| Resumo: |
The Centers for Disease Control (CDC) is currently attempting to eliminate syphilis in the United States (US); to ensure that their control strategies will be effective it is important to understand the transmission dynamics of syphilis. Epidemics of certain infectious diseases (e.g., influenza) can rise and fall with a well-defined periodicity; this cycling behavior is important because it can have significant implications for the design and effectiveness of control strategies. Here we discuss the methodology that has been used to identify epidemic cycles in longitudinal data sets, and the endogenous and exogenous mechanisms that generate cycling. We then examine the recently proposed hypothesis that syphilis epidemics cycle. This hypothesis was proposed based upon the results of a spectral analysis of a longitudinal data set that had been collected by the (CDC), and the analysis of a syphilis transmission model. We use spectral analysis to reanalyze the CDC’s data set, as well as to analyze a longitudinal mortality data set provided by the CDC. We also use published transmission models to predict the expected dynamics of syphilis epidemics. In contrast to the previous findings we find that: (i) that neither of the CDC’s data sets provide compelling evidence that syphilis epidemics cycle and (ii) published transmission models predict that syphilis epidemics should monotonically decrease (as a function of the treatment rate) rather than cycle. We explain the reasons why previous authors had proposed that syphilis epidemics cycle. Finally, we discuss the implications of our findings regarding the transmission dynamics of syphilis for the CDC’s elimination plan.
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