Characterising the structure of the largest online commercial sex network in the UK: observational study with implications for STI prevention

A Review of Network Models for HIV Spread

BACKGROUND

HIV/AIDS has been a global health crisis for over 4 decades. Network models, which simulate human behavior and intervention impacts, have become an essential tool in guiding HIV prevention strategies and policies. However, no comprehensive survey of network models in HIV research has been conducted. This article fills that gap, offering a summary of past work and future directions to engage more researchers and inform policy related to eliminating HIV.

SETTING

Network models explicitly represent interactions between individuals, making them well-suited to study HIV transmission dynamics. Two primary modeling paradigms exist: a mechanistic approach from applied mathematics and a statistical approach from the social sciences. Each has distinct strengths and weaknesses, which should be understood for effective application to HIV research.

METHODS

We conducted a systematic review of network models used in HIV research, detailing the model types, populations, interventions, behaviors, datasets, and software used, while identifying potential future research directions.

RESULTS

Network models are particularly valuable for studying behaviors central to HIV transmission, such as partner selection and treatment adherence. Unlike traditional models, they focus on individual behaviors, aligning them with clinical practice. However, more accurate network data are needed for better model calibration and actionable insights.

CONCLUSIONS

This article serves as a point of reference for HIV researchers interested in applying network models and understanding their limitations. To our knowledge, this is the most comprehensive review of HIV network models to date.

Disrupting drive-by download networks on Twitter

This paper tests disruption strategies in Twitter networks containing malicious URLs used in drive-by download attacks. Cybercriminals use popular events that attract a large number of Twitter users to infect and propagate malware by using trending hashtags and creating misleading tweets to lure users to malicious webpages. Due to Twitter’s 280 character restriction and automatic shortening of URLs, it is particularly susceptible to the propagation of malware involved in drive-by download attacks. Considering the number of online users and the network formed by retweeting a tweet, a cybercriminal can infect millions of users in a short period. Policymakers and researchers have struggled to develop an efficient network disruption strategy to stop malware propagation effectively. We define an efficient strategy as one that considers network topology and dependency on network resilience, where resilience is the ability of the network to continue to disseminate information even when users are removed from it. One of the challenges faced while curbing malware propagation on online social platforms is understanding the cybercriminal network spreading the malware. Combining computational modelling and social network analysis, we identify the most effective strategy for disrupting networks of malicious URLs. Our results emphasise the importance of specific network disruption parameters such as network and emotion features, which have proved to be more effective in disrupting malicious networks compared to random strategies. In conclusion, disruption strategies force cybercriminal networks to become more vulnerable by strategically removing malicious users, which causes successful network disruption to become a long-term effort.

Exploring sexual contact networks by analyzing a nationwide commercial-sex review website

Understanding the structure of human sexual contact networks is vital in a broad range of disciplines, including sociology, biology, public health, and anthropology. However, sexual contact networks are yet to be understood because technical and privacy issues make it difficult to conduct accurate, large-scale surveys. In this study, we surveyed data openly available on one of the largest adult entertainment websites in Japan, where male clients (MCs) can write online customer reviews of female commercial sex workers (FCSWs). In particular, our investigation focused on a type of establishment called "soapland," the only type of sex industry in Japan where sexual intercourse is publicly permitted. Soaplands are scattered throughout Japan, and the study website covers approximately 66% of them. Using such a vast amount of data on a nationwide scale, we clarified the network structure of commercial sex, characterized by small-world, scale-free, and disassortative mating properties. To study geographical characteristics, we compared the resulting network with three different artificially generated networks via the random rewiring of links. Moreover, we considered a simple epidemic model on the resulting network, and investigated whether it would be more effective to provide infection control measures to FCSWs or MCs. We determined that active FCSWs constitute an important pathway of infection propagation in commercial sex networks, but MCs also play an essential role as weak ties.