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Research Article
Lasse Hyldig Hansen
Aarhus University
Corresponding Author
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This work is licensed under a CC BY 4.0 License. Read Full License
It is crucial to understand and learn as much as possible from the current global Sars-CoV-2 pandemic for the sake of future precautions. Apart from strong government restrictions such as complete lockdowns, curfews, and mask mandates, other factors influence viral transmission.
Since June 2020, Denmark has had an extensive test and surveillance program and made data publicly available at the municipality level. Here we use these data and integrate publicly available data on government restrictions, weather data, and mobility data to model COVID-19 incidence in 98 Danish municipalities from September 2020 to February 2021.
The inclusion of municipality heterogeneity, weather and mobility data increases the amount of variance explained by ~29% compared to a simpler model taking only incidence and restrictions into account. We found a strong and significant effect from temperature which interacts with government restrictions. Our results indicate that higher temperatures limit viral transmission when government restrictions are low, but that the temperature effect diminishes under stronger restrictions. This is most likely due to a change in human behavior rather than a biological effect.
Likewise, we found that changes in residential mobility were significant factors that also interacted with restrictions. When restrictions were strong, we found that increased residential mobility resulted in decreased COVID-19 incidence, suggesting residential mobility as a proxy for compliance.
Our results show the increased explanatory power of integrating different variables when modeling COVID-19 incidence. The weather seems to predict human behavior in a quite predictable way and mobility data could be used to measure current compliance with government restrictions.
Keywords
Social Compliance, High Stringency Periods, Reduces COVID-19 Incidence, Google Mobility Reports, global Sars-CoV-2 pandemic
Figure 1
Figure 2
No competing interests reported.
This is a list of supplementary files associated with this preprint. Click to download.
- FigureS1.jpeg
Figure S1; Observed and predicted COVID-19 incidence (smoothed cases per 100,000 people) for all Danish municipalities in the period of analysis. Observed incidence (red line), Null model predictions (blue line). The grey shading around the lines is the 95% confidence interval.
- FigureS2.jpeg
Figure S2. Conditional interaction plots of three interaction effects from the model. Figure a: the interaction effect of incidence lagged on incidence that depends on the stringency index. Figure b: the interaction effect of precipitation on incidence that depends on the stringency index. Figure c: the interaction effect of transit mobility change on incidence that depends on the stringency index. Incidence lagged, Precipitation and Transit Mobility Change are on a z-scale, and varies between -1 (1 SD below average) and 1 (1 SD above average). The same is the case for stringency, where 3 conditions of stringency are visualized (1 SD below average, average, and 1 SD above average). The y-variable is the conditional average COVID-19 incidence given the parameters of the model (predicted values given our model).
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A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research Article
Lasse Hyldig Hansen
Aarhus University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 13 May, 2021
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
It is crucial to understand and learn as much as possible from the current global Sars-CoV-2 pandemic for the sake of future precautions. Apart from strong government restrictions such as complete lockdowns, curfews, and mask mandates, other factors influence viral transmission.
Since June 2020, Denmark has had an extensive test and surveillance program and made data publicly available at the municipality level. Here we use these data and integrate publicly available data on government restrictions, weather data, and mobility data to model COVID-19 incidence in 98 Danish municipalities from September 2020 to February 2021.
The inclusion of municipality heterogeneity, weather and mobility data increases the amount of variance explained by ~29% compared to a simpler model taking only incidence and restrictions into account. We found a strong and significant effect from temperature which interacts with government restrictions. Our results indicate that higher temperatures limit viral transmission when government restrictions are low, but that the temperature effect diminishes under stronger restrictions. This is most likely due to a change in human behavior rather than a biological effect.
Likewise, we found that changes in residential mobility were significant factors that also interacted with restrictions. When restrictions were strong, we found that increased residential mobility resulted in decreased COVID-19 incidence, suggesting residential mobility as a proxy for compliance.
Our results show the increased explanatory power of integrating different variables when modeling COVID-19 incidence. The weather seems to predict human behavior in a quite predictable way and mobility data could be used to measure current compliance with government restrictions.
Figure 1
Figure 2
No competing interests reported.
This is a list of supplementary files associated with this preprint. Click to download.
- FigureS1.jpeg
Figure S1; Observed and predicted COVID-19 incidence (smoothed cases per 100,000 people) for all Danish municipalities in the period of analysis. Observed incidence (red line), Null model predictions (blue line). The grey shading around the lines is the 95% confidence interval.
- FigureS2.jpeg
Figure S2. Conditional interaction plots of three interaction effects from the model. Figure a: the interaction effect of incidence lagged on incidence that depends on the stringency index. Figure b: the interaction effect of precipitation on incidence that depends on the stringency index. Figure c: the interaction effect of transit mobility change on incidence that depends on the stringency index. Incidence lagged, Precipitation and Transit Mobility Change are on a z-scale, and varies between -1 (1 SD below average) and 1 (1 SD above average). The same is the case for stringency, where 3 conditions of stringency are visualized (1 SD below average, average, and 1 SD above average). The y-variable is the conditional average COVID-19 incidence given the parameters of the model (predicted values given our model).
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