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

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,...

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Published in:	PLoS computational biology Vol. 19; no. 1; p. e1010860
Main Authors:	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
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 01-01-2023 Public Library of Science (PLoS)
Subjects:	Awareness Bayes Theorem Bayesian analysis Bayesian statistical decision theory Biology and Life Sciences Cellular telephones Communicable Disease Control Contact tracing Coronaviruses COVID-19 COVID-19 - epidemiology COVID-19 - prevention & control COVID-19 vaccines Datasets Disease transmission Epidemics Forecasting Forecasts and trends Geography Heterogeneity Hospitals Humans Immunization Infectious diseases Intensive care Laboratories Mathematical models Medicine and Health Sciences Metapopulations Methods Mobility Norway Pandemics People and Places Real time Regional development Sentinel health events Severe acute respiratory syndrome coronavirus 2 Situational awareness Statistical analysis Statistical models Statistics Stochasticity Telecommunications towers Vaccination Viral diseases Norway
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Summary:	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.
Bibliography:	new_version ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 The authors have declared that no competing interests exist.
ISSN:	1553-7358 1553-734X 1553-7358
DOI:	10.1371/journal.pcbi.1010860