Good times bad times: Automated forecasting of seasonal cryptosporidiosis in Ontario using machine learning

Olaf Berke; Lise Trotz-Williams; Simon de Montigny

doi:10.14745/ccdr.v46i06a07

Good times bad times: Automated forecasting of seasonal cryptosporidiosis in Ontario using machine learning

Can Commun Dis Rep. 2020 Jun 4;46(6):192-197. doi: 10.14745/ccdr.v46i06a07.

Authors

Olaf Berke¹, Lise Trotz-Williams^{1

2}, Simon de Montigny^{3

4}

Affiliations

¹ Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON.
² Wellington-Dufferin Guelph Public Health, Guelph, ON.
³ École de santé publique - Département de médecine sociale et préventive, Université de Montréal, Montréal, QC.
⁴ Centre de recherche du CHU Sainte-Justine, Montréal, QC.

Abstract

Background: The rise of big data and related predictive modelling based on machine learning algorithms over the last two decades have provided new opportunities for disease surveillance and public health preparedness. Big data come with the promise of faster generation of and access to more precise information, potentially facilitating predictive precision in public health ("precision public health"). As an example, we considered forecasting of the future course of the monthly cryptosporidiosis incidence in Ontario.

Methods: The traditional statistical approach to forecasting is the seasonal autoregressive integrated moving-average (SARIMA) model. We applied SARIMA and an artificial neural network (ANN) approach, specifically a feed-forward neural network, to predict monthly cryptosporidiosis incidence in Ontario in 2017 using 2005-2016 data as a training set. Both forecasting approaches are automated to make them relevant in a disease surveillance context. We compared the resulting forecasts using the root mean squared error (RMSE) and mean absolute error (MAE) as measures of predictive accuracy.

Results: Cryptosporidiosis is a seasonal disease, which peaks in Ontario in late summer. In this study, the SARIMA model and ANN forecasting approaches captured the seasonal pattern of cryptosporidiosis well. Contrary to similar studies reported in the literature, the ANN forecasts of cryptosporidiosis were slightly less accurate than the SARIMA model forecasts.

Conclusion: The ANN and SARIMA approaches are suitable for automated forecasting of public health time series data from surveillance systems. Future studies should employ additional algorithms (e.g. random forests) and assess accuracy by using alternative diseases for case studies and conducting rigorous simulation studies. Difference between the forecasts from the machine learning algorithm, that is, the ANN, and the statistical learning model, that is, the SARIMA, should be considered with respect to philosophical differences between the two approaches.

Keywords: SARIMA; artificial neural network; cryptosporidiosis; disease surveillance; forecasting; machine learning; seasonal time series; statistical learning.