Formal Stochastic Method Applied to Discrete Modeling for Data Analysis in the Relationship between Notified Cases of Syphilis in Pregnant Women and Congenital in Brazil
Formal Stochastic Method, Discrete Modeling, Congenital Syphilis, Pregnancy Syphilis
Syphilis is a sexually transmitted infection (STI) caused by Treponema pallidum subspecies pallidum. In 2016, it was declared an epidemic in Brazil due to its high morbidity and mortality rates, especially in cases of maternal syphilis (MS) and congenital syphilis (CS) with unfavorable outcomes. The objective of this study was to mathematically describe the relationship between cases of MS and SC reported in Brazil between 2010 and 2020, considering the probability of diagnosis and effective and timely maternal treatment during prenatal care, thus supporting decision-making and coordination of response to syphilis efforts. The model used in this article was based on the Stochastic Petri Net Theory (RPE). Three different regressions, including linear, polynomial, and logistic regression, were used to obtain the weights of an RPE model. To validate the model, we performed 100 independent simulations for each probability of an untreated MS case leading to a CS case (PUMLC) and performed a statistical test to reinforce the results reported here. According to our analysis, the model for predicting cases of congenital syphilis consistently achieved an average accuracy of 93% or better for all tested probabilities of an untreated case of MS leading to a case of CS. The RPE approach proved adequate to explain the Notifiable Disease Information System (SINAN) dataset using the 75-95% range for the probability that an untreated MS case leads to a CS case (PUMLC) . In addition, the predictive power of the model can help plan actions to combat the disease.