Can Search Query Forecast successfully in China’s novel coronavirus (2019-nCov) pneumonia?
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Abstract
Recently the novel coronavirus (2019-nCov) pneumonia outbreak in China then the world, and the Number of infections and death continues to increases. Search Query performs well in forecasting the epidemics. It is still a question whether search engine data can forecast the drift and the inflexion in 2019-nCov pneumonia. Based on the Baidu Search Index, we propose three prediction models: composite Index, composite Index with filtering and suspected NCP(Novel Coronavirus Pneumonia). The result demonstrates that the predictive model of composite index with filtering performs the best while the model of suspected NCP has the highest forecast error. We further predict the out-of-the-set NCP confirmed cases and monitor that the next peak of new diagnoses will occur on February 16 th and 17 th .
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SciScore for 10.1101/2020.02.12.20022400: (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 10.1101/2020.02.12.20022400: (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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