Media-driven adaptive behavior in pandemic modeling and data analysis

Human behavior and public attitudes towards preventive control measures, such as personal protection, screening, isolation, and vaccine acceptance, play a crucial role in shaping the course of a pandemic. These attitudes and behaviors are often influenced by various information sources, most prominently by social media platforms. The primary information usually comes from government bodies, e.g. CDC, responsible for public health mandates. However, social media can amplify, modify, or distort this information in numerous ways. The dual nature of social media can either raise awareness and encourage protective behaviors and reduce transmission, or have the opposite effect by spreading misinformation and fostering non-compliance. To analyze the interplay between these components, we have developed a coupled SIR-type dynamical model that integrates three essential components: (i) disease spread, as reported by official sources; (ii) the response of social media to this information; and (iii) the subsequent modification of human behavior, which directly impacts the spread of disease. To calibrate and validate our model, we utilized available data sources on the Covid-19 pandemic from a one-year period (2021-2022) in the United States, as well as data on social media responses, particularly tweets. By analyzing the data and conducting model simulations, we have identified significant inputs and parameters, such as initial compliance levels and behavioral transition rates. These factors enable a quantitative assessment of their contributions to disease outcomes, including cumulative outbreak size and its dynamic trajectory. This modeling approach gives some valuable insights into the relationship between public attitudes, information dissemination, and their impact on the progression of the pandemic. By understanding these dynamics, we can inform policy decisions, public health campaigns, and interventions to effectively combat the spread of Covid-like pathogens and future pandemics. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement NSF (FAIN): 2200255 ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors