التفاصيل البيبلوغرافية
| العنوان: |
A real-time regional model for COVID-19: Probabilistic situational awareness and forecasting. |
| المؤلفون: |
Engebretsen, Solveig, Diz-Lois Palomares, Alfonso, Rø, Gunnar, Kristoffersen, Anja Bråthen, Lindstrøm, Jonas Christoffer, Engø-Monsen, Kenth, Kamineni, Meghana, Hin Chan, Louis Yat, Dale, Ørjan, Midtbø, Jørgen Eriksson, Stenerud, Kristian Lindalen, Di Ruscio, Francesco, White, Richard, Frigessi, Arnoldo, de Blasio, Birgitte Freiesleben |
| المصدر: |
PLoS Computational Biology; 1/23/2023, Vol. 19 Issue 1, p1-26, 26p, 2 Charts, 4 Graphs, 1 Map |
| مصطلحات موضوعية: |
SITUATIONAL awareness, SARS-CoV-2, INFECTIOUS disease transmission, FORECASTING, COVID-19, ECONOMIC databases |
| مصطلحات جغرافية: |
NORWAY |
| مستخلص: |
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. Author summary: National and regional governments have imposed restrictions on their citizens to control the spread of the SARS-CoV-2 virus, which caused disruption of their lives. This pandemic has been geographically and temporally extremely complex and heterogeneous, difficult to understand and predict. Important decisions assumed homogeneity across local communities, regions and time, because of a lack of instruments to rapidly capture regional and temporal differences. We present a complete probabilistic model to estimate regional processes in space and time, focussing on operational usefulness in real time. It uses real-time mobile phone mobility data, laboratory-confirmed cases, data on cases imported from abroad, and hospitalisation incidence. We propose a new calibration method to estimate regional reproduction numbers, which handles the high dimensional parameter space. The model has been successfully used for local situational awareness and forecasting in Norway, contributing to one of the most successful handlings of the epidemic in Europe. We estimate both national and region-specific reduction effects of the lockdown. We find larger reduction effects in the more populous regions compared to the national average, where we estimate a reduction of 85% in the transmissibility (95% CI 78%-89%). [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
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