Sexually transmitted diseases pose a significant threat to the health and economic well-being of society. Hence, it is crucial to predict imminent outbreaks and identify effective interventions through the use of epidemiological tools like compartmental models. Unfortunately, traditional compartmental models, specifically the differential equation models developed by Kermack and McKendrick, require an exponential or Erlang distributed duration of infection, despite their biological invalidity. As a result, alternative approaches are required that capture the variability in the duration of infection, its associated effects on the disease trajectory, and the evaluation of disease interventions. To address this, we apply a novel family of differential equation compartmental models, based on the quantity ``person-days of infection`` that can accommodate non-exponential and non-Erlang distributed durations of infection. As proof of concept, we calibrate an SEAIR analog of our model to recent trends in chlamydia incidence in the United States and evaluate how awareness campaigns alter our model predictions of incidence averted, over a 5-year horizon.