Dueling biological and social contagions

  title={Dueling biological and social contagions},
  author={Feng Fu and Nicholas A. Christakis and James H. Fowler},
  journal={Scientific Reports},
Numerous models explore how a wide variety of biological and social phenomena spread in social networks. However, these models implicitly assume that the spread of one phenomenon is not affected by the spread of another. Here, we develop a model of “dueling contagions”, with a particular illustration of a situation where one is biological (influenza) and the other is social (flu vaccination). We apply the model to unique time series data collected during the 2009 H1N1 epidemic that includes… 

Spread of infectious disease and social awareness as parasitic contagions on clustered networks

It is found that increasing network clustering, which is known to hinder disease spread, can actually allow them to sustain larger epidemics of the disease in models with awareness, going against the conventional wisdom suggesting that random networks are justifiable as they provide worst-case scenario forecasts.

Macroscopic patterns of interacting contagions are indistinguishable from social reinforcement

It is shown that so-called simple and complex contagions cannot be told apart if there is more than one simple contagion traversing the population at the same time.

Exposure, hazard, and survival analysis of diffusion on social networks

A framework for measuring features of diffusion processes on social networks using the epidemiological concepts of exposure and competing risks is described and it is shown that classical survival regression models can be adapted to estimate the rate of diffusion, and actor/edge attributes associated with successful transmission or adoption, while accounting for the topology of the social network.

Complex social contagion induces bistability on multiplex networks

This work incorporates multilayer reinforcement into ignorant-spreader-ignorant (SIS) model on multiplex networks and shows the powerful and non-negligible impacts of complex dynamical mechanisms, which provides valuable insights toward the spreading behaviors in the digital age.

Endogenous social distancing and its underappreciated impact on the epidemic curve

This work uses game theory to formalize the interaction of voluntary social distancing in a partially infected population, improves the behavioral micro-foundations of epidemiological models, and predicts differential social Distancing rates dependent on health status.

Local risk perception enhances epidemic control

Comparing three modes of epidemiological decision-making in the midst of a growing outbreak using network-based mathematical models that capture plausible heterogeneity in human contact patterns finds vaccinating based on the number of infected acquaintances is expected to prevent the most infections while requiring the fewest intervention resources.

Coupled Dynamics of Behavior and Disease Contagion Among Antagonistic Groups

A simple model of coupled behavior-change and infection in a structured population characterized by homophily and outgroup aversion is analyzed and reveals dynamics that are suggestive of the processes currently observed under pandemic conditions in culturally and/or politically polarized populations such as the United States.

Network multipliers and public health

It is argued that spillover needs to be considered far more frequently when thinking about epidemiology and public health; otherwise, the authors may miss important opportunities for public health impact and evaluation and introduce a new relevant metric: the network multipler.

The effect of competition between health opinions on epidemic dynamics

A compartmental model that couples a disease spread framework with competition of two mutually exclusive health opinions associated with different health behaviors and captures feedback between spread of awareness through social networks and infection dynamics and can serve as a basis for more elaborate individual-based models.

Oscillatory dynamics in the dilemma of social distancing

This work quantifies the degree to which social distancing mitigates the epidemic and its dependence on individuals’ responsiveness and rationality in their behaviour changes and offers new insights into leveraging human behaviour in support of pandemic response.



Complex social contagion makes networks more vulnerable to disease outbreaks

This work builds on mounting evidence that health behaviors - such as vaccination, and refusal thereof - can spread across social networks through a process of complex contagion that requires social reinforcement, pointing to the importance of health behavior spread in predicting and controlling disease outbreaks.

Erratic Flu Vaccination Emerges from Short-Sighted Behavior in Contact Networks

This work investigates the interplay between contact patterns, influenza-related behavior, and disease dynamics by incorporating game theory into network models and demonstrates that rich and complex dynamics can result from the interaction between infectious diseases, human contact pattern, and behavior.

Social contagion theory: examining dynamic social networks and human behavior

The regularities that led us to propose that human social networks may exhibit a ‘three degrees of influence’ property are described and the statistical approaches used to characterize interpersonal influence with respect to phenomena as diverse as obesity, smoking, cooperation, and happiness are reviewed.

Social Network Sensors for Early Detection of Contagious Outbreaks

This paper proposes an alternative strategy that does not require ascertainment of global network structure, namely, simply monitoring the friends of randomly selected individuals, which could in principle be generalized to other biological, psychological, informational, or behavioral contagions that spread in networks.

Beyond network structure: How heterogeneous susceptibility modulates the spread of epidemics

These results show that the failure to include differential susceptibility to epidemic models can lead to a systematic over/under estimation of fundamental epidemic parameters when the structure of the networks is not independent from the susceptibility of the nodes or when there are correlations between the susceptible individuals.

The Impact of Imitation on Vaccination Behavior in Social Contact Networks

This work uses network-based mathematical models to study the effects of both imitation behavior and contact heterogeneity on vaccination coverage and disease dynamics and integrates contact network epidemiological models with a framework for decision-making.

Social Contact Networks and Disease Eradicability under Voluntary Vaccination

This work shows how disease eradicability in populations where voluntary vaccination is the primary control mechanism may depend partly on whether the disease is transmissible only to a few close social contacts or to a larger subset of the population.

Policy Resistance Undermines Superspreader Vaccination Strategies for Influenza

It is concluded that the design of influenza vaccine strategies involving widespread incentive use and/or targeting of superspreaders should account for policy resistance, and mitigate it whenever possible.

Assessing Vaccination Sentiments with Online Social Media: Implications for Infectious Disease Dynamics and Control

This work uses publicly available data from 101,853 users of online social media collected over a time period of almost six months to measure the spatio-temporal sentiment towards a new vaccine and finds that most communities are dominated by either positive or negative sentiments towards the novel vaccine.

Infection dynamics on scale-free networks.

  • R. MayA. Lloyd
  • Mathematics
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2001
Approximate expressions for the final size of an epidemic in an infinite closed population and for the dependence of infection probability on an individual's degree of connectivity within the population are derived.