Name
Abstract | ||
|---|---|---|
Epidemics of infectious diseases are among the largest threats to the quality of life and the economic and social well-being of developing countries. The arsenal of measures against such epidemics is well-established, but costly and insufficient to mitigate their impact. In this paper, we argue that mobile technology adds a powerful weapon to this arsenal, because (a) mobile devices endow us with the unprecedented ability to measure and model the detailed behavioral patterns of the affected population, and (b) they enable the delivery of personalized behavioral recommendations to individuals in real time. We combine these two ideas and propose several strategies to generate such recommendations from mobility patterns. The goal of each strategy is a large reduction in infections, with a small impact on the normal course of daily life. We evaluate these strategies over the Orange D4D dataset and show the benefit of mobile micro-measures, even if only a fraction of the population participates. These preliminary results demonstrate the potential of mobile technology to complement other measures like vaccination and quarantines against disease epidemics. |
Year | Venue | Field |
|---|---|---|
2013 | CoRR | Mobile technology,Population,Behavioral pattern,Disease,Computer science,Operations research,Developing country,Risk analysis (engineering),Mobile device,Artificial intelligence,Machine learning |
DocType | Volume | Citations |
Journal | abs/1307.2084 | 4 |
PageRank | References | Authors |
0.43 | 13 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohamed Kafsi | 1 | 31 | 2.76 |
| Ehsan Kazemi | 2 | 66 | 8.37 |
| Lucas Maystre | 3 | 36 | 7.28 |
| Lyudmila Yartseva | 4 | 56 | 2.96 |
| M. Grossglauser | 5 | 2965 | 448.13 |
| Patrick Thiran | 6 | 2712 | 217.24 |