Valentim RAM, Caldeira-Silva GJP, da Silva RD, Albuquerque GA, de Andrade IGM, Sales-Moioli AIL, Pinto TKDB, Miranda AE, Galvão-Lima LJ, Cruz AS, Barros DMS, Rodrigues AGCDR. Stochastic Petri net model describing the relationship between reported maternal and congenital syphilis cases in Brazil.
BMC Med Inform Decis Mak 2022;
22:40. [PMID:
35168629 PMCID:
PMC8845404 DOI:
10.1186/s12911-022-01773-1]
[Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 02/02/2022] [Indexed: 12/27/2022] Open
Abstract
INTRODUCTION
Syphilis is a sexually transmitted disease (STD) caused by Treponema pallidum subspecies pallidum. In 2016, it was declared an epidemic in Brazil due to its high morbidity and mortality rates, mainly in cases of maternal syphilis (MS) and congenital syphilis (CS) with unfavorable outcomes. This paper aimed to mathematically describe the relationship between MS and CS cases reported in Brazil over the interval from 2010 to 2020, considering the likelihood of diagnosis and effective and timely maternal treatment during prenatal care, thus supporting the decision-making and coordination of syphilis response efforts.
METHODS
The model used in this paper was based on stochastic Petri net (SPN) theory. Three different regressions, including linear, polynomial, and logistic regression, were used to obtain the weights of an SPN model. To validate the model, we ran 100 independent simulations for each probability of an untreated MS case leading to CS case (PUMLC) and performed a statistical t-test to reinforce the results reported herein.
RESULTS
According to our analysis, the model for predicting congenital syphilis cases consistently achieved an average accuracy of 93% or more for all tested probabilities of an untreated MS case leading to CS case.
CONCLUSIONS
The SPN approach proved to be suitable for explaining the Notifiable Diseases Information System (SINAN) dataset using the range of 75-95% for the probability of an untreated MS case leading to a CS case (PUMLC). In addition, the model's predictive power can help plan actions to fight against the disease.
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