Time-Series Clustering for Home Dwell Time during COVID-19: What Can We Learn from It?
This article has been Reviewed by the following groups
Discuss this preprint
Start a discussion What are Sciety discussions?Listed in
- Evaluated articles (ScreenIT)
Abstract
In this study, we investigate the potential driving factors that lead to the disparity in the time-series of home dwell time in a data-driven manner, aiming to provide fundamental knowledge that benefits policy-making for better mitigation strategies of future pandemics. Taking Metro Atlanta as a study case, we perform a trend-driven analysis by conducting Kmeans time-series clustering using fine-grained home dwell time records from SafeGraph. Furthermore, we apply ANOVA (Analysis of Variance) coupled with post-hoc Tukey’s test to assess the statistical difference in sixteen recoded demographic/socioeconomic variables (from ACS 2014–2018 estimates) among the identified time-series clusters. We find that demographic/socioeconomic variables can explain the disparity in home dwell time in response to the stay-at-home order, which potentially leads to disparate exposures to the risk from the COVID-19. The results further suggest that socially disadvantaged groups are less likely to follow the order to stay at home, pointing out the extensive gaps in the effectiveness of social distancing measures that exist between socially disadvantaged groups and others. Our study reveals that the long-standing inequity issue in the U.S. stands in the way of the effective implementation of social distancing measures.
Article activity feed
-
SciScore for 10.1101/2020.09.27.20202671: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:6.2 Limitations and future directions: It is important to mention several limitations of this study and provide guidelines for future directions. First, we acknowledge the subjectivity of predefining the number of clusters in the Kmeans clustering algorithm. In this study, we set the number of clusters as three (i.e., k = 3) via the …
SciScore for 10.1101/2020.09.27.20202671: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:6.2 Limitations and future directions: It is important to mention several limitations of this study and provide guidelines for future directions. First, we acknowledge the subjectivity of predefining the number of clusters in the Kmeans clustering algorithm. In this study, we set the number of clusters as three (i.e., k = 3) via the investigation and interpretation of the home dwell time records from SafeGraph. We notice that, even after the preprocessing, some CBGs still present unstable temporal patterns due to the low and varying daily device count. Our interpretation of the data records reveals three distinct temporal patterns with a strong, moderate, and unnoticeable increase in home dwell time during March and April (hence, k is predefined as 3). To ensure the interpretability of clusters, the selection of the number of clusters in Kmeans via prior knowledge (priori) is common. However, we acknowledge that approaches like Elbow Curve [46] and Silhouette analysis [47] are largely adopted to facilitate the optimization of k without prior knowledge. When conducting a cross- city comparison or reproducing our approach in another region, we advise re-investigating the pattern of the time-series or adopting the aforementioned approaches to derive a reasonable setting of k. Second, we construct and cluster the time-series of home dwell time using the data in the year 2020 (January 1 to Aug 31), without considering the changes in time-series compared to the previous year. It is...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on page 4. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
-
-
