Epidemic forecasts as a tool for public health: interpretation and (re)calibration
Abstract Objective: Recent studies have used Bayesian methods to predict timing of influenza epidemics many weeks in advance, but there is no documented evaluation of how such forecasts might support the day‐to‐day operations of public health staff. Methods: During the 2015 influenza season in Melbo...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2018-02-01
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| Series: | Australian and New Zealand Journal of Public Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/1753-6405.12750 |
| _version_ | 1797719620193353728 |
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| author | Robert Moss James E. Fielding Lucinda J. Franklin Nicola Stephens Jodie McVernon Peter Dawson James M. McCaw |
| author_facet | Robert Moss James E. Fielding Lucinda J. Franklin Nicola Stephens Jodie McVernon Peter Dawson James M. McCaw |
| author_sort | Robert Moss |
| collection | DOAJ |
| description | Abstract Objective: Recent studies have used Bayesian methods to predict timing of influenza epidemics many weeks in advance, but there is no documented evaluation of how such forecasts might support the day‐to‐day operations of public health staff. Methods: During the 2015 influenza season in Melbourne, Australia, weekly forecasts were presented at Health Department surveillance unit meetings, where they were evaluated and updated in light of expert opinion to improve their accuracy and usefulness. Results: Predictive capacity of the model was substantially limited by delays in reporting and processing arising from an unprecedented number of notifications, disproportionate to seasonal intensity. Adjustment of the predictive algorithm to account for these delays and increased reporting propensity improved both current situational awareness and forecasting accuracy. Conclusions: Collaborative engagement with public health practitioners in model development improved understanding of the context and limitations of emerging surveillance data. Incorporation of these insights in a quantitative model resulted in more robust estimates of disease activity for public health use. Implications for public health: In addition to predicting future disease trends, forecasting methods can quantify the impact of delays in data availability and variable reporting practice on the accuracy of current epidemic assessment. Such evidence supports investment in systems capacity. |
| first_indexed | 2024-03-12T09:07:39Z |
| format | Article |
| id | doaj.art-33c00d0298b24b86ace78d5a1714e0c7 |
| institution | Directory Open Access Journal |
| issn | 1326-0200 1753-6405 |
| language | English |
| last_indexed | 2024-03-12T09:07:39Z |
| publishDate | 2018-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Australian and New Zealand Journal of Public Health |
| spelling | doaj.art-33c00d0298b24b86ace78d5a1714e0c72023-09-02T15:11:00ZengElsevierAustralian and New Zealand Journal of Public Health1326-02001753-64052018-02-01421697610.1111/1753-6405.12750Epidemic forecasts as a tool for public health: interpretation and (re)calibrationRobert Moss0James E. Fielding1Lucinda J. Franklin2Nicola Stephens3Jodie McVernon4Peter Dawson5James M. McCaw6Modelling and Simulation Unit, Melbourne School of Population and Global Health The University of Melbourne VictoriaVictorian Infectious Diseases Reference Laboratory at the Peter Doherty Institute for Infection and Immunity The Royal Melbourne Hospital and The University of Melbourne VictoriaVictorian Government Department of Health and Human ServicesVictorian Government Department of Health and Human ServicesModelling and Simulation Unit, Melbourne School of Population and Global Health The University of Melbourne VictoriaDefence Science and Technology Group VictoriaModelling and Simulation Unit, Melbourne School of Population and Global Health The University of Melbourne VictoriaAbstract Objective: Recent studies have used Bayesian methods to predict timing of influenza epidemics many weeks in advance, but there is no documented evaluation of how such forecasts might support the day‐to‐day operations of public health staff. Methods: During the 2015 influenza season in Melbourne, Australia, weekly forecasts were presented at Health Department surveillance unit meetings, where they were evaluated and updated in light of expert opinion to improve their accuracy and usefulness. Results: Predictive capacity of the model was substantially limited by delays in reporting and processing arising from an unprecedented number of notifications, disproportionate to seasonal intensity. Adjustment of the predictive algorithm to account for these delays and increased reporting propensity improved both current situational awareness and forecasting accuracy. Conclusions: Collaborative engagement with public health practitioners in model development improved understanding of the context and limitations of emerging surveillance data. Incorporation of these insights in a quantitative model resulted in more robust estimates of disease activity for public health use. Implications for public health: In addition to predicting future disease trends, forecasting methods can quantify the impact of delays in data availability and variable reporting practice on the accuracy of current epidemic assessment. Such evidence supports investment in systems capacity.https://doi.org/10.1111/1753-6405.12750influenzaepidemicsforecastingpublic health |
| spellingShingle | Robert Moss James E. Fielding Lucinda J. Franklin Nicola Stephens Jodie McVernon Peter Dawson James M. McCaw Epidemic forecasts as a tool for public health: interpretation and (re)calibration Australian and New Zealand Journal of Public Health influenza epidemics forecasting public health |
| title | Epidemic forecasts as a tool for public health: interpretation and (re)calibration |
| title_full | Epidemic forecasts as a tool for public health: interpretation and (re)calibration |
| title_fullStr | Epidemic forecasts as a tool for public health: interpretation and (re)calibration |
| title_full_unstemmed | Epidemic forecasts as a tool for public health: interpretation and (re)calibration |
| title_short | Epidemic forecasts as a tool for public health: interpretation and (re)calibration |
| title_sort | epidemic forecasts as a tool for public health interpretation and re calibration |
| topic | influenza epidemics forecasting public health |
| url | https://doi.org/10.1111/1753-6405.12750 |
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