Social learning in a network model of Covid-19

This paper investigates the effects of social learning on the transmission of Covid-19 in a network model. We calibrate our model to detailed data for Cape Town, South Africa and show that the inclusion of social learning improves the prediction of excess fatalities, reducing the best-fit squared difference from 19.34 to 11.40. The inclusion of social learning both flattens and shortens the curves for infections, hospitalizations, and excess fatalities, which is qualitatively different from flattening the curve by reducing the contact rate or transmission probability through non-pharmaceutical interventions. While social learning reduces infections, this alone is not sufficient to curb the spread of the virus because learning is slower than the rate at which the disease spreads. We use our model to study the efficacy of different vaccination strategies and find that vaccinating vulnerable groups first leads to a 72% reduction in fatalities and a 5% increase in total infections compared to a random-order benchmark. By contrast, using a contact-based vaccination strategy reduces infections by only 0.9% but results in 42% more fatalities than the benchmark.