A network suspected infectious disease model for the development of syphilis transmission from 2015 to 2021 in Hubei province, China.

Aims Understanding the transmission mode of syphilis is essential to prevent and predict its future prevalence and to develop effective control measures. This study aimed to develop a network suspected infectious disease model to simulate the syphilis transmission. Methods and results The number of syphilis cases in Wuhan's Fourth Hospital, Hubei province, China, from October 2015 to July 2021 was collected. The simulation was carried out by interpersonal network-SI (suspected infectious) model based on temporal exponential family random graph models. Late latent syphilis and tertiary syphilis are predicted by December 2025. The validity of simulated value and real data was tested, including determination coefficient (R 2), root means square error (RMSE), and means relative error (MRE). Moreover, we developed an online app that can more easily predict the number of syphilis infections in different scenarios by setting different parameters. Results showed that R 2, RMSE, and MRE were 0.995, 36.19, and 6.31, respectively. Speed from latent infection to primary syphilis, primary syphilis to secondary syphilis, and susceptible group to latent infection decreased rapidly. The speed of transformation from secondary syphilis to early incubation period and early latent to late latent experienced a process from increase to decreased. Late latent to tertiary syphilis patients increased steadily. The number of late latent patients, early latent, invisible infection, primary syphilis, and secondary syphilis all increased at first and turn to decreased. However, tertiary syphilis continuously kept rising in the whole process. To better make use of the transmission model, an online application was developed (https://alanwu.shinyapps.io/MD-shiny/). Conclusions Based on the simulation that late latent and tertiary syphilis were steadily increasing, the prevention and treatment for syphilis were imperative. [ABSTRACT FROM AUTHOR]