Evaluating COVID-19 reporting data in the context of testing strategies across 31 low- and middle-income countries
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Abstract
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SciScore for 10.1101/2021.02.11.21251590: (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:There are limitations in our analysis related to the data themselves as well as our methods. Simultaneously, these data challenges are precisely the motivation for developing our methods: maximizing information with limited data. Our data are potentially biased by unmeasured factors such as fluctuations in testing capacity and …
SciScore for 10.1101/2021.02.11.21251590: (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:There are limitations in our analysis related to the data themselves as well as our methods. Simultaneously, these data challenges are precisely the motivation for developing our methods: maximizing information with limited data. Our data are potentially biased by unmeasured factors such as fluctuations in testing capacity and undocumented population sampling strategies over time, delays and temporal uncertainty due to reporting systems, and incentives for case-finding. Defining co-occurrence when working with imprecise time series is a challenge, which we partially mitigate by considering uncertainty bounds when defining change groups. We emphasize, of course, that co-occurrence does not establish causality. In PELT change detection, the changes detected are influenced by the choice of the sparsity parameter. In a sensitivity analysis of our novel parameterization approach, however, we find that Rwanda remains the leader in surveillance system performance, regardless of the parameterization choice. Results from this analysis highlight that surveillance data must be used carefully to ensure proper programmatic responses. As a sufficient and less resource-intensive approximation of random sampling, open testing would enable better estimation of disease prevalence and examination of NPI impacts in geographies without reliable hospitalization data, death records, or seroprevalence surveys. NPIs without epidemiological changes may indicate inefficacy of policy, but may also indic...
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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