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  Comparing human and model-based forecasts of COVID-19 in Germany and Poland

Bosse, N. I., Abbott, S., Bracher, J., Hain, H., Quilty, B. J., Jit, M., et al. (2022). Comparing human and model-based forecasts of COVID-19 in Germany and Poland. PLoS Computational Biology, 18(9): e1010405. doi:10.1371/journal.pcbi.1010405.

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 Creators:
Bosse, Nikos I., Author
Abbott, Sam, Author
Bracher, Johannes, Author
Hain, Habakuk1, Author           
Quilty, Billy J., Author
Jit, Mark, Author
Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group, Author              
van Leeuwen, Edwin, Author
Cori, Anne, Author
Funk, Sebastian, Author
Affiliations:
1Research Group of Synaptic Nanophysiology, Max Planck Institute for Multidisciplinary Sciences, Max Planck Society, ou_3350139              

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 Abstract: Forecasts based on epidemiological modelling have played an important role in shaping public policy throughout the COVID-19 pandemic. This modelling combines knowledge about infectious disease dynamics with the subjective opinion of the researcher who develops and refines the model and often also adjusts model outputs. Developing a forecast model is difficult, resource- and time-consuming. It is therefore worth asking what modelling is able to add beyond the subjective opinion of the researcher alone. To investigate this, we analysed different real-time forecasts of cases of and deaths from COVID-19 in Germany and Poland over a 1-4 week horizon submitted to the German and Polish Forecast Hub. We compared crowd forecasts elicited from researchers and volunteers, against a) forecasts from two semi-mechanistic models based on common epidemiological assumptions and b) the ensemble of all other models submitted to the Forecast Hub. We found crowd forecasts, despite being overconfident, to outperform all other methods across all forecast horizons when forecasting cases (weighted interval score relative to the Hub ensemble 2 weeks ahead: 0.89). Forecasts based on computational models performed comparably better when predicting deaths (rel. WIS 1.26), suggesting that epidemiological modelling and human judgement can complement each other in important ways.

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Language(s): eng - English
 Dates: 2022-09-19
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1371/journal.pcbi.1010405
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Title: PLoS Computational Biology
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 18 (9) Sequence Number: e1010405 Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1