Multiplayer games and HIV transmission via casual encounters

Abstract

Population transmission models have been helpful in studying the spread of HIV. They assess changes made at the population level for different intervention strategies. To further understand how individual changes affect the population as a whole, game-theoretical models are used to quantify the decision-making process. Investigating multiplayer nonlinear games that model HIV transmission represents a unique approach in epidemiological research. We present here 2-player and multiplayer noncooperative games where players are defined by HIV status and age and may engage in casual (sexual) encounters. The games are modelled as generalized Nash games with shared constraints, which is completely novel in the context of our applied problem. Each player's HIV status is known to potential partners, and players have personal preferences ranked via utility values of unprotected and protected sex outcomes. We model a player's strategy as their probability of being engaged in a casual unprotected sex encounter (USE), which may lead to HIV transmission; however, we do not incorporate a transmission model here. We study the sensitivity of Nash strategies with respect to varying preference rankings, and the impact of a prophylactic vaccine introduced in players of youngest age groups. We also study the effect of these changes on the overall increase in infection level, as well as the effects that a potential prophylactic treatment may have on age-stratified groups of players. We conclude that the biggest impacts on increasing the infection levels in the overall population are given by the variation in the utilities assigned to individuals for unprotected sex with others of opposite HIV status, while the introduction of a prophylactic vaccine in youngest age group (15-20 yr olds) slows down the increase in HIV infection.

The Impact of Pre-exposure Prophylaxis for Human Immunodeficiency Virus on Gonorrhea Prevalence

“…Similarly, both social dynamics and contact network structure can play a significant role in the spread of infectious diseases, including HIV and gonorrhea [27, 28, 54, 54, 55, 56, 57, 58, 59, 60, 61]. In future work, our study design could be refined by using network simulations that account for social dynamics concerning HIV and gonorrhea transmission.…”

“…HIV transmission and the impact of interventions such as antiviral drugs, condoms, and hypothetical vaccines have been a frequent topic of mathematical modelling efforts [23, 24, 25, 26, 27, 28, 29, 30]. A few of these models have also considered interactions between HIV and other infections, such as the effects of tuberculosis coinfection with HIV [23].…”

Insufficient declines in new HIV infection rates amidst high scores in HIV testing and awareness: ‘a game theory simulation’

“…That is to say, individuals would prefer some form of sex to no sex based on their HIV status. We build on extensive work done by (Schroeder and Rojas 2002;Tully et al 2017). In scenario 1 (Fig.…”

“…It is concerned with the analysis of strategic interactions among agents, and how they reach optimal decision-making assuming rationality (Brickley et al 2000;Malhotra 2012;Barough et al 2012). Whereas some efforts have been made to explain the determinants of sexual behaviour and the spread of HIV (Ahituv et al 1996;Schroeder and Rojas 2002;Tully et al 2017), more evidence is still needed to explain the micro-level behavioural-dynamics of the spread of HIV as a function of increased HIV tests and awareness. The present paper builds on previous work by adding the gender dimension in sexual behaviours.…”