Banca de QUALIFICAÇÃO: RODRIGO DANTAS DA SILVA

Uma banca de QUALIFICAÇÃO de DOUTORADO foi cadastrada pelo programa.
STUDENT : RODRIGO DANTAS DA SILVA
DATE: 26/01/2023
TIME: 09:00
LOCAL: Laboratório de Inovação Tecnológica em Saúde (LAIS)
TITLE:

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

KEY WORDS:

Formal Stochastic Method, Discrete Modeling, Congenital Syphilis, Pregnancy Syphilis

PAGES: 15
BIG AREA: Engenharias
AREA: Engenharia Biomédica
SUMMARY:

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.

COMMITTEE MEMBERS:
Presidente - 2488270 - RICARDO ALEXSANDRO DE MEDEIROS VALENTIM
Interno - 2885532 - IVANOVITCH MEDEIROS DANTAS DA SILVA
Externa ao Programa - ***.555.724-** - THAISA GOIS FARIAS DE MOURA SANTOS LIMA - MS
Externo à Instituição - ANTONIO HIGOR FREIRE DE MORAIS - IFRN
Externo à Instituição - JAILTON CARLOS DE PAIVA - IFRN