Dynamics of Neisseria gonorrhoeae transmission among female sex workers and clients: A mathematical modeling study.

BACKGROUND: This study aimed to examine the transmission dynamics of Neisseria gonorrhoeae (NG) in heterosexual sex work networks (HSWNs) and the impact of variation in sexual behavior and interventions on NG epidemiology. METHODS: The study employed an individual-based mathematical model to simulate NG transmission dynamics in sexual networks involving female sex workers (FSWs) and their clients, primarily focusing on the Middle East and North Africa region. A deterministic model was also used to describe NG transmission from clients to their spouses. RESULTS: NG epidemiology in HSWNs displays two distinct patterns. In the common low-partner-number HSWNs, a significant proportion of NG incidence occurs among FSWs, with NG prevalence 13 times higher among FSWs than clients, and three times higher among clients than their spouses. Interventions substantially reduce incidence. Increasing condom use from 10 % to 50 % lowers NG prevalence among FSWs, clients, and their spouses from 12.2 % to 6.4 %, 1.2 % to 0.5 %, and 0.4 % to 0.2 %, respectively. Increasing symptomatic treatment coverage among FSWs from 0 % to 100 % decreases prevalence from 10.6 % to 4.5 %, 0.8 % to 0.4 %, and 0.3 % to 0.1 %, respectively. Increasing asymptomatic treatment coverage among FSWs from 0 % to 50 % decreases prevalence from 8.2 % to 0.4 %, 0.6 % to 0.1 %, and 0.2 % to 0.0 %, respectively, with very low prevalence when coverage exceeds 50 %. In high-partner-number HSWNs, prevalence among FSWs saturates at a high level, and the vast majority of incidence occurs among clients and their spouses, with a limited impact of incremental increases in interventions. CONCLUSION: NG epidemiology in HSWNs is typically a "fragile epidemiology" that is responsive to a range of interventions even if the interventions are incremental, partially efficacious, and only applied to FSWs.

Estimating protection afforded by prior infection in preventing reinfection: Applying the test-negative study design.

The COVID-19 pandemic has highlighted the need to use infection testing databases to rapidly estimate effectiveness of prior infection in preventing reinfection ($P{E}_S$) by novel SARS-CoV-2 variants. Mathematical modeling was used to demonstrate a theoretical foundation for applicability of the test-negative, case-control study design to derive $P{E}_S$. Apart from the very early phase of an epidemic, the difference between the test-negative estimate for $P{E}_S$ and true value of $P{E}_S$ was minimal and became negligible as the epidemic progressed. The test-negative design provided robust estimation of $P{E}_S$ and its waning. Assuming that only 25% of prior infections are documented, misclassification of prior infection status underestimated $P{E}_S$, but the underestimate was considerable only when >50% of the population was ever infected. Misclassification of latent infection, misclassification of current active infection, and scale-up of vaccination all resulted in negligible bias in estimated $P{E}_S$. The test-negative design was applied to national-level testing data in Qatar to estimate $P{E}_S$ for SARS-CoV-2. $P{E}_S$ against SARS-CoV-2 Alpha and Beta variants was estimated at 97.0% (95% CI: 93.6-98.6) and 85.5% (95% CI: 82.4-88.1), respectively. These estimates were validated using a cohort study design. The test-negative design offers a feasible, robust method to estimate protection from prior infection in preventing reinfection.