Tweets vs Pathogen Spread: A Case Study of COVID-19 in American States

TL;DR

This study models the mutual influence of awareness and COVID-19 spread using a coupled SIR model, analyzing Twitter data across US states to reveal how awareness impacts epidemic dynamics and state-specific parameters.

Contribution

It introduces a coupled SIR model incorporating awareness effects and empirically links Twitter activity to epidemic parameters across US states.

Findings

Awareness can suppress epidemic spread in certain parameter regions.

Twitter activity correlates with model-derived immunity parameters.

State-specific parameters change at different pandemic peaks.

Abstract

The concept of the mutual influence that awareness and disease may exert on each other has recently presented significant challenges. The actions individuals take to prevent contracting a disease and their level of awareness can profoundly affect the dynamics of its spread. Simultaneously, disease outbreaks impact how people become aware. In response, we initially propose a null model that couples two Susceptible-Infectious-Recovered (SIR) dynamics and analyze it using a mean-field approach. Subsequently, we explore the parameter space to quantify the effects of this mutual influence on various observables. Finally, based on this null model, we conduct an empirical analysis of Twitter data related to COVID-19 and confirmed cases within American states. Our findings indicate that in specific regions of the parameter space, it is possible to suppress the epidemic by increasing awareness, and we investigate phase transitions. Furthermore, our model demonstrates the ability to alter the dominant population group by adjusting parameters throughout the course of the outbreak. Additionally, using the model, we assign a set of parameters to each state, revealing that these parameters change at different pandemic peaks. Notably, a robust correlation emerges between the ranking of states' Twitter activity, as gathered from empirical data, and the immunity parameters assigned to each state using our model. This observation underscores the pivotal role of sustained awareness transitioning from the initial to the subsequent peaks in the disease progression.