Sequential Data Assimilation of the Stochastic SEIR Epidemic Model for Regional COVID-19 Dynamics
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Abstract
Newly emerging pandemics like COVID-19 call for predictive models to implement precisely tuned responses to limit their deep impact on society. Standard epidemic models provide a theoretically well-founded dynamical description of disease incidence. For COVID-19 with infectiousness peaking before and at symptom onset, the SEIR model explains the hidden build-up of exposed individuals which creates challenges for containment strategies. However, spatial heterogeneity raises questions about the adequacy of modeling epidemic outbreaks on the level of a whole country. Here, we show that by applying sequential data assimilation to the stochastic SEIR epidemic model, we can capture the dynamic behavior of outbreaks on a regional level. Regional modeling, with relatively low numbers of infected and demographic noise, accounts for both spatial heterogeneity and stochasticity. Based on adapted models, short-term predictions can be achieved. Thus, with the help of these sequential data assimilation methods, more realistic epidemic models are within reach.
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SciScore for 10.1101/2020.04.13.20063768: (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: We detected the following sentences addressing limitations in the study:There are obvious limitations within our current modeling framework, which we did not address because of our methodological focus. Longer-term predictions (∼ months) are important, but they critically depend on an estimation of undocumented infections (see Li et al. [16]). Such hidden infections create, after recovery, an unknown reduction in the number of susceptibles, which slows down the epidemic dynamics; such an effect is …
SciScore for 10.1101/2020.04.13.20063768: (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: We detected the following sentences addressing limitations in the study:There are obvious limitations within our current modeling framework, which we did not address because of our methodological focus. Longer-term predictions (∼ months) are important, but they critically depend on an estimation of undocumented infections (see Li et al. [16]). Such hidden infections create, after recovery, an unknown reduction in the number of susceptibles, which slows down the epidemic dynamics; such an effect is currently not included in our current model. However, it seems compatible with our framework to extend the SEIR model by an additional class of undocumented infected individuals [16]. Another important limitation of these results comes from the simplifying assumption that there is no coupling to neighboring regions. As a consequence, the regional differences in the contact parameter could be at least partly due to differences in the contacts between the regions. Couplings between the regions [16] could also be integrated into our modeling framework. However, the non-coupling approximation might be realistic in the situation of social distancing and travel bans during the period investigated here.
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.
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