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.