Covid19 infection spread in Greece: Ensemble forecasting models with statistically calibrated parameters and stochastic noise
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Abstract
Following the outbreak of the novel coronavirus SARS-Cov2 in Europe and the subsequent failure of national healthcare systems to sufficiently respond to the fast spread of the pandemic, extensive statistical analysis and accurate forecasting of the epidemic in local communities is of primary importance in order to better organize the social and healthcare interventions and determine the epidemiological characteristics of the disease. For this purpose, a novel combination of Monte Carlo simulations, wavelet analysis and least squares optimization is applied to a known basis of SEIR compartmental models, resulting in the development of a novel class of stochastic epidemiological models with promising short and medium-range forecasting performance. The models are calibrated with the epidemiological data of Greece, while data from Switzerland and Germany are used as a supplementary background. The developed models are capable of estimating parameters of primary importance such as the reproduction number and the real magnitude of the infection in Greece. A clear demonstration of how the social distancing interventions managed to promptly restrict the epidemic growth in the country is included. The stochastic models are also able to generate robust 30-day and 60-day forecast scenarios in terms of new cases, deaths, active cases and recoveries.
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SciScore for 10.1101/2020.06.18.20132977: (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: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
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…
SciScore for 10.1101/2020.06.18.20132977: (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: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
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.
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