Berlin 2024 – scientific programme
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SOE: Fachverband Physik sozio-ökonomischer Systeme
SOE 7: Poster
SOE 7.12: Poster
Monday, March 18, 2024, 18:00–20:30, Poster D
Modeling the Interplay of Awareness and Epidemics: A Mean-Field Approach with Twitter Data Analysis on COVID-19 Dynamics — Sara Shabani1,2, Sahar Jafarbegloo1, Sadegh Raeisi1, and •Fakhteh Ghanbarnejad1,3 — 1Sharif University of Technology, Tehran, Iran — 2Virginia Tech, Blacksburg, USA — 3Technical University of Dresden, Dresden,Germany
The recent exploration of the reciprocal impact between awareness and disease introduces notable challenges. The preventive actions individuals take and their awareness levels can significantly shape the dynamics of disease spread, while disease outbreaks can influence awareness. We propose an initial null model that couples two Susceptible-Infectious-Recovered (SIR) dynamics, employing a mean-field approach for analysis. We then explore the parameter space to quantify the mutual influence on various observables. Utilizing this null model, we empirically analyze Twitter data related to COVID-19 and confirmed cases in American states.
Our findings reveal that in specific parameter space, increasing awareness can suppress the epidemic, leading us to investigate phase transitions. Moreover, our model showcases the ability to shift the dominant population group by adjusting parameters during the outbreak. Applying the model, we assign parameters to each state, unveiling changes at different pandemic peaks. Notably, a robust correlation emerges between states' Twitter activity and immunity parameters assigned using our model, emphasizing the crucial role of sustained awareness in disease progression from initial to subsequent peaks.
Keywords: Epidemic spreading; Epidemic control; SIR dynamic