Publications

High-resolution Spatio-temporal Model for County-level COVID-19 Activity in the U.S

Author(s): Shixiang Zhu, Alexander Bukharin, Liyan Xie, Mauricio Santillana, Shihao Yang, Yao Xie|Journal: ArXiv| September 2020

We present an interpretable high-resolution spatio-temporal model to estimate COVID-19 deaths together with confirmed cases one-week ahead of the current…

Tracking disease outbreaks from sparse data with Bayesian inference

Author(s): Bryan Wilder, Michael J Mina, Milind Tambe|Journal: ArXiv| September 2020

The COVID-19 pandemic provides new motivation for a classic problem in epidemiology: estimating the empirical rate of transmission during an…

Pixelate to communicate: visualising uncertainty in maps of disease risk and other spatial continua

Author(s): Aimee R Taylor, James A Watson, Caroline O Buckee|Journal: ArXiv| May 2020

Maps have long been been used to visualise estimates of spatial variables, in particular disease burden and risk. Predictions made…

Fever and mobility data indicate social distancing has reduced incidence of communicable disease in the United States

Author(s): Parker Liautaud, Peter Huybers, Mauricio Santillana|Journal: ArXiv| April 2020

In March of 2020, many U.S. state governments encouraged or mandated restrictions on social interactions to slow the spread of…

A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models.

Author(s): Liu D, Clemente L, Poirier C, Ding X, Chinazzi M, Vespignani A, Santillana M|Journal: ArXiv|PMID: 32550248| April 2020

We present a timely and novel methodology that combines disease estimates from mechanistic models with digital traces, via interpretable machine-learning…