Model selection for seasonal influenza forecasting
Epidemics of seasonal influenza inflict a huge burden in temperate climes such as Melbourne (Australia) where there is also significant variability in their timing and magnitude. Particle filters combined with mechanistic transmission models for the spread of influenza have emerged as a popular meth...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2017-02-01
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| Series: | Infectious Disease Modelling |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468042716300318 |
| _version_ | 1797205424280174592 |
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| author | Alexander E. Zarebski Peter Dawson James M. McCaw Robert Moss |
| author_facet | Alexander E. Zarebski Peter Dawson James M. McCaw Robert Moss |
| author_sort | Alexander E. Zarebski |
| collection | DOAJ |
| description | Epidemics of seasonal influenza inflict a huge burden in temperate climes such as Melbourne (Australia) where there is also significant variability in their timing and magnitude. Particle filters combined with mechanistic transmission models for the spread of influenza have emerged as a popular method for forecasting the progression of these epidemics. Despite extensive research it is still unclear what the optimal models are for forecasting influenza, and how one even measures forecast performance.In this paper, we present a likelihood-based method, akin to Bayes factors, for model selection when the aim is to select for predictive skill. Here, âpredictive skillâ is measured by the probability of the data after the forecasting date, conditional on the data from before the forecasting date. Using this method we choose an optimal model of influenza transmission to forecast the number of laboratory-confirmed cases of influenza in Melbourne in each of the 2010â15 epidemics. The basic transmission model considered has the susceptible-exposed-infectious-recovered structure with extensions allowing for the effects of absolute humidity and inhomogeneous mixing in the population. While neither of the extensions provides a significant improvement in fit to the data they do differ in terms of their predictive skill. Both measurements of absolute humidity and a sinusoidal approximation of those measurements are observed to increase the predictive skill of the forecasts, while allowing for inhomogeneous mixing reduces the skill.We discuss how our work could be integrated into a forecasting system and how the model selection method could be used to evaluate forecasts when comparing to multiple surveillance systems providing disparate views of influenza activity. |
| first_indexed | 2024-04-24T08:50:54Z |
| format | Article |
| id | doaj.art-53c1277c70cf4d78876ad9db955fb89f |
| institution | Directory Open Access Journal |
| issn | 2468-0427 |
| language | English |
| last_indexed | 2024-04-24T08:50:54Z |
| publishDate | 2017-02-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Infectious Disease Modelling |
| spelling | doaj.art-53c1277c70cf4d78876ad9db955fb89f2024-04-16T11:54:31ZengKeAi Communications Co., Ltd.Infectious Disease Modelling2468-04272017-02-01215670Model selection for seasonal influenza forecastingAlexander E. Zarebski0Peter Dawson1James M. McCaw2Robert Moss3School of Mathematics and Statistics, The University of Melbourne, Melbourne, AustraliaLand Personnel Protection Branch, Land Division, Defence Science and Technology Organisation, Melbourne, AustraliaSchool of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia; Modelling & Simulation, Murdoch Childrens Research Institute, Royal Childrens Hospital, Melbourne, AustraliaCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia; Corresponding author.Epidemics of seasonal influenza inflict a huge burden in temperate climes such as Melbourne (Australia) where there is also significant variability in their timing and magnitude. Particle filters combined with mechanistic transmission models for the spread of influenza have emerged as a popular method for forecasting the progression of these epidemics. Despite extensive research it is still unclear what the optimal models are for forecasting influenza, and how one even measures forecast performance.In this paper, we present a likelihood-based method, akin to Bayes factors, for model selection when the aim is to select for predictive skill. Here, âpredictive skillâ is measured by the probability of the data after the forecasting date, conditional on the data from before the forecasting date. Using this method we choose an optimal model of influenza transmission to forecast the number of laboratory-confirmed cases of influenza in Melbourne in each of the 2010â15 epidemics. The basic transmission model considered has the susceptible-exposed-infectious-recovered structure with extensions allowing for the effects of absolute humidity and inhomogeneous mixing in the population. While neither of the extensions provides a significant improvement in fit to the data they do differ in terms of their predictive skill. Both measurements of absolute humidity and a sinusoidal approximation of those measurements are observed to increase the predictive skill of the forecasts, while allowing for inhomogeneous mixing reduces the skill.We discuss how our work could be integrated into a forecasting system and how the model selection method could be used to evaluate forecasts when comparing to multiple surveillance systems providing disparate views of influenza activity.http://www.sciencedirect.com/science/article/pii/S2468042716300318 |
| spellingShingle | Alexander E. Zarebski Peter Dawson James M. McCaw Robert Moss Model selection for seasonal influenza forecasting Infectious Disease Modelling |
| title | Model selection for seasonal influenza forecasting |
| title_full | Model selection for seasonal influenza forecasting |
| title_fullStr | Model selection for seasonal influenza forecasting |
| title_full_unstemmed | Model selection for seasonal influenza forecasting |
| title_short | Model selection for seasonal influenza forecasting |
| title_sort | model selection for seasonal influenza forecasting |
| url | http://www.sciencedirect.com/science/article/pii/S2468042716300318 |
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