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Social multipliers and the Covid-19 epidemic: Analysis through constrained maximum entropy modeling

Abstract: Social multipliers occur when individual actions influence other individual actions so as to lead to amplified aggregate effects. Epidemic infections offer a dramatic example of this phenomenon since individual actions such as social distancing and masking that have small effects on individuals' risk can have very large effects in reducing risk when they are widely adopted. This paper uses the info-metric method of constrained maximum entropy modeling to estimate the impact of social multiplier effects in the Covid-19 epidemic with a model that infers the length of infection, the rate of mortality, the base infection factor, and reductions in the infection factor due to changes in social behavior from data on daily infections and deaths. While patterns are not universal over the sample of country data, they strongly support the conclusion that changes in social behavior are the primary factor influencing the dynamics of epidemics.