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Implementing a lockdown for disease mitigation is a balancing act:Non-pharmaceutical interventions can reduce disease transmission significantly,but interventions also have considerable societal costs. Therefore,decision-makers need near real-time information to calibrate the level ofrestrictions. We fielded daily surveys in Denmark during the second wave of theCOVID-19 pandemic to monitor public response to the announced lockdown. A keyquestion asked respondents to state their number of close contacts within thepast 24 hours. Here, we establish a link between survey data, mobility data,and hospitalizations via epidemic modelling. Using Bayesian analysis, we thenevaluate the usefulness of survey responses as a tool to monitor the effects oflockdown and then compare the predictive performance to that of mobility data.We find that, unlike mobility, self-reported contacts decreased significantlyin all regions before the nation-wide implementation of non-pharmaceuticalinterventions and improved predicting future hospitalizations compared tomobility data. A detailed analysis of contact types indicates that contact withfriends and strangers outperforms contact with colleagues and family members(outside the household) on the same prediction task. Representative surveysthus qualify as a reliable, non-privacy invasive monitoring tool to track theimplementation of non-pharmaceutical interventions and study potentialtransmission paths.

Monitoring Public Behavior During a Pandemic Using Surveys: Proof-of-Concept Via Epidemic Modelling