Building a one-vs-all classifier for spatial prediction of detected pathogens
Maskell, Peter, Ryan, Matt, Karawita, Anjana, Hickson, R.I., and Golchin, Maryam (2023) Building a one-vs-all classifier for spatial prediction of detected pathogens. In: Proceedings of the 25th International Congress on Modelling and Simulation. pp. 560-566. From: MODSIM 2023: 25th International Congress on Modelling and Simulation, 9-14 July 2023, Darwin, NT, Australia.
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Abstract
More than 75% of human infectious diseases are caused by the transmission of pathogens from animals to humans (that is, zoonotic spillover). This demonstrates the importance of understanding the relative risks of each pathogen in each spatial region. In this study, we build one-vs-all classifiers to distinguish Mycobacterium and Listeria amongst all other recorded bacteria. We selected these two bacteria as they cause morbidity and fatality among humans and animals. We overcome the impact of class imbalance caused by spatial and taxonomical biases in detected pathogen occurrence data by under-sampling the majority negative samples and keeping all the minority positive samples. We further improved the prediction results by including animal richness data (number of genera present). Our findings highlight that there is a weak relationship between the predictive features and the relative occurrence of the target pathogen. We also identified that the inclusion of spatial-temporal information in the prediction process could increase generalisability. The biological study of the detected features suggests that more targeted infectious diseases surveillance data is required to validate the predicted results.
| Item ID: | 82398 |
|---|---|
| Item Type: | Conference Item (Research - E1) |
| ISBN: | 9780987214300 |
| Keywords: | Listeria, Mycobacterium, One Health, one-vs-all classifier, zoonotic spillover |
| Copyright Information: | These proceedings are licensed under the terms of the Creative Commons Attribution 4.0 International CC BY License (http://creativecommons.org/licenses/by/4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you attribute MSSANZ and the original author(s) and source, provide a link to the Creative Commons licence and indicate if changes were made. Images or other third party material are included in this licence, unless otherwise indicated in a credit line to the material. |
| Date Deposited: | 21 Mar 2024 01:41 |
| FoR Codes: | 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461199 Machine learning not elsewhere classified @ 80% 42 HEALTH SCIENCES > 4202 Epidemiology > 420205 Epidemiological modelling @ 20% |
| SEO Codes: | 20 HEALTH > 2004 Public health (excl. specific population health) > 200404 Disease distribution and transmission (incl. surveillance and response) @ 100% |
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