- Country and US state-level forecasts for COVID-19 using heterogeneous infection rate model - Data-driven identification of unreported case
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 (, Ajitesh Srivastava (
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:
Our paper on forecasting:
Paper on detecting unreported cases:

Cite As

Ajitesh Srivastava (2024). ReCOVER (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2020a
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Version Published Release Notes

Fixed a forecast lag


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


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


Added some comments