The impact of mobility network properties on predicted epidemic dynamics in Dhaka and Bangkok
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SciScore for 10.1101/2021.02.07.21250586: (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: We detected the following sentences addressing limitations in the study:Multiple limitations of our modeling approach are important to consider. The stochastic model used in this study assumes that mixing is homogeneous in each compartment and assumes that co-location in the same node is an adequate proxy for person-person interaction. Also, this model does not account for potential differences in the …
SciScore for 10.1101/2021.02.07.21250586: (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: We detected the following sentences addressing limitations in the study:Multiple limitations of our modeling approach are important to consider. The stochastic model used in this study assumes that mixing is homogeneous in each compartment and assumes that co-location in the same node is an adequate proxy for person-person interaction. Also, this model does not account for potential differences in the average time spent in each location (“dwell time”), which is likely to vary across locations resulting in differences in average daily force of infection by location, nor does it account for geographic heterogeneity in age or household structure. Additional studies, comparing directly observed person-person interactions (for example, via Bluetooth handshake [27] or RFID sensors [28]) to mobility trajectories estimated from CDR and other passively-collected mobile data sources, are needed to better understand the limits of CDR-based mobility estimates in this context. Lastly, we initialize our model with a homogeneously (and entirely) susceptible population, and as such this approach is poorly suited for understanding endemic infections where there is pre-existing immunity that may be heterogeneous between demographic groups and across geographic space. In conclusion, this study describes previously unreported characteristics of mobility networks in Dhaka and Bangkok, and reports important differences in the trajectories of simulated epidemics propagated over these networks. Our findings support the continued development of passively-collected mobile...
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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