Predicting Long-term Evolution of COVID-19 by On-going Data using Bayesian Susceptible-Infected-Removed Model
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Abstract
In this study, we propose a novel statistical method to predict a long-term epidemic evolution based on a on-going data. We developed a Bayesian framework for the Susceptible-Infected-Removed model (Bayesian SIR), and estimated its underlying parameters based on day-by-day timeseries of the cumulative number of infectious individuals. The new Baysian framework extends the deterministic SIR model to a probabilistic form, which provides an accurate estimation of the underlying system by a short and noisy data. We applied it to the data reported on the Coronavirus Disease 2019 (COVID-19), and made a month long prediction on its evolution. Our simulated test using past timeseries to predict the current data gives a reasonable reliablity of the proposed method. Our analysis of the current data detected and warned a rising trend in the countries in Central Asia, Middle East, and South America, while United States or European countries, which have already experienced large numbers of infected cases, are predicted to slow down in the increase.
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SciScore for 10.1101/2020.05.08.20094953: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. 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 …
SciScore for 10.1101/2020.05.08.20094953: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. 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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