This paper studies 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 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 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 relative to the benchmark.Competing Interest StatementThe authors have declared no competing interest.Funding StatementThis work was supported by the South African Reserve Bank to study contagion in economic and financial systems.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:Was not necessary for this simulation study that uses public data only.All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as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).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll data and scripts are available on Github https://github.com/blackrhinoabm/sabcom