Evidence map›Paper›PMID 42018585›Full record

ArticlePLoS computational biology2026

Ensemble forecasts of COVID-19 activity to support Australia's pandemic response: 2020-22.

Robert Moss, Ruarai J Tobin, Mitchell O'Hara-Wild, Adeshina I Adekunle, Dennis Liu, Tobin South, Dylan J Morris, Gerard E Ryan, Tianxiao Hao, Aarathy Babu and 9 more

Abstract read
In one paragraph

Article in PLoS computational biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Robert MossMelbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.ORCID https://orcid.org/0000-0002-4568-2012
Ruarai J TobinMelbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.ORCID https://orcid.org/0000-0002-5202-240X
Mitchell O'Hara-WildDepartment of Econometrics and Business Statistics, Monash University, Melbourne, Victoria, Australia.ORCID https://orcid.org/0000-0001-6729-7695
Adeshina I AdekunleDefence Science and Technology Group, Melbourne, Victoria, Australia.
Dennis LiuSchool of Computer and Mathematical Sciences, The University of Adelaide, Adelaide, South Australia, Australia.
Tobin SouthSchool of Computer and Mathematical Sciences, The University of Adelaide, Adelaide, South Australia, Australia.
Dylan J MorrisSchool of Computer and Mathematical Sciences, The University of Adelaide, Adelaide, South Australia, Australia.
Gerard E RyanMelbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.ORCID https://orcid.org/0000-0003-0183-7630
Tianxiao HaoMelbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.
Aarathy BabuThe Kids Research Institute Australia, Perth, Western Australia, Australia.
Katharine L SeniorMelbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.ORCID https://orcid.org/0000-0003-1123-1472
James G WoodSchool of Population Health, University of New South Wales, Sydney, New South Wales, Australia.
Nick GoldingThe Kids Research Institute Australia, Perth, Western Australia, Australia.
Joshua V RossDepartment for Health and Wellbeing, Government of South Australia, Adelaide, South Australia, Australia.
Peter DawsonSensors and Effectors Division, Defence Science and Technology Group, Department of Defence, Melbourne, Victoria, Australia.
Rob J HyndmanDepartment of Econometrics and Business Statistics, Monash University, Melbourne, Victoria, Australia.
David J PriceMelbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.
James M McCawMelbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.
Freya M ShearerMelbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

During the COVID-19 pandemic, many countries used real-time data analyses, predictive modelling, and COVID-19 case forecasts, to incorporate emerging evidence into their decisions. In Australia, national and jurisdictional public health responses were informed by weekly ensemble forecasts of daily COVID-19 case counts for each of Australia's eight states and territories, produced by a consortium of researchers under contract with the Australian Government. As members of this consortium, who produced these forecasts at each week, we now retrospectively evaluate approximately 100,000 predictions for daily case counts 1-28 days into the future, generated between July 2020 and December 2022, and report here (a) how the ensemble forecasts supported public health responses; (b) how well the ensemble forecast performed, relative to the forecasts produced by each contributing team; and (c) how we refined our reporting and visualisations to ensure that outputs were interpreted appropriately. Similar to COVID-19 forecasting studies in other countries, we found that the ensemble forecast consistently out-performed the individual model forecasts, and that performance was lowest when there were rapid changes in the epidemiology, such as periods around epidemic peaks. Our consortium's internal peer-review process allowed us to explain how features of each ensemble forecast related to the design of the individual models, and this helped enable public health stakeholders to interpret the forecasts appropriately. Ultimately, our forecasts provided information that supported public health responses during periods of different policy goals, and over a wide range of epidemic scenarios.

Indexed as

COVID-19PandemicsAustraliaComputational BiologyEnsemble LearningForecastingHumansPublic HealthRetrospective StudiesSARS-CoV-2

Identifiers

PMID42018585
PMCPMC13124057

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.