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 (2026). ReCOVER (https://in.mathworks.com/matlabcentral/fileexchange/75281-recover), MATLAB Central File Exchange. Retrieved .

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ReCOVER/scripts

ReCOVER/scripts

Version Published Release Notes
2.01

Fixed a forecast lag
2.0

Added smoothing in forecasting. Also added possibility of detecting unreported cases
1.1

Improved hyperparameter search. Added pre-calculated hyper-parameters for various days in the past.
Also added scripts to generate dynamic reproductive number
1.0.1

Added some comments
1.0.0