Integrating psychosocial variables and societal diversity in epidemic models for predicting COVID-19 transmission dynamics

  1. Viktor K. Jirsa
  2. Spase Petkoski
  3. Huifang Wang
  4. Marmaduke Woodman
  5. Jan Fousek
  6. Cornelia Betsch
  7. Lisa Felgendreff
  8. Robert Bohm
  9. Lau Lilleholt
  10. Ingo Zettler
  11. Sarah Faber
  12. Kelly Shen
  13. Anthony Randal Mcintosh

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Abstract

During the current COVID-19 pandemic, governments must make decisions based on a variety of information including estimations of infection spread, health care capacity, economic and psychosocial considerations. The disparate validity of current short-term forecasts of these factors is a major challenge to governments. By causally linking an established epidemiological spread model with dynamically evolving psychosocial variables, using Bayesian inference we estimate the strength and direction of these interactions for German and Danish data of disease spread, human mobility, and psychosocial factors based on the serial cross-sectional COVID-19 Snapshot Monitoring (COSMO; N = 16,981). We demonstrate that the strength of cumulative influence of psychosocial variables on infection rates is of a similar magnitude as the influence of physical distancing. We further show that the efficacy of political interventions to contain the disease strongly depends on societal diversity, in particular group-specific sensitivity to affective risk perception. As a consequence, the model may assist in quantifying the effect and timing of interventions, forecasting future scenarios, and differentiating the impact on diverse groups as a function of their societal organization. Importantly, the careful handling of societal factors, including support to the more vulnerable groups, adds another direct instrument to the battery of political interventions fighting epidemic spread.

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  1. Version published to 10.1371/journal.pdig.0000098
  2. ScreenIT

    SciScore for 10.1101/2020.08.12.20173252: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

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    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

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    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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    • No protocol registration statement was detected.

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  3. Version published to 10.1101/2020.08.12.20173252 on medRxiv