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ReCOVER

version 2.01 (637 KB) by Ajitesh Srivastava
- Country and US state-level forecasts for COVID-19 using heterogeneous infection rate model - Data-driven identification of unreported case

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Updated 15 Jun 2020

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This is a part of the following NSF project:
ReCOVER: Accurate Predictions and Resource Allocation for COVID-19 Epidemic Response
PIs: Viktor K. Prasanna (prasanna@usc.edu), Ajitesh Srivastava (ajiteshs@usc.edu)
University of Southern California

This repository contains some codes for our ongoing work on NSF-funded project on COVID-19 forecasting.
We use our own epidemic model called SI-kJalpha - Heterogeneous Infection Rate with Human Mobility.

For live script for forecasting, run: plot_gen.mlx
For detecting unreported cases use: daily_explore_unrep.mlx

Our relevant presentation: https://www.youtube.com/watch?v=ll6k8wlxOFo


Our paper on forecasting: https://arxiv.org/abs/2004.11372
Paper on detecting unreported cases: https://arxiv.org/abs/2006.02127

Cite As

Ajitesh Srivastava (2020). ReCOVER (https://www.mathworks.com/matlabcentral/fileexchange/75281-recover), MATLAB Central File Exchange. Retrieved .

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