Jörg Simon Wicker Senior Lecturer | School of Computer Science | The University of Auckland
Senior Lecturer | School of Computer Science | The University of Auckland

Geographic Ensembles of Observations using Randomised Ensembles of Autoregression Chains: Ensemble methods for spatio-temporal Time Series Forecasting of Influenza-like Illness

Nooriyan Poonawala-Lohani, Pat Riddle, Mehnaz Adnan, Jörg Wicker: Geographic Ensembles of Observations using Randomised Ensembles of Autoregression Chains: Ensemble methods for spatio-temporal Time Series Forecasting of Influenza-like Illness. In: Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, pp. 1-7, Association for Computing Machinery, New York, NY, USA, 2022, ISBN: 9781450393867.

Abstract

Influenza is a communicable respiratory illness that can cause serious public health hazards. Flu surveillance in New Zealand tracks case counts from various District health boards (DHBs) in the country to monitor the spread of influenza in different geographic locations. Many factors contribute to the spread of the influenza across a geographic region, and it can be challenging to forecast cases in one region without taking into account case numbers in another region. This paper proposes a novel ensemble method called Geographic Ensembles of Observations using Randomised Ensembles of Autoregression Chains (GEO-Reach). GEO-Reach is an ensemble technique that uses a two layer approach to utilise interdependence of historical case counts between geographic regions in New Zealand. This work extends a previously published method by the authors called Randomized Ensembles of Auto-regression chains (Reach). State-of-the-art forecasting models look at studying the spread of the virus. They focus on accurate forecasting of cases for a location using historical case counts for the same location and other data sources based on human behaviour such as movement of people across cities/geographic regions. This new approach is evaluated using Influenza like illness (ILI) case counts in 7 major regions in New Zealand from the years 2015-2019 and compares its performance with other standard methods such as Dante, ARIMA, Autoregression and Random Forests. The results demonstrate that the proposed method performed better than baseline methods when applied to this multi-variate time series forecasting problem.

BibTeX (Download)

@inproceedings{poonawala-lohani2022geographic,
title = {Geographic Ensembles of Observations using Randomised Ensembles of Autoregression Chains: Ensemble methods for spatio-temporal Time Series Forecasting of Influenza-like Illness},
author = {Nooriyan Poonawala-Lohani and Pat Riddle and Mehnaz Adnan and J\"{o}rg Wicker},
doi = {10.1145/3535508.3545562},
isbn = {9781450393867},
year  = {2022},
date = {2022-08-07},
urldate = {2022-08-07},
booktitle = {Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics},
pages = {1-7},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
abstract = {Influenza is a communicable respiratory illness that can cause serious public health hazards. Flu surveillance in New Zealand tracks case counts from various District health boards (DHBs) in the country to monitor the spread of influenza in different geographic locations. Many factors contribute to the spread of the influenza across a geographic region, and it can be challenging to forecast cases in one region without taking into account case numbers in another region. This paper proposes a novel ensemble method called Geographic Ensembles of Observations using Randomised Ensembles of Autoregression Chains (GEO-Reach). GEO-Reach is an ensemble technique that uses a two layer approach to utilise interdependence of historical case counts between geographic regions in New Zealand. This work extends a previously published method by the authors called Randomized Ensembles of Auto-regression chains (Reach). State-of-the-art forecasting models look at studying the spread of the virus. They focus on accurate forecasting of cases for a location using historical case counts for the same location and other data sources based on human behaviour such as movement of people across cities/geographic regions. This new approach is evaluated using Influenza like illness (ILI) case counts in 7 major regions in New Zealand from the years 2015-2019 and compares its performance with other standard methods such as Dante, ARIMA, Autoregression and Random Forests. The results demonstrate that the proposed method performed better than baseline methods when applied to this multi-variate time series forecasting problem.},
keywords = {bioinformatics, computational sustainability, dynamic time warping, forecasting, influenza, machine learning, medicine, time series},
pubstate = {published},
tppubtype = {inproceedings}
}