Epidemic modelling of monitoring public behavior using surveys during pandemic-induced lockdowns
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Epidemic modelling of monitoring public behavior using surveys during pandemic-induced lockdowns. / Koher, Andreas; Jørgensen, Frederik; Petersen, Michael Bang; Lehmann, Sune.
In: Communications Medicine, Vol. 3, No. 1, 80, 2023.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Epidemic modelling of monitoring public behavior using surveys during pandemic-induced lockdowns
AU - Koher, Andreas
AU - Jørgensen, Frederik
AU - Petersen, Michael Bang
AU - Lehmann, Sune
N1 - © 2023. The Author(s).
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
U2 - 10.1038/s43856-023-00310-z
DO - 10.1038/s43856-023-00310-z
M3 - Journal article
C2 - 37291090
VL - 3
JO - Communications Medicine
JF - Communications Medicine
SN - 2730-664X
IS - 1
M1 - 80
ER -
ID: 371557911