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

Roberto Vega,Russell Greiner,Leonardo Flores

doi:10.3390/forecast4010005

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.

Highlights

Since its identification in December 2019, COVID-19 has posed critical challenges for the public health and economies of essentially every country in the world [1–3]
The intuition used to generate the conditional probability tables (CPTs)’s is as follows: We considered that a change in trend in the current week depends on changes in policies during the previous three weeks
We evaluated the performance of SIMLR, in terms of the mean absolute percentage error (MAPE) and mean absolute error (MAE), for forecasting the number of new infections one to four weeks in advance, in data from United States and the six biggest provinces of Canada: Alberta (AB), British Columbia (BC), Manitoba (MN), Ontario (ON), Quebec (QB), and Saskatchewan (SK)

Summary

Introduction

Since its identification in December 2019, COVID-19 has posed critical challenges for the public health and economies of essentially every country in the world [1–3]. Forecasting the number of new infected people in the short and medium term is critical for the timely decisions about policies and for the proper allocation of medical resources [5,6]. There are three basic approaches for predicting the dynamics of an epidemic: compartmental models, statistical methods, and ML-based methods [5,7]. Compartmental models subdivide a population into mutually exclusive categories, with a set of dynamical equations that explain the transitions among categories [8]. Statistical methods extract general statistics from the data to fit mathematical models that explain the evolution of the epidemic [6]. ML-based methods use machine learning algorithms to analyze historical data and find patterns that lead to accurate predictions of the number of new infected people [7,10]

Methods

Results

Discussion

Conclusion