457 citations · 604 across the 11 of their papers we have counts for
11 papers
Using digital traces to build prospective and real-time county-level early warning systems to anticipate COVID-19 outbreaks in the United States
Lucas M. Stolerman, Leonardo Clemente, Canelle Poirier +5
The ongoing COVID-19 pandemic continues to affect communities around the world. To date, almost 6 million people have died as a consequence of COVID-19, and more than one-quarter o…
High-resolution Spatio-temporal Model for County-level COVID-19 Activity in the U.S
Shixiang Zhu, Alexander Bukharin, Liyan Xie +3
We present an interpretable high-resolution spatio-temporal model to estimate COVID-19 deaths together with confirmed cases one-week ahead of the current time, at the county-level…
Fever and mobility data indicate social distancing has reduced incidence of communicable disease in the United States
Parker Liautaud, Peter Huybers, Mauricio Santillana
In March of 2020, many U.S. state governments encouraged or mandated restrictions on social interactions to slow the spread of COVID-19, the disease caused by the novel coronavirus…
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
Dianbo Liu, Leonardo Clemente, Canelle Poirier +5
We present a timely and novel methodology that combines disease estimates from mechanistic models with digital traces, via interpretable machine-learning methodologies, to reliably…
Towards the Use of Neural Networks for Influenza Prediction at Multiple Spatial Resolutions
Emily L. Aiken, Andre T. Nguyen, Mauricio Santillana
We introduce the use of a Gated Recurrent Unit (GRU) for influenza prediction at the state- and city-level in the US, and experiment with the inclusion of real-time flu-related Int…
Advances in using Internet searches to track dengue
Shihao Yang, S. C. Kou, Fred Lu +3
Dengue is a mosquito-borne disease that threatens more than half of the world's population. Despite being endemic to over 100 countries, government-led efforts and mechanisms to ti…
Relatedness of the Incidence Decay with Exponential Adjustment (IDEA) Model, "Farr's Law" and Compartmental Difference Equation SIR Models
Mauricio Santillana, Ashleigh Tuite, Tahmina Nasserie +5
Mathematical models are often regarded as recent innovations in the description and analysis of infectious disease outbreaks and epidemics, but simple models have been in use for p…
Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance
Mauricio Santillana, Andre T. Nguyen, Mark Dredze +2
We present a machine learning-based methodology capable of providing real-time ("nowcast") and forecast estimates of influenza activity in the US by leveraging data from multiple d…
Estimating numerical errors due to operator splitting in global atmospheric chemistry models: Transport and chemistry
Mauricio Santillana, Ling Zhang, Robert Yantosca
We present upper bounds for the numerical errors introduced when using operator splitting methods to integrate transport and non-linear chemistry processes in global chemical trans…
Quantifying the loss of information in source attribution problems using the adjoint method in global models of atmospheric chemical transport
Mauricio Santillana
It is of crucial importance to be able to identify the location of atmospheric pollution sources in our planet. Global models of atmospheric transport in combination with diverse E…
Gradient-based estimation of Manning's friction coefficient from noisy data
Victor M. Calo, Nathan Collier, Matthias Gehre +2
We study the numerical recovery of Manning's roughness coefficient for the diffusive wave approximation of the shallow water equation. We describe a conjugate gradient method for t…