Abstract The objective of this study is to examine the transmission risk of COVID-19 based on cross-county population co-location data from Facebook. The rapid spread of COVID-19 in the United States has imposed a major threat to public health, the real economy, and human well-being. With the absence of effective vaccines, the preventive actions of social distancing, travel reduction and stay-at-home orders are recognized as essential non-pharmacologic approaches to control the infection and spatial spread of COVID-19. Prior studies demonstrated that human movement and mobility drove the spatiotemporal distribution of COVID-19 in China. Little is known, however, about the patternsmore »
This content will become publicly available on June 10, 2022
Evaluating the utility of high-resolution proximity metrics in predicting the spread of COVID-19
High resolution mobility datasets have become increasingly available in the past few years and have
enabled detailed models for infectious disease spread including those for COVID-19. However, there
are open questions on how such a mobility data can be used effectively within epidemic models and
for which tasks they are best suited. In this paper, we extract a number of graph-based proximity
metrics from high resolution cellphone trace data from X-Mode and use it to study COVID-19
epidemic spread in 50 land grant university counties in the US. We present an approach to estimate
the effect of mobility on cases by fitting an ODE based model and performing multivariate linear
regression to explain the estimated time varying transmissibility. We find that, while mobility plays a
significant role, the contribution is heterogeneous across the counties, as exemplified by a subsequent
correlation analysis. We subsequently evaluate the metrics’ utility for case surge prediction defined
as a supervised classification problem, and show that the learnt model can predict surges with 95%
accuracy and 87% F1-score.
- Publication Date:
- NSF-PAR ID:
- 10313657
- Journal Name:
- ArXivorg
- ISSN:
- 2331-8422
- Sponsoring Org:
- National Science Foundation
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