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    Human behavior can slow epidemics through self-regulation, a phenomenon called behavioral autorepression. This study provides evidence for behavioral autorepression during COVID-19, improving epidemic forecasting and intervention strategies.

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    Area of Science:

    • Epidemiology
    • Mathematical Modeling
    • Behavioral Science

    Background:

    • Epidemiological forecasting models often omit human behavioral responses due to a lack of empirical evidence.
    • Behavioral autorepression, where population behavior self-regulates epidemic spread, has been hypothesized but not empirically demonstrated.
    • This omission can lead to significant inaccuracies in epidemic forecasting and intervention planning.

    Purpose of the Study:

    • To provide empirical evidence for behavioral autorepression during the COVID-19 pandemic.
    • To quantify the strength of behavioral autorepression across different populations.
    • To assess the impact of behavioral autorepression on epidemic forecasting and the efficacy of non-pharmaceutical interventions.

    Main Methods:

    • Analysis of early COVID-19 dynamics across 96 countries, 50 US states, and 562 US counties.
    • Identification of behavioral autorepression signatures through mobility-death correlations, fluctuation-magnitude analysis, and infection wave dynamics.
    • Incorporation of behavioral autorepression into conventional epidemiological models for forecasting and analysis.

    Main Results:

    • Evidence for behavioral autorepression was identified across multiple scales using distinct analytical approaches.
    • Accurate forecasting of COVID-19 mortality required the inclusion of behavioral autorepression in models.
    • The strength of behavioral autorepression was found to influence the effectiveness of non-pharmaceutical interventions.

    Conclusions:

    • Behavioral autorepression is a significant factor in regulating epidemic dynamics, as evidenced by COVID-19 data.
    • Incorporating behavioral autorepression into models substantially improves epidemic forecasting accuracy.
    • Understanding and quantifying behavioral autorepression can lead to more effective public health interventions and tailored strategies for future epidemics.