Stochastic Petri net model describing the relationship between reported maternal and congenital syphilis cases in Brazil
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 unfavorab...
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2022-02-01
|
| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-022-01773-1 |
| _version_ | 1819279523410083840 |
|---|---|
| author | Ricardo A. M. Valentim Gleyson J. P. Caldeira-Silva Rodrigo D. da Silva Gabriela A. Albuquerque Ion G. M. de Andrade Ana Isabela L. Sales-Moioli Talita K. de B. Pinto Angélica E. Miranda Leonardo J. Galvão-Lima Agnaldo S. Cruz Daniele M. S. Barros Anna Giselle C. D. R. Rodrigues |
| author_facet | Ricardo A. M. Valentim Gleyson J. P. Caldeira-Silva Rodrigo D. da Silva Gabriela A. Albuquerque Ion G. M. de Andrade Ana Isabela L. Sales-Moioli Talita K. de B. Pinto Angélica E. Miranda Leonardo J. Galvão-Lima Agnaldo S. Cruz Daniele M. S. Barros Anna Giselle C. D. R. Rodrigues |
| author_sort | Ricardo A. M. Valentim |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-24T00:29:15Z |
| format | Article |
| id | doaj.art-3d051e4598fc472e877520a0c19ada1e |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-12-24T00:29:15Z |
| publishDate | 2022-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-3d051e4598fc472e877520a0c19ada1e2022-12-21T17:24:20ZengBMCBMC Medical Informatics and Decision Making1472-69472022-02-0122111210.1186/s12911-022-01773-1Stochastic Petri net model describing the relationship between reported maternal and congenital syphilis cases in BrazilRicardo A. M. Valentim0Gleyson J. P. Caldeira-Silva1Rodrigo D. da Silva2Gabriela A. Albuquerque3Ion G. M. de Andrade4Ana Isabela L. Sales-Moioli5Talita K. de B. Pinto6Angélica E. Miranda7Leonardo J. Galvão-Lima8Agnaldo S. Cruz9Daniele M. S. Barros10Anna Giselle C. D. R. Rodrigues11Laboratory of Technological Innovation in Health, Federal University of Rio Grande do NorteLaboratory of Technological Innovation in Health, Federal University of Rio Grande do NorteLaboratory of Technological Innovation in Health, Federal University of Rio Grande do NorteLaboratory of Technological Innovation in Health, Federal University of Rio Grande do NorteLaboratory of Technological Innovation in Health, Federal University of Rio Grande do NorteLaboratory of Technological Innovation in Health, Federal University of Rio Grande do NorteLaboratory of Technological Innovation in Health, Federal University of Rio Grande do NortePostgraduate Program in Infectious Diseases, Federal University of Espírito SantoLaboratory of Technological Innovation in Health, Federal University of Rio Grande do NorteLaboratory of Technological Innovation in Health, Federal University of Rio Grande do NorteLaboratory of Technological Innovation in Health, Federal University of Rio Grande do NorteDigital Metrópole Institute, Federal University of Rio Grande do NorteAbstract 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.https://doi.org/10.1186/s12911-022-01773-1Stochastic Petri netCongenital syphilisMaternal syphilis |
| spellingShingle | Ricardo A. M. Valentim Gleyson J. P. Caldeira-Silva Rodrigo D. da Silva Gabriela A. Albuquerque Ion G. M. de Andrade Ana Isabela L. Sales-Moioli Talita K. de B. Pinto Angélica E. Miranda Leonardo J. Galvão-Lima Agnaldo S. Cruz Daniele M. S. Barros Anna Giselle C. D. R. Rodrigues Stochastic Petri net model describing the relationship between reported maternal and congenital syphilis cases in Brazil BMC Medical Informatics and Decision Making Stochastic Petri net Congenital syphilis Maternal syphilis |
| title | Stochastic Petri net model describing the relationship between reported maternal and congenital syphilis cases in Brazil |
| title_full | Stochastic Petri net model describing the relationship between reported maternal and congenital syphilis cases in Brazil |
| title_fullStr | Stochastic Petri net model describing the relationship between reported maternal and congenital syphilis cases in Brazil |
| title_full_unstemmed | Stochastic Petri net model describing the relationship between reported maternal and congenital syphilis cases in Brazil |
| title_short | Stochastic Petri net model describing the relationship between reported maternal and congenital syphilis cases in Brazil |
| title_sort | stochastic petri net model describing the relationship between reported maternal and congenital syphilis cases in brazil |
| topic | Stochastic Petri net Congenital syphilis Maternal syphilis |
| url | https://doi.org/10.1186/s12911-022-01773-1 |
| work_keys_str_mv | AT ricardoamvalentim stochasticpetrinetmodeldescribingtherelationshipbetweenreportedmaternalandcongenitalsyphiliscasesinbrazil AT gleysonjpcaldeirasilva stochasticpetrinetmodeldescribingtherelationshipbetweenreportedmaternalandcongenitalsyphiliscasesinbrazil AT rodrigoddasilva stochasticpetrinetmodeldescribingtherelationshipbetweenreportedmaternalandcongenitalsyphiliscasesinbrazil AT gabrielaaalbuquerque stochasticpetrinetmodeldescribingtherelationshipbetweenreportedmaternalandcongenitalsyphiliscasesinbrazil AT iongmdeandrade stochasticpetrinetmodeldescribingtherelationshipbetweenreportedmaternalandcongenitalsyphiliscasesinbrazil AT anaisabelalsalesmoioli stochasticpetrinetmodeldescribingtherelationshipbetweenreportedmaternalandcongenitalsyphiliscasesinbrazil AT talitakdebpinto stochasticpetrinetmodeldescribingtherelationshipbetweenreportedmaternalandcongenitalsyphiliscasesinbrazil AT angelicaemiranda stochasticpetrinetmodeldescribingtherelationshipbetweenreportedmaternalandcongenitalsyphiliscasesinbrazil AT leonardojgalvaolima stochasticpetrinetmodeldescribingtherelationshipbetweenreportedmaternalandcongenitalsyphiliscasesinbrazil AT agnaldoscruz stochasticpetrinetmodeldescribingtherelationshipbetweenreportedmaternalandcongenitalsyphiliscasesinbrazil AT danielemsbarros stochasticpetrinetmodeldescribingtherelationshipbetweenreportedmaternalandcongenitalsyphiliscasesinbrazil AT annagisellecdrrodrigues stochasticpetrinetmodeldescribingtherelationshipbetweenreportedmaternalandcongenitalsyphiliscasesinbrazil |