Characterising information gains and losses when collecting multiple epidemic model outputs Articles uri icon

authors

  • Sherratt, Katharine
  • Srivastava, Ajitesh
  • Ainslie, Kylie
  • EXPOSITO SINGH, DAVID
  • CUBLIER MARTINEZ, AYMAR
  • MARINESCU, MARIA CRISTINA
  • CARRETERO PEREZ, JESUS
  • CASCAJO GARCIA, ALBERTO
  • Franco, Nicolas
  • Willem, Lander
  • Abrams, Steven
  • Faes, Christel
  • Beutels, Philippe
  • Hens, Niel
  • Müller, Sebastian
  • Charlton, Billy
  • Ewert, Ricardo
  • Paltra, Sydney
  • Rakow, Christian
  • Rehmann, Jakob
  • Conrad, Tim
  • Schütte, Christof
  • Nagel, Kai
  • Grah, Rok
  • Niehus, Rene
  • Prasse, Bastian
  • Sandmann, Frank
  • Funk, Sebastian

publication date

  • April 2024

start page

  • 1

end page

  • 8

volume

  • 47

International Standard Serial Number (ISSN)

  • 1755-4365

Electronic International Standard Serial Number (EISSN)

  • 1878-0067

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

subjects

  • Computer Science

keywords

  • information; scenarios; uncertainty; aggregation; modelling