Regional probabilistic situational awareness and forecasting of COVID-19
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Abstract
Mathematical models and statistical inference are fundamental for surveillance and control of the COVID-19 pandemic. Several aspects cause regional heterogeneity in disease spread. Individual behaviour, mobility, viral variants and transmission vary locally, temporally and with season, and interventions and vaccination are often implemented regionally. Therefore, we developed a new regional changepoint stochastic SEIR metapopulation model. The model is informed by real-time mobility estimates from mobile phone data, laboratory-confirmed cases, and hospitalisation incidence. To estimate locally and time-varying transmissibility, case detection probabilities, and missed imported cases, we present a new 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, suitable for real-time surveillance.
Significance
We developed a regional infectious disease spread model focussing on operational usefulness in real time. The model is informed by near real-time mobile phone mobility data, laboratory-confirmed cases, and hospitalisation incidence. The model is used to estimate reproduction numbers and provide regional predictions of future hospital beds. Regional reproduction numbers are important due spatio-temporal heterogeneity due to for example local interventions. We assume different regional reproduction numbers for different periods of the epidemic. We propose a new calibration method to estimate the reproduction numbers and other parameters of the model, tailored to handle the increasingly high dimension of parameters over time. The model has been successfully used for local situational awareness and forecasting for the Norwegian health authorities during COVID-19.
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SciScore for 10.1101/2021.10.25.21265166: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this …
SciScore for 10.1101/2021.10.25.21265166: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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