ABSTRACT
The Omicron wave is the largest wave of COVID-19 pandemic to date, more than doubling any other in terms of cases and hospitalizations in the United States. In this paper, a large-scale agent-based model of policy interventions that could have been implemented to mitigate the Omicron wave is presented. The model takes into account the behaviors of individuals and their interactions with one another within a nationally representative population, as well as the efficacy of various interventions such as social distancing, mask wearing, testing, tracing, and vaccination. We use the model to simulate the impact of different policy scenarios and evaluate their potential effectiveness in controlling the spread of the virus. The results suggest the Omicron wave could have been substantially curtailed via a combination of interventions comparable in effectiveness to extreme and unpopular singular measures such as widespread closure of schools and workplaces, and highlight the importance of early and decisive action. © 2023 Wiley-VCH GmbH.
ABSTRACT
Inferring group membership of social media users is of high interest in many domains. Group membership is typically inferred via network interactions with other members, or by the usage of in-group language. However, network information is incomplete when users or groups move between platforms, and in-group keywords lose significance as public discussion about a group increases. Similarly, using keywords to filter content and users can fail to distinguish between the various groups that discuss a topic-perhaps confounding research on public opinion and narrative trends. We present a classifier intended to distinguish members of groups from users discussing a group based on contextual usage of keywords. We demonstrate the classifier on a sample of community pairs from Reddit and focus on results related to the COVID-19 pandemic.