11 citations · 22 across the 5 of their papers we have counts for
5 papers
Population-aware Hierarchical Bayesian Domain Adaptation via Multiple-component Invariant Learning
Vishwali Mhasawade, Nabeel Abdur Rehman, Rumi Chunara
While machine learning is rapidly being developed and deployed in health settings such as influenza prediction, there are critical challenges in using data from one environment in…
Deep Landscape Features for Improving Vector-borne Disease Prediction
Nabeel Abdur Rehman, Umar Saif, Rumi Chunara
The global population at risk of mosquito-borne diseases such as dengue, yellow fever, chikungunya and Zika is expanding. Infectious disease models commonly incorporate environment…
Population-aware Hierarchical Bayesian Domain Adaptation
Vishwali Mhasawade, Nabeel Abdur Rehman, Rumi Chunara
Population attributes are essential in health for understanding who the data represents and precision medicine efforts. Even within disease infection labels, patients can exhibit s…
Domain Adaptation for Infection Prediction from Symptoms Based on Data from Different Study Designs and Contexts
Nabeel Abdur Rehman, Maxwell Matthaios Aliapoulios, Disha Umarwani +1
Acute respiratory infections have epidemic and pandemic potential and thus are being studied worldwide, albeit in many different contexts and study formats. Predicting infection fr…
Creating Full Individual-level Location Timelines from Sparse Social Media Data
Nabeel Abdur Rehman, Kunal Relia, Rumi Chunara
In many domain applications, a continuous timeline of human locations is critical; for example for understanding possible locations where a disease may spread, or the flow of traff…