Epidemic modelling of monitoring public behavior using surveys during pandemic-induced lockdowns
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BACKGROUND: 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 of restrictions.
METHODS: We fielded daily surveys in Denmark during the second wave of the COVID-19 pandemic to monitor public response to the announced lockdown. A key question asked respondents to state their number of close contacts within the past 24 hours. Here, we establish a link between survey data, mobility data, and hospitalizations via epidemic modelling of a short time-interval around Denmark's December 2020 lockdown. Using Bayesian analysis, we then evaluate the usefulness of survey responses as a tool to monitor the effects of lockdown and then compare the predictive performance to that of mobility data.
RESULTS: We find that, unlike mobility, self-reported contacts decreased significantly in all regions before the nation-wide implementation of non-pharmaceutical interventions and improved predicting future hospitalizations compared to mobility data. A detailed analysis of contact types indicates that contact with friends and strangers outperforms contact with colleagues and family members (outside the household) on the same prediction task.
CONCLUSIONS: Representative surveys thus qualify as a reliable, non-privacy invasive monitoring tool to track the implementation of non-pharmaceutical interventions and study potential transmission paths.
Originalsprog | Engelsk |
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Artikelnummer | 80 |
Tidsskrift | Communications Medicine |
Vol/bind | 3 |
Udgave nummer | 1 |
Antal sider | 10 |
ISSN | 2730-664X |
DOI | |
Status | Udgivet - 2023 |
Bibliografisk note
© 2023. The Author(s).
ID: 371557911