Commentary on “Transparent modeling of influenza incidence”: Because the model said so

Moss, Robert

doi:10.1016/j.ijforecast.2021.01.028

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…This meant that we did not need to explicitly model control measures that were imposed and relaxed in each jurisdiction — such an effort would have been extremely complex and it would be impossible to account for decisions that occur within each forecast horizon and their impact on transmission dynamics. The fact that our forecasts exhibited high forecast skill, particularly during periods of sustained local transmission, demonstrates that increasing model complexity is not always necessary or appropriate 12 , 22 , 36 , 37 .…”

Section: Discussionmentioning

confidence: 95%

“…One of the most significant challenges in using predictive models to support public health decision-making in real time is understanding how much confidence to place in the predictions 20 – 22 . We address the question “When should we trust these forecasts?” by showing how forecast skill can be assessed in real-time before the ground truth is known .…”

Section: Introductionmentioning

confidence: 99%

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Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020

Moss

Price

Golding

et al. 2023

Sci Rep

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As of January 2021, Australia had effectively controlled local transmission of COVID-19 despite a steady influx of imported cases and several local, but contained, outbreaks in 2020. Throughout 2020, state and territory public health responses were informed by weekly situational reports that included an ensemble forecast of daily COVID-19 cases for each jurisdiction. We present here an analysis of one forecasting model included in this ensemble across the variety of scenarios experienced by each jurisdiction from May to October 2020. We examine how successfully the forecasts characterised future case incidence, subject to variations in data timeliness and completeness, showcase how we adapted these forecasts to support decisions of public health priority in rapidly-evolving situations, evaluate the impact of key model features on forecast skill, and demonstrate how to assess forecast skill in real-time before the ground truth is known. Conditioning the model on the most recent, but incomplete, data improved the forecast skill, emphasising the importance of developing strong quantitative models of surveillance system characteristics, such as ascertainment delay distributions. Forecast skill was highest when there were at least 10 reported cases per day, the circumstances in which authorities were most in need of forecasts to aid in planning and response.

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Section: Discussionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020

Moss

Price

Golding

et al. 2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

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Reply to commentaries on “Transparent modelling of influenza incidence”: Recency heuristics and psychological AI

Katsikopoulos

Şimşek

Buckmann

et al. 2022

International Journal of Forecasting

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Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020

Moss

Price

Golding

et al. 2022

Preprint

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As of January 2021, Australia had effectively controlled local transmission of COVID-19 despite a steady influx of imported cases and several local, but contained, outbreaks in 2020. Throughout 2020, state and territory public health responses were informed by weekly situational reports that included an ensemble forecast for each jurisdiction. We present here an analysis of one forecasting model included in this ensemble across the variety of scenarios experienced by each jurisdiction from May to October 2020. We examine how successfully the forecasts characterised future case incidence, subject to variations in data timeliness and completeness, showcase how we adapted these forecasts to support decisions of public health priority in rapidly-evolving situations, evaluate the impact of key model features on forecast skill, and demonstrate how to assess forecast skill in real-time before the ground truth is known. Conditioning the model on the most recent, but incomplete, data improved the forecast skill, emphasising the importance of developing strong quantitative models of surveillance system characteristics, such as ascertainment delay distributions. Forecast skill was highest when there were at least 10 reported cases per day, the circumstances in which authorities were most in need of forecasts to aid in planning and response.

show abstract

Commentary on “Transparent modeling of influenza incidence”: Because the model said so

Cited by 3 publications

References 14 publications

Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020

Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020

Reply to commentaries on “Transparent modelling of influenza incidence”: Recency heuristics and psychological AI

Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020

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