A real-time regional model for COVID-19: Probabilistic situational awareness and forecasting.

Autor: Engebretsen S; SAMBA, Norwegian Computing Center, Oslo, Norway., Diz-Lois Palomares A; Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway., Rø G; Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway., Kristoffersen AB; Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway., Lindstrøm JC; Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway., Engø-Monsen K; Telenor Research, Fornebu, Norway., Kamineni M; Oslo Centre for Biostatistics and Epidemiology. University of Oslo and Oslo University Hospital, Oslo, Norway., Hin Chan LY; Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway., Dale Ø; Telenor Norge AS Fornebu, Norway., Midtbø JE; Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway.; Telenor Norge AS Fornebu, Norway., Stenerud KL; Telenor Norge AS Fornebu, Norway., Di Ruscio F; Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway., White R; Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway., Frigessi A; Oslo Centre for Biostatistics and Epidemiology. University of Oslo and Oslo University Hospital, Oslo, Norway., de Blasio BF; Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway.; Oslo Centre for Biostatistics and Epidemiology. University of Oslo and Oslo University Hospital, Oslo, Norway.
Jazyk: angličtina
Zdroj: PLoS computational biology [PLoS Comput Biol] 2023 Jan 23; Vol. 19 (1), pp. e1010860. Date of Electronic Publication: 2023 Jan 23 (Print Publication: 2023).
DOI: 10.1371/journal.pcbi.1010860
Abstrakt: The COVID-19 pandemic is challenging nations with devastating health and economic consequences. The spread of the disease has revealed major geographical heterogeneity because of regionally varying individual behaviour and mobility patterns, unequal meteorological conditions, diverse viral variants, and locally implemented non-pharmaceutical interventions and vaccination roll-out. To support national and regional authorities in surveilling and controlling the pandemic in real-time as it unfolds, we here develop a new regional mathematical and statistical model. The model, which has been in use in Norway during the first two years of the pandemic, is informed by real-time mobility estimates from mobile phone data and laboratory-confirmed case and hospitalisation incidence. To estimate regional and time-varying transmissibility, case detection probabilities, and missed imported cases, we developed a novel sequential Approximate Bayesian Computation method allowing inference in useful time, despite the high parametric dimension. We test our approach on Norway and find that three-week-ahead predictions are precise and well-calibrated, enabling policy-relevant situational awareness at a local scale. By comparing the reproduction numbers before and after lockdowns, we identify spatially heterogeneous patterns in their effect on the transmissibility, with a stronger effect in the most populated regions compared to the national reduction estimated to be 85% (95% CI 78%-89%). Our approach is the first regional changepoint stochastic metapopulation model capable of real time spatially refined surveillance and forecasting during emergencies.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2023 Engebretsen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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