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Title: | Characterising information gains and losses when collecting multiple epidemic model outputs | Authors: | Sherratt, Katharine Srivastava, Ajitesh Ainslie, Kylie Singh, David E. Cublier, Aymar Marinescu, Maria Cristina Carretero, Jesus Garcia, Alberto Cascajo FRANCO, Nicolas WILLEM, Lander ABRAMS, Steven FAES, Christel Beutels, Philippe HENS, Niel Mueller, Sebastian Charlton, Billy Ewert, Ricardo Paltra, Sydney Rakow, Christian Rehmann, Jakob Conrad, Tim Schutte, Christof Nagel, Kai Abbott, Sam Grah, Rok Niehus, Rene Prasse, Bastian Sandmann, Frank Funk, Sebastian |
Issue Date: | 2024 | Publisher: | ELSEVIER | Source: | Epidemics (Print), 47 (Art N° 100765) | Abstract: | Background: Collaborative comparisons and combinations of epidemic models are used as policy-relevant evidence during epidemic outbreaks. In the process of collecting multiple model projections, such collaborations may gain or lose relevant information. Typically, modellers contribute a probabilistic summary at each time-step. We compared this to directly collecting simulated trajectories. We aimed to explore information on key epidemic quantities; ensemble uncertainty; and performance against data, investigating potential to continuously gain information from a single cross-sectional collection of model results. Methods: We compared projections from the European COVID-19 Scenario Modelling Hub. Five teams modelled incidence in Belgium, the Netherlands, and Spain. We compared July 2022 projections by incidence, peaks, and cumulative totals. We created a probabilistic ensemble drawn from all trajectories, and compared to ensembles from a median across each model's quantiles, or a linear opinion pool. We measured the predictive accuracy of individual trajectories against observations, using this in a weighted ensemble. We repeated this sequentially against increasing weeks of observed data. We evaluated these ensembles to reflect performance with varying observed data. Results: By collecting modelled trajectories, we showed policy-relevant epidemic characteristics. Trajectories contained a right-skewed distribution well represented by an ensemble of trajectories or a linear opinion pool, but not models' quantile intervals. Ensembles weighted by performance typically retained the range of plausible incidence over time, and in some cases narrowed this by excluding some epidemic shapes. Conclusions: We observed several information gains from collecting modelled trajectories rather than quantile distributions, including potential for continuously updated information from a single model collection. The value of information gains and losses may vary with each collaborative effort's aims, depending on the needs of projection users. Understanding the differing information potential of methods to collect model projections can support the accuracy, sustainability, and communication of collaborative infectious disease modelling efforts. | Notes: | Sherratt, K (corresponding author), London Sch Hyg & Trop Med, London, England. katharine.sherratt@lshtm.ac.uk |
Keywords: | Information;Scenarios;Uncertainty;Aggregation;Modelling | Document URI: | http://hdl.handle.net/1942/43719 | ISSN: | 1755-4365 | e-ISSN: | 1878-0067 | DOI: | 10.1016/j.epidem.2024.100765 | ISI #: | WOS:001287307800001 | Datasets of the publication: | 10.5281/zenodo.10891377 | Rights: | 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) | Category: | A1 | Type: | Journal Contribution |
Appears in Collections: | Research publications |
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