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

TL;DR

The paper introduces SIMLR, a machine learning-enhanced SIR model that improves COVID-19 infection forecasts by incorporating policy changes, providing accurate and interpretable predictions for 1-4 weeks ahead.

Contribution

It presents a novel integration of machine learning with the SIR epidemiological model to improve short-term infectious disease forecasting.

Findings

SIMLR achieves competitive MAPE performance with state-of-the-art models.

The model effectively incorporates policy change data for better predictions.

It provides interpretable insights into the impact of policies on infection dynamics.

Abstract

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.