activity
20132026
most citedThe challenges of modeling and forecasting the spread of COVID-19

614 citations · 780 across the 14 of their papers we have counts for

collaborators
Showing 2020Show all

6 papers · 1 filter

stat.AP2020★ 1 cited

A Fully Bayesian, Logistic Regression Tracking Algorithm for Mitigating Disparate Misclassification

Martin B. Short, George O. Mohler

We develop a fully Bayesian, logistic tracking algorithm with the purpose of providing classification results that are unbiased when applied uniformly to individuals with differing…

stat.ML2020★ 10 cited

Point Process Modeling of Drug Overdoses with Heterogeneous and Missing Data

Xueying Liu, Jeremy Carter, Brad Ray +1

Opioid overdose rates have increased in the United States over the past decade and reflect a major public health crisis. Modeling and prediction of drug and opioid hotspots, where…

cs.SI2020★ 48 cited

Dynamic topic modeling of the COVID-19 Twitter narrative among U.S. governors and cabinet executives

Hao Sha, Mohammad Al Hasan, George Mohler +1

A combination of federal and state-level decision making has shaped the response to COVID-19 in the United States. In this paper we analyze the Twitter narratives around this decis…

q-bio.PE2020★ 614 cited

The challenges of modeling and forecasting the spread of COVID-19

Andrea L. Bertozzi, Elisa Franco, George Mohler +2

We present three data driven model-types for COVID-19 with a minimal number of parameters to provide insights into the spread of the disease that may be used for developing policy…

q-bio.PE2020★ 5 cited

Analyzing the World-Wide Impact of Public Health Interventions on the Transmission Dynamics of COVID-19

George Mohler, Frederic Schoenberg, Martin B. Short +1

We analyze changes in the reproduction number, R, of COVID-19 in response to public health interventions. Our results indicate that public health measures undertaken in China reduc…

cs.LG2020★ 1 cited

Hawkes Process Multi-armed Bandits for Disaster Search and Rescue

Wen-Hao Chiang, George Mohler

We propose a novel framework for integrating Hawkes processes with multi-armed bandit algorithms to solve spatio-temporal event forecasting and detection problems when data may be…