What is it about?
In our paper, we take a step towards understanding the utility of anonymized geolocation data collected from mobile users in answering the dynamics of pandemic spread. We extract metrics corresponding to interaction of individuals from the anonymized geolocation data in 50 land grant university counties in the US. We find that, in spite of the limitations of such data sets, e.g. noise, low coverage, simple mobility metrics are highly correlated with the spread of COVID-19 during certain periods of the pandemic. Moreover, we find that these metrics can be used to predict a possible surge in the number of infections in the next 2-3 weeks with relatively high accuracy.
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Why is it important?
The insights can be used by policy makers to make more informed decisions pertaining to social interventions to better contain the pandemic. University leaders can take proactive steps to reduce the spread by reducing large scale social interactions during the early phase of a pandemic.
Read the Original
This page is a summary of: Evaluating the Utility of High-Resolution Proximity Metrics in Predicting the Spread of COVID-19, ACM Transactions on Spatial Algorithms and Systems, December 2022, ACM (Association for Computing Machinery),
DOI: 10.1145/3531006.
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