Mathematical model of dating apps influence on sexually transmitted diseases spread

TL;DR

An extended $SIS$ framework links dating-app usage to STD spread on a two-layer social graph, implemented via agent-based simulation. The model introduces an Exposed state, immunity decay, and multi-pathogen interactions to capture heterogeneity and network structure. Experiments show that higher dating-app adoption elevates the reproduction number $E[R_t]$ and can trigger outbreaks, but policy levers such as limiting contact frequency or enforcing STD-free testing can mitigate risk. The work provides a quantitative tool for in silico evaluation of interventions at the intersection of digital platforms and infectious disease dynamics.

Abstract

Sexually transmitted diseases (STDs) are a group of pathogens infecting new hosts through sexual interactions. Due to its social and economic burden, multiple models have been proposed to study the spreading of pathogens. In parallel, in the ever-evolving landscape of digital social interactions, the pervasive utilization of dating apps has become a prominent facet of modern society. Despite the surge in popularity and the profound impact on relationship formation, a crucial gap in the literature persists regarding the potential ramifications of dating apps usage on the dynamics of STDs. In this paper, we address this gap by presenting a novel mathematical framework - an extended Susceptible-Infected-Susceptible (SIS) epidemiological model to elucidate the intricate interplay between dating apps engagement and the propagation of STDs. Namely, as dating apps are designed to make users revisit them and have mainly casual sexual interactions with other users, they increase the number of causal partners, which increases the overall spread of STDS. Using extensive simulation, based on real-world data, explore the effect of dating apps adoption and control on the STD spread. We show that an increased adoption of dating apps can result in an STD outbreak if not handled appropriately.

Figures (5)

• Figure 1: A schematic view of transition between disease stages, shown for $k=2$. The red arrows indicate that from this stage, the individual might die from the disease. In a similar manner, the orange, black, and green arrows indicate exposure, infection, and recovery with immunity decay, respectively.
• Figure 2: A schematic view of the interaction graph for a single point in time.
• Figure 3: A comparison of the STD spread dynamics with different levels of dating app adoption. The results are shown as the mean $\pm$ standard deviation of $n=100$ simulation realizations. The case inferred from the historical data is marked by a red square while the other cases are marked by blue circles. The gray (dashed) line indicates $E[R_t] = 1$ which is the epidemic outbreak threshold.
• Figure 4: A comparison of the STD spread dynamics for two cases - genuinely helping users to find stable relationships and promoting casual sexual encounters and further usage of the application. The results are shown as the mean $\pm$ standard deviation of $n=100$ simulation realizations. The x-axis is presented in a logarithmic scale. The gray (dashed) line indicates $E[R_t] = 1$ which is the epidemic outbreak threshold.
• Figure 5: The average reproduction number ($E[R_t]$) with respect to the duration between two times a user has to prove it is STD-free $\tau$. The results are shown as the mean $\pm$ standard deviation of $n=100$ simulation realizations. The x-axis is presented in a logarithmic scale. The gray (dashed) line indicates $E[R_t] = 1$ which is the epidemic outbreak threshold.