Epidemic modelling of monitoring public behavior using surveys during pandemic-induced lockdowns
Abstract 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...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-06-01
|
| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-023-00310-z |
| _version_ | 1797806531699277824 |
|---|---|
| author | Andreas Koher Frederik Jørgensen Michael Bang Petersen Sune Lehmann |
| author_facet | Andreas Koher Frederik Jørgensen Michael Bang Petersen Sune Lehmann |
| author_sort | Andreas Koher |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-13T06:08:40Z |
| format | Article |
| id | doaj.art-a81e50880ee448cf8ce75b728fb2e77b |
| institution | Directory Open Access Journal |
| issn | 2730-664X |
| language | English |
| last_indexed | 2024-03-13T06:08:40Z |
| publishDate | 2023-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Medicine |
| spelling | doaj.art-a81e50880ee448cf8ce75b728fb2e77b2023-06-11T11:23:45ZengNature PortfolioCommunications Medicine2730-664X2023-06-013111010.1038/s43856-023-00310-zEpidemic modelling of monitoring public behavior using surveys during pandemic-induced lockdownsAndreas Koher0Frederik Jørgensen1Michael Bang Petersen2Sune Lehmann3DTU Compute, Technical University of DenmarkDepartment of Political Science, Aarhus UniversityDepartment of Political Science, Aarhus UniversityDTU Compute, Technical University of DenmarkAbstract 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.https://doi.org/10.1038/s43856-023-00310-z |
| spellingShingle | Andreas Koher Frederik Jørgensen Michael Bang Petersen Sune Lehmann Epidemic modelling of monitoring public behavior using surveys during pandemic-induced lockdowns Communications Medicine |
| title | Epidemic modelling of monitoring public behavior using surveys during pandemic-induced lockdowns |
| title_full | Epidemic modelling of monitoring public behavior using surveys during pandemic-induced lockdowns |
| title_fullStr | Epidemic modelling of monitoring public behavior using surveys during pandemic-induced lockdowns |
| title_full_unstemmed | Epidemic modelling of monitoring public behavior using surveys during pandemic-induced lockdowns |
| title_short | Epidemic modelling of monitoring public behavior using surveys during pandemic-induced lockdowns |
| title_sort | epidemic modelling of monitoring public behavior using surveys during pandemic induced lockdowns |
| url | https://doi.org/10.1038/s43856-023-00310-z |
| work_keys_str_mv | AT andreaskoher epidemicmodellingofmonitoringpublicbehaviorusingsurveysduringpandemicinducedlockdowns AT frederikjørgensen epidemicmodellingofmonitoringpublicbehaviorusingsurveysduringpandemicinducedlockdowns AT michaelbangpetersen epidemicmodellingofmonitoringpublicbehaviorusingsurveysduringpandemicinducedlockdowns AT sunelehmann epidemicmodellingofmonitoringpublicbehaviorusingsurveysduringpandemicinducedlockdowns |