The coevolution of contagion and behavior with increasing and decreasing awareness

Cognitive cascades: How to model (and potentially counter) the spread of fake news

Understanding the spread of false or dangerous beliefs-often called misinformation or disinformation-through a population has never seemed so urgent. Network science researchers have often taken a page from epidemiologists, and modeled the spread of false beliefs as similar to how a disease spreads through a social network. However, absent from those disease-inspired models is an internal model of an individual's set of current beliefs, where cognitive science has increasingly documented how the interaction between mental models and incoming messages seems to be crucially important for their adoption or rejection. Some computational social science modelers analyze agent-based models where individuals do have simulated cognition, but they often lack the strengths of network science, namely in empirically-driven network structures. We introduce a cognitive cascade model that combines a network science belief cascade approach with an internal cognitive model of the individual agents as in opinion diffusion models as a public opinion diffusion (POD) model, adding media institutions as agents which begin opinion cascades. We show that the model, even with a very simplistic belief function to capture cognitive effects cited in disinformation study (dissonance and exposure), adds expressive power over existing cascade models. We conduct an analysis of the cognitive cascade model with our simple cognitive function across various graph topologies and institutional messaging patterns. We argue from our results that population-level aggregate outcomes of the model qualitatively match what has been reported in COVID-related public opinion polls, and that the model dynamics lend insights as to how to address the spread of problematic beliefs. The overall model sets up a framework with which social science misinformation researchers and computational opinion diffusion modelers can join forces to understand, and hopefully learn how to best counter, the spread of disinformation and "alternative facts."

Social network theory-an underutilized opportunity to align innovative methods with the demands of the opioid epidemic

It has been almost 3 years since the opioid epidemic was declared a national public health emergency under federal law. Solutions have focused on supply-reduction strategies. These approaches, however, have failed to significantly curtail opioid overdose and related death. Demand for opioid use arising from social networks and environment is an important contributing factor to the current opioid epidemic. Adoption of existing underused methods is needed to drive further progress. This Perspective proposes the social contagion model as a promising framework through which to operationalize evaluation of the influence of social networks and environment in the opioid epidemic and argues for its greater application. Comparing the current epidemic with previous opioid epidemics reiterates the utility of the social contagion model. This model acknowledges social network influence on individual behavior. It leverages tools from epidemiology, permits evaluation of interpersonal influence, facilitates consideration of disproportionate and collateral effects, and overcomes limitations of traditional models and geographic assumptions inherent to many approaches surrounding the current opioid epidemic. Analyzing the opioid epidemic within a social contagion framework will enhance evaluation methods and enable the design of interventions to reflect the actual demands of the current crisis. If the influence of social networks and environment is not considered, the devastating toll of the opioid epidemic could grow.

A New Transmission Route for the Propagation of the SARS-CoV-2 Coronavirus

BACKGROUND

Starting late 2019, a novel coronavirus spread from the capital of the Hubei province in China to the rest of the country, then to most of the world. To anticipate future trends in the development of the pandemic, we explore here, based on public records of infected persons, how variation in the virus tropism could end up in different patterns, warranting a specific strategy to handle the epidemic.

METHODS

We use a compartmental model to describe the evolution of an individual through several possible states: susceptible, infected, alternative infection, detected, and removed. We fit the parameters of the model to the existing data, taking into account significant quarantine changes where necessary.

RESULTS

The model indicates that Wuhan quarantine measures were effective, but that alternative virus forms and a second propagation route are compatible with available data. For the Hong Kong, Singapore, and Shenzhen regions, the secondary route does not seem to be active.

CONCLUSIONS

Hypotheses of an alternative infection tropism (the gut tropism) and a secondary propagation route are discussed using a model fitted by the available data. Corresponding prevention measures that take into account both routes should be implemented to the benefit of epidemic control.

A New Transmission Route for the Propagation of the SARS-CoV-2 Coronavirus

Simple Summary

In animals, two dominant organs are infected by coronaviruses, the respiratory tract and the gut. In past epidemics, a shift from the lung to the gut or vice versa has been observed. Analysis of the SARS episode in 2003 had suggested the possibility of a “double epidemic” explaining the absence of severe cases in Shanghai at the time. Here, using data from Wuhan and Hong Kong, Singapore, and Shenzhen, we discuss whether the present respiratory contagion route for SARS-CoV-2 might have also resulted from faecal contamination. We noticed that the propagation of the disease in the city of Wuhan underwent an original course. Besides the expected respiratory route, the disease appeared to have been greatly facilitated by a secondary propagation route, thus substantiating the beneficial effect of an effective quarantine. By contrast, a similar situation based on early data did not prevail in Hong Kong or Singapore. Our work shows how health authorities could decide to orient their prevention measures, depending on the analysis of ongoing epidemics, as a function of the identification of a secondary route in parallel with the prevailing one. Our model could also help highlight voluntarily or accidentally biased data collection.

Abstract

Background: Starting late 2019, a novel coronavirus spread from the capital of the Hubei province in China to the rest of the country, then to most of the world. To anticipate future trends in the development of the pandemic, we explore here, based on public records of infected persons, how variation in the virus tropism could end up in different patterns, warranting a specific strategy to handle the epidemic. Methods: We use a compartmental model to describe the evolution of an individual through several possible states: susceptible, infected, alternative infection, detected, and removed. We fit the parameters of the model to the existing data, taking into account significant quarantine changes where necessary. Results: The model indicates that Wuhan quarantine measures were effective, but that alternative virus forms and a second propagation route are compatible with available data. For the Hong Kong, Singapore, and Shenzhen regions, the secondary route does not seem to be active. Conclusions: Hypotheses of an alternative infection tropism (the gut tropism) and a secondary propagation route are discussed using a model fitted by the available data. Corresponding prevention measures that take into account both routes should be implemented to the benefit of epidemic control.