Mining Enormous Mobile Datasets to Improve Mitigation Strategies for Limiting the Spread of Infectious Disease
2014
- 28Usage
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage28
- Abstract Views28
Article Description
We secured access to a dataset containing the entire anonymized call detail records (phone/text) of 5 million mobile phone subscribers in Cote d’Ivoire, tracked over a 5-month period. The goal of this work-in-progress is to analyze the dataset for information that could help public health officials develop more effective strategies for limiting the spread of infectious disease. Using antenna (cell tower) proximity data to situate subscribers, clustering algorithms were applied to identify groups of individuals expressing similar mobility patterns. Incorporating this knowledge of dynamic population densities could lead to better-informed quarantine/isolation decisions.
Bibliographic Details
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know