Predicting COVID-19 incidence in French hospitals using human contact network analytics
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SciScore for 10.1101/2021.06.17.21258666: (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
Software and Algorithms Sentences Resources Curvature was calculated in the sense of discrete Ollivier-Ricci (OR) curvature [20] with a freely available Python code at https://github.com/saibalmars/GraphRicciCurvature. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has several limitations. First, by focusing on the elaboration of contact network analytics we have utilized rather elementary statistical models. These have the advantage to be easy to interpret, …
SciScore for 10.1101/2021.06.17.21258666: (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
Software and Algorithms Sentences Resources Curvature was calculated in the sense of discrete Ollivier-Ricci (OR) curvature [20] with a freely available Python code at https://github.com/saibalmars/GraphRicciCurvature. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has several limitations. First, by focusing on the elaboration of contact network analytics we have utilized rather elementary statistical models. These have the advantage to be easy to interpret, but, given the number of features, more sophisticated machine learning techniques such as long short-term memory neural networks or random forests might could allow extracting more information from the data. Second, the interpretation of colocation data as a proxy for infectious contacts remains to be validated in the field, e.g. in community transmission studies [26]. The validation would also require to concurrently taking into account the importance of hospital catchment areas when recording disease incidence [27]. Furthermore, assortativity between users and user coverage might introduce biases that do not reflect the extent of human contacts pertaining to disease transmission. Taken together, our time series analysis shows the potential of human contact network analytics to improve both predictions and a posteriori model fits of disease incidence as recorded during the COVID-19 pandemic in France. Combining network analytics with mechanistic models of disease transmission opens promising novel avenues for real-time disease control.
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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