Global monitoring of the impact of the COVID-19 pandemic through online surveys sampled from the Facebook user base

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1. Version published to 10.1073/pnas.2111455118

   Dec 13, 2021
2. 

   ScreenIT

   Jul 10, 2021

   SciScore for 10.1101/2021.07.05.21259989: (What is this?)

   Please note, not all rigor criteria are appropriate for all manuscripts.

   Table 1: Rigor

   Ethics	IRB: UMD-CTIS Survey: This research is based on survey results from UMD-CTIS, approved by the UMD Institutional Review Board (IRB, 1587016-10) and described previously(19). Consent: After obtaining consent in Facebook, users were pushed to the Qualtrics platform utilizing a token system.
   Sex as a biological variable	The input features were self-reported age-gender categories (using self-reported age 18-54 versus ≥ 55 years, gender male versus female) and responses to 12 symptoms queried in versions 1-6.
   Randomization	Qualtrics implemented standard survey administration methods (e.g. response randomization).
   Blinding	not detected.
   Power Analysis	not detected.

   Table 2: Resources

   Software and Algorithms
   S…

   SciScore for 10.1101/2021.07.05.21259989: (What is this?)

   Please note, not all rigor criteria are appropriate for all manuscripts.

   Table 1: Rigor

   Ethics	IRB: UMD-CTIS Survey: This research is based on survey results from UMD-CTIS, approved by the UMD Institutional Review Board (IRB, 1587016-10) and described previously(19). Consent: After obtaining consent in Facebook, users were pushed to the Qualtrics platform utilizing a token system.
   Sex as a biological variable	The input features were self-reported age-gender categories (using self-reported age 18-54 versus ≥ 55 years, gender male versus female) and responses to 12 symptoms queried in versions 1-6.
   Randomization	Qualtrics implemented standard survey administration methods (e.g. response randomization).
   Blinding	not detected.
   Power Analysis	not detected.

   Table 2: Resources

   Software and Algorithms
   Sentences	Resources
   Clustering for visualization used benchmark data (using row clustering method=”complete” in Heatmap from ComplexHeatmap 2.3.4, R 3.6.3 https://www.R-project.org/).	ComplexHeatmap suggested: (ComplexHeatmap, RRID:SCR_017270)
   Four base modeling methods (Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, and Light Gradient Boosting Machine) were compared before determining that Light Gradient Boosting Machine (LightGBM, lightgbm in Python 3.8 with Shapley values visualization package for predictor visualization) was the highest performing at a moderate time cost, and is presented throughout.	Python suggested: (IPython, RRID:SCR_001658)

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   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).

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   Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

   Despite acknowledged possible limitations, the potential value of the UMD-CTIS is substantial. Compared to the Gallup World Poll, the largest global survey using established methodology, the UMD-CTIS survey is conducted on a daily (not annual) basis, and using a social-media based instrument instead of telephone and in-person interviews(38). Gallup World Poll interviews about 1000 subjects per country per year, while the UMD-CTIS sampled five to ten times that per country per week with a fraction of the in-country infrastructure and human resources(39). The great majority of the cost and logistical resources of the survey and deployment are borne by Facebook and the UMD Joint Program in Survey Methodology (JPSM). The purpose of the UMD-CTIS is not to provide exact and unbiased on-time point estimates, but to allow for the analysis of time trends and small area estimation. With this, UMD-CTIS is the largest ongoing real-time, remote, global health survey ever conducted. Importantly, the survey serves many countries that have been relatively under-represented in COVID-19 biomedical literature to date. The other covariates in UMD-CTIS provide a valuable window into impacts on populations that may be underrepresented in the COVID-19 biomedical literature. We highlight in this study only a handful of these, including population-level concern about COVID-19-related illness, in relation to temporal changes in self-reported mitigation strategies. Each country experienced COVID-19 dif...

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   Results from TrialIdentifier: No clinical trial numbers were referenced.

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   Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

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   Results from JetFighter: We did not find any issues relating to colormaps.

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   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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   Results from scite Reference Check: We found no unreliable references.

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3. 

   Version published to 10.1101/2021.07.05.21259989v1 on medRxiv

   Jul 7, 2021