# SIMLR: Machine Learning inside the SIR model for COVID-19 Forecasting

arXiv: https://arxiv.org/abs/2106.01590 · HTML: https://mlsanity.com/paper/2106.01590/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 36 references that are in the map.
- First posted on arXiv 3 Jun 2021.

## Builds on (most cited first, 4 of 4)

- [Interpretable Sequence Learning for COVID-19 Forecasting](https://mlsanity.com/paper/2008.00646/) (2020)
- [Inter-Series Attention Model for COVID-19 Forecasting](https://mlsanity.com/paper/2010.13006/) (2020)
- [A Time-dependent SIR model for COVID-19 with Undetectable Infected Persons](https://mlsanity.com/paper/2003.00122/) (2020)
- [Sample Efficient Learning of Image-Based Diagnostic Classifiers Using Probabilistic Labels](https://mlsanity.com/paper/2102.06164/) (2021)

## Built on by (most cited first, 5 of 5)

- [Containing a spread through sequential learning: to exploit or to explore?](https://mlsanity.com/paper/2303.00141/) (2023)
- [Inverse problem for parameters identification in a modified SIRD epidemic model using ensemble neural networks](https://mlsanity.com/paper/2203.00407/) (2022)
- [Modeling and Forecasting COVID-19 Cases using Latent Subpopulations](https://mlsanity.com/paper/2302.04829/) (2023)
- [Deep Demixing: Reconstructing the Evolution of Network Epidemics](https://mlsanity.com/paper/2306.07938/) (2023)
- [Clustering Alzheimer's Disease Subtypes via Similarity Learning and Graph Diffusion](https://mlsanity.com/paper/2410.03937/) (2024)

## Abstract (arXiv)

Accurate forecasts of the number of newly infected people during an epidemic are critical for making effective timely decisions. This paper addresses this challenge using the SIMLR model, which incorporates machine learning (ML) into the epidemiological SIR model. For each region, SIMLR tracks the changes in the policies implemented at the government level, which it uses to estimate the time-varying parameters of an SIR model for forecasting the number of new infections 1- to 4-weeks in advance.It also forecasts the probability of changes in those government policies at each of these future times, which is essential for the longer-range forecasts. We applied SIMLR to data from regions in Canada and in the United States,and show that its MAPE (mean average percentage error) performance is as good as SOTA forecasting models, with the added advantage of being an interpretable model. We expect that this approach will be useful not only for forecasting COVID-19 infections, but also in predicting the evolution of other infectious diseases.