Comparing human and model-based forecasts of COVID-19 in Germany and Poland.
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 ad...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-09-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010405 |
| _version_ | 1811166326948888576 |
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| author | Nikos I Bosse Sam Abbott Johannes Bracher Habakuk Hain Billy J Quilty Mark Jit Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group Edwin van Leeuwen Anne Cori Sebastian Funk |
| author_facet | Nikos I Bosse Sam Abbott Johannes Bracher Habakuk Hain Billy J Quilty Mark Jit Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group Edwin van Leeuwen Anne Cori Sebastian Funk |
| author_sort | Nikos I Bosse |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T15:51:39Z |
| format | Article |
| id | doaj.art-55642145bc7e42038a75cac49131220e |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-10T15:51:39Z |
| publishDate | 2022-09-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-55642145bc7e42038a75cac49131220e2023-02-11T05:30:31ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-09-01189e101040510.1371/journal.pcbi.1010405Comparing human and model-based forecasts of COVID-19 in Germany and Poland.Nikos I BosseSam AbbottJohannes BracherHabakuk HainBilly J QuiltyMark JitCentre for the Mathematical Modelling of Infectious Diseases COVID-19 Working GroupEdwin van LeeuwenAnne CoriSebastian FunkForecasts 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.https://doi.org/10.1371/journal.pcbi.1010405 |
| spellingShingle | Nikos I Bosse Sam Abbott Johannes Bracher Habakuk Hain Billy J Quilty Mark Jit Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group Edwin van Leeuwen Anne Cori Sebastian Funk Comparing human and model-based forecasts of COVID-19 in Germany and Poland. PLoS Computational Biology |
| title | Comparing human and model-based forecasts of COVID-19 in Germany and Poland. |
| title_full | Comparing human and model-based forecasts of COVID-19 in Germany and Poland. |
| title_fullStr | Comparing human and model-based forecasts of COVID-19 in Germany and Poland. |
| title_full_unstemmed | Comparing human and model-based forecasts of COVID-19 in Germany and Poland. |
| title_short | Comparing human and model-based forecasts of COVID-19 in Germany and Poland. |
| title_sort | comparing human and model based forecasts of covid 19 in germany and poland |
| url | https://doi.org/10.1371/journal.pcbi.1010405 |
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