Data Sharing in Southeast Asia During the First Wave of the COVID-19 Pandemic
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
Background: When a new pathogen emerges, consistent case reporting is critical for public health surveillance. Tracking cases geographically and over time is key for understanding the spread of an infectious disease and effectively designing interventions to contain and mitigate an epidemic. In this paper we describe the reporting systems on COVID-19 in Southeast Asia during the first wave in 2020, and highlight the impact of specific reporting methods.
Methods: We reviewed key epidemiological variables from various sources including a regionally comprehensive dataset, national trackers, dashboards, and case bulletins for 11 countries during the first wave of the epidemic in Southeast Asia. We recorded timelines of shifts in epidemiological reporting systems and described the differences in how epidemiological data are reported across countries and timepoints.
Results: Our findings suggest that countries in Southeast Asia generally reported precise and detailed epidemiological data during the first wave of the pandemic. Changes in reporting rarely occurred for demographic data, while reporting shifts for geographic and temporal data were frequent. Most countries provided COVID-19 individual-level data daily using HTML and PDF, necessitating scraping and extraction before data could be used in analyses.
Conclusion: Our study highlights the importance of more nuanced analyses of COVID-19 epidemiological data within and across countries because of the frequent shifts in reporting. As governments continue to respond to impacts on health and the economy, data sharing also needs to be prioritised given its foundational role in policymaking, and in the implementation and evaluation of interventions.
Article activity feed
-
-
SciScore for 10.1101/2020.10.23.20217570: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:An important limitation of this study is the absence of an assessment on data quality. This evaluation was not carried out because of the fast progression of the pandemic with corresponding rapid changes in data reporting. The lack of an up-to-date and complete line list also prevents a thorough assessment of data quality. …
SciScore for 10.1101/2020.10.23.20217570: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:An important limitation of this study is the absence of an assessment on data quality. This evaluation was not carried out because of the fast progression of the pandemic with corresponding rapid changes in data reporting. The lack of an up-to-date and complete line list also prevents a thorough assessment of data quality. Lastly and most importantly, an evaluation of data quality also requires the consideration of other indicators such as flexibility, representativeness, data security and system stability to provide a more accurate picture of health systems and disease surveillance systems.7 These information are not readily available and require more resources to be collected. Despite such caveats, however, this study is the first to systematically describe and compare reporting of important epidemiological data for COVID-19 across countries during the first wave. Our findings will allow researchers to conduct more nuanced analyses using epidemiological data of COVID-19. In conclusion, reporting systems in the region have been quickly established and countries provided detailed individual-level data during the first wave. This pandemic highlights the critical role of timely, accurate, and precise data sharing during outbreaks of global scale. Some concerns regarding data sharing remain, such as data privacy and public criticisms.3,4 Given that sharing of data is needed for evidence-informed policies and interventions, maintaining and strengthening data reporting systems sho...
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.
-
