6 citations · 20 across the 11 of their papers we have counts for
12 papers · 1 filter
Beyond Time Series: Spatial Reasoning for Epidemic Forecasting via Multimodal Learning
Diana Guadalupe Gomez, Chenwei Wu, Zhiyi Wang +2
Epidemic forecasting models typically rely on surveillance data reported over administrative regions, treating them as atomic units, thereby obscuring sub-regional spatial structur…
Modular Deep-Learning-Based Early Warning System for Deadly Heatwave Prediction
Shangqing Xu, Zhiyuan Zhao, Megha Sharma +4
Severe heatwaves in urban areas significantly threaten public health, calling for establishing early warning strategies. Despite predicting occurrence of heatwaves and attributing…
When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting
Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez +2
Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting, where the goal is to model and forecast multivariate time-series that have un…
Data-Centric Epidemic Forecasting: A Survey
Alexander Rodríguez, Harshavardhan Kamarthi, Pulak Agarwal +4
The COVID-19 pandemic has brought forth the importance of epidemic forecasting for decision makers in multiple domains, ranging from public health to the economy as a whole. While…
Differentiable Agent-based Epidemiology
Ayush Chopra, Alexander Rodríguez, Jayakumar Subramanian +4
Mechanistic simulators are an indispensable tool for epidemiology to explore the behavior of complex, dynamic infections under varying conditions and navigate uncertain environment…
When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting
Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez +2
Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting, where the goal is to model and forecast multivariate time-series that have un…
EINNs: Epidemiologically-informed Neural Networks
Alexander Rodríguez, Jiaming Cui, Naren Ramakrishnan +2
We introduce EINNs, a framework crafted for epidemic forecasting that builds upon the theoretical grounds provided by mechanistic models as well as the data-driven expressibility a…
CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting
Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez +2
Probabilistic time-series forecasting enables reliable decision making across many domains. Most forecasting problems have diverse sources of data containing multiple modalities an…
Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future
Harshavardhan Kamarthi, Alexander Rodríguez, B. Aditya Prakash
In real-time forecasting in public health, data collection is a non-trivial and demanding task. Often after initially released, it undergoes several revisions later (maybe due to h…
When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting
Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez +2
Accurate and trustworthy epidemic forecasting is an important problem that has impact on public health planning and disease mitigation. Most existing epidemic forecasting models di…
Incorporating Expert Guidance in Epidemic Forecasting
Alexander Rodríguez, Bijaya Adhikari, Naren Ramakrishnan +1
Forecasting influenza like illnesses (ILI) has rapidly progressed in recent years from an art to a science with a plethora of data-driven methods. While these methods have achieved…
Mapping Network States Using Connectivity Queries
Alexander Rodríguez, Bijaya Adhikari, Andrés D. González +3
Can we infer all the failed components of an infrastructure network, given a sample of reachable nodes from supply nodes? One of the most critical post-disruption processes after a…