Optimising predictive modelling of Ross River virus using meteorological variables.
Background Statistical models are regularly used in the forecasting and surveillance of infectious diseases to guide public health. Variable selection assists in determining factors associated with disease transmission, however, often overlooked in this process is the evaluation and suitability of t...
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ftdoajarticles:oai:doaj.org/article:63b99113c2c4443695c5aca5955e01c4 2023-05-15T15:16:41+02:00 Optimising predictive modelling of Ross River virus using meteorological variables. Iain S Koolhof Simon M Firestone Silvana Bettiol Michael Charleston Katherine B Gibney Peter J Neville Andrew Jardine Scott Carver 2021-03-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0009252 https://doaj.org/article/63b99113c2c4443695c5aca5955e01c4 EN eng Public Library of Science (PLoS) https://doi.org/10.1371/journal.pntd.0009252 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0009252 https://doaj.org/article/63b99113c2c4443695c5aca5955e01c4 PLoS Neglected Tropical Diseases, Vol 15, Iss 3, p e0009252 (2021) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2021 ftdoajarticles https://doi.org/10.1371/journal.pntd.0009252 2022-12-31T13:53:10Z Background Statistical models are regularly used in the forecasting and surveillance of infectious diseases to guide public health. Variable selection assists in determining factors associated with disease transmission, however, often overlooked in this process is the evaluation and suitability of the statistical model used in forecasting disease transmission and outbreaks. Here we aim to evaluate several modelling methods to optimise predictive modelling of Ross River virus (RRV) disease notifications and outbreaks in epidemiological important regions of Victoria and Western Australia. Methodology/principal findings We developed several statistical methods using meteorological and RRV surveillance data from July 2000 until June 2018 in Victoria and from July 1991 until June 2018 in Western Australia. Models were developed for 11 Local Government Areas (LGAs) in Victoria and seven LGAs in Western Australia. We found generalised additive models and generalised boosted regression models, and generalised additive models and negative binomial models to be the best fit models when predicting RRV outbreaks and notifications, respectively. No association was found with a model's ability to predict RRV notifications in LGAs with greater RRV activity, or for outbreak predictions to have a higher accuracy in LGAs with greater RRV notifications. Moreover, we assessed the use of factor analysis to generate independent variables used in predictive modelling. In the majority of LGAs, this method did not result in better model predictive performance. Conclusions/significance We demonstrate that models which are developed and used for predicting disease notifications may not be suitable for predicting disease outbreaks, or vice versa. Furthermore, poor predictive performance in modelling disease transmissions may be the result of inappropriate model selection methods. Our findings provide approaches and methods to facilitate the selection of the best fit statistical model for predicting mosquito-borne disease notifications and ... Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic PLOS Neglected Tropical Diseases 15 3 e0009252 |
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Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Iain S Koolhof Simon M Firestone Silvana Bettiol Michael Charleston Katherine B Gibney Peter J Neville Andrew Jardine Scott Carver Optimising predictive modelling of Ross River virus using meteorological variables. |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
Background Statistical models are regularly used in the forecasting and surveillance of infectious diseases to guide public health. Variable selection assists in determining factors associated with disease transmission, however, often overlooked in this process is the evaluation and suitability of the statistical model used in forecasting disease transmission and outbreaks. Here we aim to evaluate several modelling methods to optimise predictive modelling of Ross River virus (RRV) disease notifications and outbreaks in epidemiological important regions of Victoria and Western Australia. Methodology/principal findings We developed several statistical methods using meteorological and RRV surveillance data from July 2000 until June 2018 in Victoria and from July 1991 until June 2018 in Western Australia. Models were developed for 11 Local Government Areas (LGAs) in Victoria and seven LGAs in Western Australia. We found generalised additive models and generalised boosted regression models, and generalised additive models and negative binomial models to be the best fit models when predicting RRV outbreaks and notifications, respectively. No association was found with a model's ability to predict RRV notifications in LGAs with greater RRV activity, or for outbreak predictions to have a higher accuracy in LGAs with greater RRV notifications. Moreover, we assessed the use of factor analysis to generate independent variables used in predictive modelling. In the majority of LGAs, this method did not result in better model predictive performance. Conclusions/significance We demonstrate that models which are developed and used for predicting disease notifications may not be suitable for predicting disease outbreaks, or vice versa. Furthermore, poor predictive performance in modelling disease transmissions may be the result of inappropriate model selection methods. Our findings provide approaches and methods to facilitate the selection of the best fit statistical model for predicting mosquito-borne disease notifications and ... |
format |
Article in Journal/Newspaper |
author |
Iain S Koolhof Simon M Firestone Silvana Bettiol Michael Charleston Katherine B Gibney Peter J Neville Andrew Jardine Scott Carver |
author_facet |
Iain S Koolhof Simon M Firestone Silvana Bettiol Michael Charleston Katherine B Gibney Peter J Neville Andrew Jardine Scott Carver |
author_sort |
Iain S Koolhof |
title |
Optimising predictive modelling of Ross River virus using meteorological variables. |
title_short |
Optimising predictive modelling of Ross River virus using meteorological variables. |
title_full |
Optimising predictive modelling of Ross River virus using meteorological variables. |
title_fullStr |
Optimising predictive modelling of Ross River virus using meteorological variables. |
title_full_unstemmed |
Optimising predictive modelling of Ross River virus using meteorological variables. |
title_sort |
optimising predictive modelling of ross river virus using meteorological variables. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2021 |
url |
https://doi.org/10.1371/journal.pntd.0009252 https://doaj.org/article/63b99113c2c4443695c5aca5955e01c4 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 15, Iss 3, p e0009252 (2021) |
op_relation |
https://doi.org/10.1371/journal.pntd.0009252 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0009252 https://doaj.org/article/63b99113c2c4443695c5aca5955e01c4 |
op_doi |
https://doi.org/10.1371/journal.pntd.0009252 |
container_title |
PLOS Neglected Tropical Diseases |
container_volume |
15 |
container_issue |
3 |
container_start_page |
e0009252 |
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1766346974125096960 |