COFFEE: COVID-19 Forecasts using Fast Evaluations and Estimation

10/04/2021

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This document details the methodology of the Los Alamos National Laboratory COVID-19 forecasting model, COFFEE (COVID-19 Forecasts using Fast Evaluations and Estimation).

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COFFEE: COVID-19 Forecasts using Fast Evaluations and Estimation

Estimation of the incubation time distribution for COVID-19

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EpiCovDA: a mechanistic COVID-19 forecasting model with data assimilation

Interpretable Sequence Learning for COVID-19 Forecasting

Curating a COVID-19 data repository and forecasting county-level death counts in the United States

Assessment of COVID-19 hospitalization forecasts from a simplified SIR model

Smooth multi-period forecasting with application to prediction of COVID-19 cases

COFFEE: COVID-19 Forecasts using Fast Evaluations and Estimation

Related Research

Estimation of the incubation time distribution for COVID-19

Assessing the Uncertainty of Epidemiological Forecasts with Normalised Estimation Error Squared

EpiCovDA: a mechanistic COVID-19 forecasting model with data assimilation

Interpretable Sequence Learning for COVID-19 Forecasting

Curating a COVID-19 data repository and forecasting county-level death counts in the United States

Assessment of COVID-19 hospitalization forecasts from a simplified SIR model

Smooth multi-period forecasting with application to prediction of COVID-19 cases