseases however, computationally complicated for nonspecialists. As an alternative, the present study suggested the use of R(t). R(t) reasonably reflects time-dependent changes in the susceptible fraction of the population in question and other various factors determining the transmission (including seasonality) [60, 61] . In particular, it should be noted that R(t) does not reflect onset or death but can represent an infection event with time, proving its potential as a marker to model seasonal and periodic transmission cycles. In addition, quantitative assessment of R(t) is theoretically important, because the amplitude of seasonal forces of infection characterizes the length of the interepidemic period [33, 62, 63] . A continued super-annual cycle mathematically requires seasonally varying forces of infection, which determines the season-specific threshold condition [64] and evolutionary dynamics of a disease [65, 66] . To the best of our knowledge, the present study is the first to suggest a quantitative method to reasonably extract the amplitude using the notation of R(t) and extending the previous efforts of Stallybrass [53] .