Conference Agenda

A central theme in social network analysis is diffusion—the spread of diseases, information, and behaviors through social ties. Originally introduced by demographers and widely applied by epidemiologists, the basic reproduction number (𝑹0) and its derivations serve as foundational metrics for diffusion processes. Using infectious disease diffusion as an example, this paper describes a mechanism overlooked in most conventional analyses, in which a disease can endogenously “compete” with itself when multiple infectious individuals race to infect the same susceptible individual, thereby reducing the effective reproductive rate. Utilizing an empirically-calibrated network epidemiological model of wild-type COVID-19 diffusion in its early pandemic, we show that the mechanism would be expected to reduce its reproductive rate by an average of 39%. Simulation experiments further identify several different types of endogenous competition mechanisms and their relative effect sizes. We highlight the incorporation of endogenous competition mechanism as a necessary step in realistically modeling diffusion processes.

Understanding optimal strategies for promoting protective behaviors in networked populations is crucial for effective epidemic containment. While diseases spread via simple contagion—where a single contact can transmit infection—protective behaviors like mask-wearing often spread through complex contagion, requiring reinforcement from multiple contacts. This dichotomy presents a strategic trade-off: seeding behavior adoption at nodes with high standard centrality (key in disease spread) may slow the epidemic but hinder the propagation of the protective behavior, whereas seeding at nodes with high complex centrality accelerates behavior adoption but may allow the epidemic to spread unchecked.

In this work, we couple a simple Susceptible-Infected-Susceptible (SIS) epidemic model with a complex contagion model of behavior adoption. We analyze various seeding strategies across single-layer and multiplex network frameworks to assess their effectiveness under different objectives, such as minimizing fatalities or delaying peak infection. Our findings reveal that the optimal seeding strategy is sensitive to the relative time scales of disease and behavior spread and often involves an interpolation between standard and complex centrality measures. These results offer nuanced insights into designing targeted interventions that balance rapid adoption of protective behaviors with effective suppression of epidemic spread.

Social norms are often thought to emerge because they benefit those who follow them. Yet, the persistence of “unpopular norms”—behavioral regularities that endure despite being privately rejected by most—challenges this assumption. Examples include foot binding in historical China, child marriage, and entrenched patterns of bribery and corruption. How can we explain the emergence of such norms?

We argue that the structure of social networks plays a crucial role in the emergence of unpopular norms by magnifying the “majority illusion” paradox: Even when a behavior is globally rare, certain network structures can cause individuals to perceive that most of their neighbors conform to it, creating the false impression that it is more widespread than it truly is. This illusion, when coupled with network externalities, can ripple through the network, pushing the population toward widespread compliance with an unpopular norm.

Our agent-based simulations reveal that network structure not only magnifies this illusion but also facilitates its spread under conditions of incomplete information and simplified decision-making heuristics. While most network configurations allow the majority to resist the spread of an unpopular norm pushed by a few fanatics, specific structures—characterized by fat-tailed degree distributions, disassortative mixing, and strong degree-trait correlation—enable these fanatics to sway the majority into compliance.

To empirically validate this mechanism, we will conduct a large-scale incentivized social network experiment involving an asymmetric coordination game on experimentally manipulated networks. During my talk, I will present the simulation results, describe the experimental design, and—if available—discuss the experimental findings.

Despite a remarkable decline in global incidence over the past two decades, the full eradication of malaria remains a major global health challenge. An estimated 249 million malaria cases still occurred in 85 endemic countries in 2022, according to the World Health Organization, which has set a goal of reducing global incidence by 90% by 2030. Achieving this target requires increased efforts to address persistent local malaria clusters. These are often located in hard-to-reach populations in marginalized areas of the Global South, where conventional large-scale interventions may prove insufficient hence presenting

challenges for public health policies. Effectively fighting malaria in these settings requires context-specific interventions informed by localized knowledge.

Large scale interventions focus on indoor mosquito bites’ preventive strategies such as long-lasting insecticide-treated nets (LLINs) and indoor residual spraying (IRS). Residual malaria transmission take place in outdoor contexts, where exposure occurs during occupational activities, outdoor sleeping, or social gatherings. This challenge is particularly concerning given that effective solutions exist. For example, topical insecticides and repellents, available in various forms such as sprays, lotions, and creams, have been proven to provide cost-effective protection against outdoor biting, and individuals generally acknowledge the effectiveness of these preventive measures. However, an important gap remains between recognition and implementation – the adoption of these protective measures at the community level consistently falls short of the critical mass necessary to achieve meaningful epidemiological impact.

In this work, we show that social network effects can help explain the low adoption of outdoor biting preventive measures in residual malaria epicenters. While previous research in the social sciences and public health has emphasized how networks facilitate the diffusion of positive health practices, our findings reveal that networks can also amplify resistance to adopt new measures that originates from local health beliefs or perceived costs and risks of changing established routines. Through our investigation of two marginalized village populations totalling 352 individuals in Meghalaya, India — a recognized residual malaria epicenter — we identified a dual mechanism combining high-threshold complex contagion and negative influence that explains the persistently low adoption rates of an insecticidal cream designed for outdoor prevention use. By analyzing fieldwork signed network data on the villagers’ health-related peer-to-peer discussions (i.e., villagers with whom one was likely to discuss health issues, as well as villagers with whom one avoided such discussions), we found that overcoming personal resistance to using the cream required a relatively high prevalence of users among their positive ties, which we were able to empirically estimate. However the positive impact provided by such an unlikely situation is offset by observing just one negative tie using the cream: our results provide strong evidence of a negative marginal effect on one’s likelihood of using the cream yielded by the presence of at least one user to whom one reported being negatively tied, so that having a negative tie with a cream user decreased the villagers’ probability of using the cream by 5.5%, net of the other modelled factors.

This study examines the mechanisms of public compliance with stay-at-home orders introduced in response to the Covid-19 pandemic. Various hypothesis have been proposed by scholars to explain compliance patterns as a result of media coverage, policy enforcement and civic capital. However, they fail to explain why some countries such as France had high levels of compliance but low levels of trust in policymakers and the medias. Our hypothesis is that, through their day-to-day interactions, individuals have contributed significantly to the emergence and reinforcement of compliance with the to stay-at-home orders, as a result of social control and peer influence. In the context of France’s 2020 spring lockdown, this article seeks to answer the question of the extent to which social networks and interactions influenced the dynamics of adherence with public health policy.

A data-driven approach was employed to develop agent-based models, with data derived from the Google Community Mobility Reports and social surveys conducted in France. Subsequently, an exploration of compliance mechanisms was conducted through a systematic comparison of model outputs with the actual lockdown compliance curves. Our results reveal significant variation in compliance levels due to social interactions, demonstrating that individual lockdown compliance cannot be accurately understood without considering account social networks and interactions. Our model allows us to better interpret compliance with public health measures as the result of the combined effect of policy, media coverage and social interactions.