39 citations · 67 across the 9 of their papers we have counts for
10 papers
Boundary Degree as a Node-level Feature for Epidemic Scenario Identification in Agent-based Cascade Simulations
Amro Alabsi Aljundi, Galen Harrison, Jiangzhuo Chen +3
Characterizing the scenario underlying an epidemic from its disease cascade is an important task in simulation analytics. We propose boundary degree, the count of an infected node'…
Pandemics In Silico: Scaling an Agent-Based Simulation on Realistic Social Contact Networks
Joy Kitson, Ian Costello, Jiangzhuo Chen +7
Preventing the spread of infectious diseases requires implementing interventions at various levels of government and evaluating the potential impact and efficacy of those preemptiv…
Cohorting to isolate asymptomatic spreaders: An agent-based simulation study on the Mumbai Suburban Railway
Alok Talekar, Sharad Shriram, Nidhin Vaidhiyan +10
The Mumbai Suburban Railways, \emph{locals}, are a key transit infrastructure of the city and is crucial for resuming normal economic activity. To reduce disease transmission, poli…
Examining Deep Learning Models with Multiple Data Sources for COVID-19 Forecasting
Lijing Wang, Aniruddha Adiga, Srinivasan Venkatramanan +3
The COVID-19 pandemic represents the most significant public health disaster since the 1918 influenza pandemic. During pandemics such as COVID-19, timely and reliable spatio-tempor…
Wisdom of the Ensemble: Improving Consistency of Deep Learning Models
Lijing Wang, Dipanjan Ghosh, Maria Teresa Gonzalez Diaz +5
Deep learning classifiers are assisting humans in making decisions and hence the user's trust in these models is of paramount importance. Trust is often a function of constant beha…
Data-driven modeling for different stages of pandemic response
Aniruddha Adiga, Jiangzhuo Chen, Madhav Marathe +3
Some of the key questions of interest during the COVID-19 pandemic (and all outbreaks) include: where did the disease start, how is it spreading, who is at risk, and how to control…
TDEFSI: Theory Guided Deep Learning Based Epidemic Forecasting with Synthetic Information
Lijing Wang, Jiangzhuo Chen, Madhav Marathe
Influenza-like illness (ILI) places a heavy social and economic burden on our society. Traditionally, ILI surveillance data is updated weekly and provided at a spatially coarse res…
Learning Everywhere: Pervasive Machine Learning for Effective High-Performance Computation
Geoffrey Fox, James A. Glazier, JCS Kadupitiya +10
The convergence of HPC and data-intensive methodologies provide a promising approach to major performance improvements. This paper provides a general description of the interaction…
Calibrating a Stochastic Agent Based Model Using Quantile-based Emulation
Arindam Fadikar, Dave Higdon, Jiangzhuo Chen +3
In a number of cases, the Quantile Gaussian Process (QGP) has proven effective in emulating stochastic, univariate computer model output (Plumlee and Tuo, 2014). In this paper, we…
A Framework for Evaluating Epidemic Forecasts
Farzaneh Sadat Tabataba, Prithwish Chakraborty, Naren Ramakrishnan +4
Background: Over the past few decades, numerous forecasting methods have been proposed in the field of epidemic forecasting. Such methods can be classified into different categorie…