39 citations · 66 across the 8 of their papers we have counts for
8 papers
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…
Models for COVID-19 Pandemic: A Comparative Analysis
Aniruddha Adiga, Devdatt Dubhashi, Bryan Lewis +3
COVID-19 pandemic represents an unprecedented global health crisis in the last 100 years. Its economic, social and health impact continues to grow and is likely to end up as one of…
Generating a Heterosexual Bipartite Network Embedded in Social Network
Asma Azizi, Zhuolin Qu, Bryan Lewis +1
We describe how to generate a heterosexual network with a prescribed joint-degree distribution that is embedded in a prescribed large-scale social contact network. The structure of…
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…
Guided Deep List: Automating the Generation of Epidemiological Line Lists from Open Sources
Saurav Ghosh, Prithwish Chakraborty, Bryan L. Lewis +5
Real-time monitoring and responses to emerging public health threats rely on the availability of timely surveillance data. During the early stages of an epidemic, the ready availab…