arXiv (All)
open access
Empirical Trajectory Sparsity Biases Mobility-Informed Epidemic Modeling
GPS mobility data are increasingly used in epidemic modeling, allowing the construction of co-location networks or population flows. These trajectories typically exhibit high temporal sparsity because data collection is opportunistic and tied to phone use. Despite growing awareness of this limitatio
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Intermittent quarantine suppresses epidemic spreading beyond simple contact reduction#1155364
- Stochastic epidemic models on random networks: casual contacts, clustering and vaccination#649219
- Use of Topological Data Analysis for the Detection of Phenomenological Bifurcations in Stochastic Epidemiological Models#1109757
- Walk based Laplacians for Modeling Diffusion on Complex Networks#792690
- Walk based Laplacians for Modeling Diffusion on Complex Networks#788690
Record · ID 1062914
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive