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

Roberto Vega, Leonardo Flores and Russell Greiner
Additional contact information
Roberto Vega: Department of Computing Science, University of Alberta, Edmonton, AB T6G 2R3, Canada
Leonardo Flores: Independent Researcher, San Luis Potosi 78170, Mexico
Russell Greiner: Department of Computing Science, University of Alberta, Edmonton, AB T6G 2R3, Canada

Forecasting, 2022, vol. 4, issue 1, 1-23

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 one to four 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 in Canada and the United States, and show that its mean average percentage error is as good as state-of-the-art 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.

Keywords: COVID-19; probabilistic graphical models; interpretable machine learning (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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