2025
Cheena, Asif; Dost, Katharina; Sarris, Theo; Straathof, Nina; Wicker, Jörg
Don't Swim in Data: Real-Time Microbial Forecasting for New Zealand Recreational Waters Unpublished Forthcoming
SRRN, Forthcoming.
Abstract | Links | BibTeX | Altmetric | PlumX | Tags: computational sustainability, machine learning, matrix decomposition, time series forecasting, water quality
@unpublished{Cheena2025dont,
title = {Don\'t Swim in Data: Real-Time Microbial Forecasting for New Zealand Recreational Waters},
author = {Asif Cheena and Katharina Dost and Theo Sarris and Nina Straathof and J\"{o}rg Wicker},
doi = {10.2139/ssrn.5230457},
year = {2025},
date = {2025-04-30},
urldate = {2025-04-30},
abstract = {Traditional water quality monitoring, reliant on infrequent sampling and 48-hour laboratory delays, often fails to capture rapid contamination fluctuations, exposing recreational water users to significant health risks. We propose two novel machine learning frameworks for real-time forecasting of Enterococci concentrations in Canterbury, New Zealand. The Probabilistic Forecasting Framework uses an ensemble of quantile regression models with a gradient boosting meta-learner and Conformalized Quantile Regression (CQR) to produce accurate point forecasts and calibrated 90% prediction intervals. In parallel, the Matrix Decomposition Framework employs Non-negative Matrix Factorization (NMF) to decompose spatio-temporal data into interpretable latent factors, modeled via multi-target Random Forests to enhance generalizability. Evaluated on data from 15 sites (2021\textendash2024, 1047 samples, 100 exceedance events), our frameworks exceed USGS guidelines, achieving exceedance sensitivities of 67.0% and 61.0%, with high precautionary sensitivities of 77.0% and 74.0%, respectively, and competitive performance relative to state-of-the-art systems such as Auckland’s Safeswim.},
howpublished = {SRRN},
keywords = {computational sustainability, machine learning, matrix decomposition, time series forecasting, water quality},
pubstate = {forthcoming},
tppubtype = {unpublished}
}
Albrecht, Steffen; Kim, Alex; Madelino, João; Dost, Katharina; Zhu, Johnny; Broderick, David; Poonawala-Lohani, Nooriyan; Jamison, Sarah; Rasanathan, Damayanthi; Stanley, Alicia; Lawrence, Shirley; Marsh, Samantha; Castelino, Lorraine; Trenholme, Adrian; Turner, Nikki; McIntyre, Peter; Paynter, Janine; Riddle, Pat; Grant, Cameron; Wicker, Jörg; Dobbie, Gillian
How does a GPT perform in Forecasting Severe Respiratory Disease Hospitalizations? Proceedings Article
In: 31th International Conference on Neural Information Processing (ICONIP) , Auckland University of Technology (AUT) Library, 2025.
Abstract | Links | BibTeX | Altmetric | PlumX | Tags: influenza, machine learning, time series, time series forecasting
@inproceedings{nokey,
title = {How does a GPT perform in Forecasting Severe Respiratory Disease Hospitalizations?},
author = {Steffen Albrecht and Alex Kim and Jo\~{a}o Madelino and Katharina Dost and Johnny Zhu and David Broderick and Nooriyan Poonawala-Lohani and Sarah Jamison and Damayanthi Rasanathan and Alicia Stanley and Shirley Lawrence and Samantha Marsh and Lorraine Castelino and Adrian Trenholme and Nikki Turner and Peter McIntyre and Janine Paynter and Pat Riddle and Cameron Grant and J\"{o}rg Wicker and Gillian Dobbie},
doi = { 10.24135/iconip1},
year = {2025},
date = {2025-03-17},
booktitle = {31th International Conference on Neural Information Processing (ICONIP) },
volume = {1},
publisher = {Auckland University of Technology (AUT) Library},
abstract = {Forecasting surges in hospital admissions caused by severe respiratory infections is of crucial importance during the winter season to enable proactive hospital management and timely decision-making to prevent healthcare system overload. As time series derived from hospital surveillance systems for these severe cases are sparse and encode weak seasonality patterns, machine learning is key to computing accurate forecasts. The most recent algorithmic advance in time series forecasting is the adaptation of generative pre-trained transformers (GPTs). Those models, pre-trained on large datasets, have the potential to transfer knowledge to smaller datasets, such as hospital surveillance data, for very specific and well-defined case definitions. We demonstrate that despite this great potential for such practical applications, one of the largest first-generation GPTs is not able to provide accurate forecasts and is outperformed by simple linear models and even na\"{i}ve forecasts. DOI: https://doi.org/10.24135/ICONIP1
},
keywords = {influenza, machine learning, time series, time series forecasting},
pubstate = {published},
tppubtype = {inproceedings}
}
2024
Albrecht, Steffen; Broderick, David; Dost, Katharina; Cheung, Isabella; Nghiem, Nhung; Wu, Milton; Zhu, Johnny; Poonawala-Lohani, Nooriyan; Jamison, Sarah; Rasanathan, Damayanthi; Huang, Sue; Trenholme, Adrian; Stanley, Alicia; Lawrence, Shirley; Marsh, Samantha; Castelino, Lorraine; Paynter, Janine; Turner, Nikki; McIntyre, Peter; Riddle, Pat; Grant, Cameron; Dobbie, Gillian; Wicker, Jörg
Forecasting severe respiratory disease hospitalizations using machine learning algorithms Journal Article
In: BMC Medical Informatics and Decision Making, vol. 24, iss. 1, pp. 293, 2024, ISSN: 1472-6947.
Abstract | Links | BibTeX | Altmetric | PlumX | Tags: influenza, machine learning, time series, time series forecasting
@article{Albrecht2024forecasting,
title = {Forecasting severe respiratory disease hospitalizations using machine learning algorithms},
author = {Steffen Albrecht and David Broderick and Katharina Dost and Isabella Cheung and Nhung Nghiem and Milton Wu and Johnny Zhu and Nooriyan Poonawala-Lohani and Sarah Jamison and Damayanthi Rasanathan and Sue Huang and Adrian Trenholme and Alicia Stanley and Shirley Lawrence and Samantha Marsh and Lorraine Castelino and Janine Paynter and Nikki Turner and Peter McIntyre and Pat Riddle and Cameron Grant and Gillian Dobbie and J\"{o}rg Wicker},
url = {https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-024-02702-0},
doi = {10.1186/s12911-024-02702-0},
issn = {1472-6947},
year = {2024},
date = {2024-10-09},
urldate = {2024-10-07},
journal = {BMC Medical Informatics and Decision Making},
volume = {24},
issue = {1},
pages = {293},
abstract = {Forecasting models predicting trends in hospitalization rates have the potential to inform hospital management during seasonal epidemics of respiratory diseases and the associated surges caused by acute hospital admissions. Hospital bed requirements for elective surgery could be better planned if it were possible to foresee upcoming peaks in severe respiratory illness admissions. Forecasting models can also guide the use of intervention strategies to decrease the spread of respiratory pathogens and thus prevent local health system overload. In this study, we explore the capability of forecasting models to predict the number of hospital admissions in Auckland, New Zealand, within a three-week time horizon. Furthermore, we evaluate probabilistic forecasts and the impact on model performance when integrating laboratory data describing the circulation of respiratory viruses.},
keywords = {influenza, machine learning, time series, time series forecasting},
pubstate = {published},
tppubtype = {article}
}