Partnership dynamics in mathematical models and implications for representation of sexually transmitted infections: a review

Rao, Darcy White; Wheatley, Margo M; Goodreau, Steven M.; Enns, Eva A.

doi:10.1016/j.annepidem.2021.04.012

Cited by 6 publications

(6 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, due to limited availability of partner race/ethnicity data, we analyzed racial/ethnic homophily among up to three current partnerships only, which may not be representative of all partnerships, particularly those of short duration. We were also unable to examine trends in age mixing because partner age was not available in the public dataset after 2015 or in mixing by sexual activity, which was not assessed, despite their importance to STI transmission (Easterly et al, 2018 ; Newman, 2002 ; Rao et al, 2021 ). More generally, the way partners connect has been changing rapidly (Anderson et al, 2020 ; Rosenfeld et al, 2019 ), which could have significant implications for the sexual network’s structure and epidemic potential, and these data were collected prior to the COVID-19 pandemic, which impacted sexual behaviors, networks, and STI screening (Masoudi et al, 2022 ; Mourikis et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

“…However, except Ueda et al ( 2020 ), these analyses of sexual behavior did not cover the more recent period from 2015 to 2019 during which gonorrhea in particular has been rising rapidly (Centers for Disease Control & Prevention, 2021 ), and most are missing behaviors such as frequency of sex or sexual network attributes, defined as the behaviors and partnership-level characteristics that contribute to linkages among people in sexual networks (Doherty et al, 2005 ; Weiss et al, 2020 ). Network attributes, for example concurrency and racial/ethnic mixing, influence the size, speed, and populations impacted in STI epidemics (Rao et al, 2021 ). Despite STI diagnosis rates increasing among 30–44-year-old women and 15–44-year-old males (Centers for Disease Control & Prevention, 2021 ), studies of STI outcomes have generally focused on women under 30.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Changes in Sexual Behaviors with Opposite-Sex Partners and Sexually Transmitted Infection Outcomes Among Females and Males Ages 15–44 Years in the USA: National Survey of Family Growth, 2008–2019

et al. 2022

Self Cite

View full text Add to dashboard Cite

Rates of reported gonorrhea and chlamydial infections have increased substantially over the past decade in the USA and disparities persist across age and race/ethnicity. We aimed to understand potential changes in sexual behaviors, sexual network attributes, and sexually transmitted infection (STI) screening that may be contributing to these trends. We analyzed data from 29,423 female and 24,605 male respondents ages 15–44 years from the National Survey of Family Growth, 2008–2019. We used survey-weighted linear or logistic regression to evaluate linear temporal trends in sexual behaviors with opposite-sex partners, network attributes, and STI testing, treatment, and diagnosis. Significant declines were observed in condom use at last vaginal sex, mean number of vaginal sex acts, proportion of condom-protected sex acts in the past 4 weeks, and racial/ethnic homophily with current partners among males and females from 2008–2010 through 2017–2019. Among males, mean number of female partners in the past 12 months and concurrency also declined, while the percent reporting ever having sex with another male increased. Past-year testing for chlamydia and any STI increased among females. Research is needed to understand how these changes interact and potentially contribute to increasing reported gonorrhea and chlamydia diagnoses and identify avenues for future intervention.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Changes in Sexual Behaviors with Opposite-Sex Partners and Sexually Transmitted Infection Outcomes Among Females and Males Ages 15–44 Years in the USA: National Survey of Family Growth, 2008–2019

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Luo et al (2018). Various other computational modelling approaches have been proposed to represent sexual partnership dynamics and their impact on the transmission of STIs, see Rao et al (2021) and the references within.…”

Section: 3mentioning

confidence: 99%

Agent-Based Modelling of Mpox Infection Dynamics: Simulating Disease Transmission and Control Strategies

Ajmal,

Hunter,

Duggan

et al. 2024

JASSS

View full text Add to dashboard Cite

Mpox is a viral disease caused by the monkey pox virus (MPXV) of the Poxviridae family. It is similar to smallpox and cowpox. Mpox is not categorized as a sexually transmitted infection, however, most transmission in the recent global outbreak has been attributed to be through men who have sex with men (MSM). Agentbased modelling (ABM) is an effective approach to simulate the spread of Mpox because it enables the detailed representation of heterogeneity among individuals or sub-populations, including variations in behaviour, susceptibility to disease, and contact patterns. This is crucial for accurately modelling diseases in populations with long tailed distributions of characteristics such as sexual activity, that has been seen in the recent Mpox outbreak. Two ABMs are presented in this paper which simulate the spread of Mpox in an artificial population. The parameters for contact formation and dissolution are drawn from the NATSAL-3 dataset (National Survey of Sexual Attitudes and Lifestyles) (Mercer et al. 2013). The presented ABMs are based on the principles of the classic compartmental Susceptible-Exposed-Infected-Recovered (SEIR) model and complexity is further added to break the assumptions of homogeneous mixing and populations. The first ABM is based on an heterogeneous population, consisting of people from two self-identified genders (male and female) and three sexual preferences: homosexual, bi-sexual and heterosexual. The second ABM is a subset of the first ABM, that focuses on the MSM population, which is considered to be at high-risk of Mpox. We report a set of experiments modelling relevant scenarios by number of imported cases, number of super-spreader events, and different vaccination strategies. Our analysis concludes that Mpox infection reaches locally stable disease-free equilibrium in the absence of concurrent partnerships. Our simulations demonstrate that prioritizing vaccines for the MSM population can significantly reduce the effective R value from 1.62 to 0.19, highlighting the potential impact of targeted vaccination strategies in mitigating Mpox transmission.

show abstract

“…Mathematical models are valuable tools for infectious disease research [ 20 ], and one of their most important applications is predicting disease occurrence. With the development of different mathematical models postoperative infection in elderly spinal fractures [ 21 ], high risk types of human papillomaviruse infection [ 22 ] and disease outcome of septic shock [ 23 ] can be predicted.…”

Section: Introductionmentioning

confidence: 99%

Development of a predictive machine learning model for pathogen profiles in patients with secondary immunodeficiency

Liu,

Chen,

Xie

et al. 2024

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

Background Secondary immunodeficiency can arise from various clinical conditions that include HIV infection, chronic diseases, malignancy and long-term use of immunosuppressives, which makes the suffering patients susceptible to all types of pathogenic infections. Other than HIV infection, the possible pathogen profiles in other aetiology-induced secondary immunodeficiency are largely unknown. Methods Medical records of the patients with secondary immunodeficiency caused by various aetiologies were collected from the First Affiliated Hospital of Nanchang University, China. Based on these records, models were developed with the machine learning method to predict the potential infectious pathogens that may inflict the patients with secondary immunodeficiency caused by various disease conditions other than HIV infection. Results Several metrics were used to evaluate the models’ performance. A consistent conclusion can be drawn from all the metrics that Gradient Boosting Machine had the best performance with the highest accuracy at 91.01%, exceeding other models by 13.48, 7.14, and 4.49% respectively. Conclusions The models developed in our study enable the prediction of potential infectious pathogens that may affect the patients with secondary immunodeficiency caused by various aetiologies except for HIV infection, which will help clinicians make a timely decision on antibiotic use before microorganism culture results return.

show abstract

Partnership dynamics in mathematical models and implications for representation of sexually transmitted infections: a review

Cited by 6 publications

References 55 publications

Changes in Sexual Behaviors with Opposite-Sex Partners and Sexually Transmitted Infection Outcomes Among Females and Males Ages 15–44 Years in the USA: National Survey of Family Growth, 2008–2019

Changes in Sexual Behaviors with Opposite-Sex Partners and Sexually Transmitted Infection Outcomes Among Females and Males Ages 15–44 Years in the USA: National Survey of Family Growth, 2008–2019

Agent-Based Modelling of Mpox Infection Dynamics: Simulating Disease Transmission and Control Strategies

Development of a predictive machine learning model for pathogen profiles in patients with secondary immunodeficiency

Contact Info

Product

Resources

About