Frequent and unpredictable changes in COVID-19 policies and restrictions reduce the accuracy of model forecasts.
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| Title: | Frequent and unpredictable changes in COVID-19 policies and restrictions reduce the accuracy of model forecasts. |
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| Authors: | Houdroge F; Modelling and Biostatistics Group, Burnet Institute, 85 Commercial Road, Melbourne, VIC, 3004, Australia. farah.houdroge@burnet.edu.au., Palmer A; Modelling and Biostatistics Group, Burnet Institute, 85 Commercial Road, Melbourne, VIC, 3004, Australia., Delport D; Modelling and Biostatistics Group, Burnet Institute, 85 Commercial Road, Melbourne, VIC, 3004, Australia., Walsh T; Modelling and Biostatistics Group, Burnet Institute, 85 Commercial Road, Melbourne, VIC, 3004, Australia., Kelly SL; Modelling and Biostatistics Group, Burnet Institute, 85 Commercial Road, Melbourne, VIC, 3004, Australia., Hainsworth SW; Modelling and Biostatistics Group, Burnet Institute, 85 Commercial Road, Melbourne, VIC, 3004, Australia., Abeysuriya R; Modelling and Biostatistics Group, Burnet Institute, 85 Commercial Road, Melbourne, VIC, 3004, Australia.; Department of Epidemiology and Preventative Medicine, Monash University, Melbourne, Australia., Stuart RM; Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark., Kerr CC; Institute for Disease Modeling, Bill & Melinda Gates Foundation, Seattle, WA, USA.; School of Physics, University of Sydney, Sydney, NSW, Australia., Coplan P; Epidemiology, Johnson and Johnson, Titusville, NJ, USA., Wilson DP; Modelling and Biostatistics Group, Burnet Institute, 85 Commercial Road, Melbourne, VIC, 3004, Australia.; Department of Epidemiology and Preventative Medicine, Monash University, Melbourne, Australia.; Bill & Melinda Gates Foundation, Seattle, WA, USA., Scott N; Modelling and Biostatistics Group, Burnet Institute, 85 Commercial Road, Melbourne, VIC, 3004, Australia.; Department of Epidemiology and Preventative Medicine, Monash University, Melbourne, Australia. |
| Source: | Scientific reports [Sci Rep] 2023 Jan 25; Vol. 13 (1), pp. 1398. Date of Electronic Publication: 2023 Jan 25. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 2045-2322 |
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| DOI: | 10.1038/s41598-023-27711-3 |
