Using an agent-based sexual-network model to guide mitigation efforts for controlling chlamydia

Abstract

We create and analyze a stochastic heterosexual agent-based bipartite network model to help understand the spread of chlamydia trachomatis. Chlamydia is the most common sexually transmitted infection in the United States and is major cause of infertility, pelvic inflammatory disease, and ectopic pregnancy among women. We use an agent-based network model to capture the complex heterogeneous assortative sexual mixing network of men and women. Both long-term and casual partnerships are modeled with different sexual contact frequencies and condom use. We use simulations to compare the effectiveness of intervention strategies based on randomly screening people for infection, treating the partners of infected people, and rescreening for infection after treatment. We compare the difference between treating the partners of an infected person both with, and without, testing them first for infection. The highest prevalence is among young sexually active individuals. We calibrate the model parameters to agree with recent survey data showing chlamydia prevalence of 14% of the women and 9% of the men in the 15 − 25 year-old African American residents of New Orleans, Louisiana. We observed that although increased chlamydia screening and treating most of the partners of infected people will reduce the incidence, these mitigations alone are not sufficient to control the epidemic. The model predicts that the current epidemic can brought under control once over half of the partners of infected people are tested and treated.

Generating a heterosexual bipartite network embedded in social network

“…We are currently simulating a stochastic agent-based network model on SexNet for the spread of chlamydia and comparing different intervention strategies to control the spread of STIs that are implemented in public health, such as screening, partner treatment, rescreening, and peer referrals (Qu et al 2020;Azizi et al 2020). These simulations will use the underlying social contact network to improve the current intervention models by considering the impact of counseling and behavioral changes such as increasing condom use or social contact notification.…”

“…These graphs must account for the distribution for the number of sexual partners people have (their degree distribution) and the number of partners their partners have (the joint-degree, or degree-degree, distribution). The existing algorithms that generate bipartite random graphs preserving degree and joint-degree distributions of the nodes are strictly based on the number of partners people have and not other demographic factors, such as age or location (Newman 2002;Hakimi 1962;Boroojeni et al 2017;Azizi et al 2016Azizi et al , 2017Azizi et al , 2018.…”

Epidemics on networks: Reducing disease transmission using health emergency declarations and peer communication

“…However, the networks differ in terms of degree distributions and other properties due to their different structures. Simulations start by infecting the most connected node (index case) to reach a balanced initial condition [1]. They are came out with the model baseline parameters in Table (1), unless stated otherwise.…”

“…Simulations start by infecting the most connected node (index case) to reach a balanced initial condition [1]. They are came out with the model baseline parameters in Table (1), unless stated otherwise.…”