COVID-19: Data Quality and Considerations for Modeling and Analysis
The rapid spread and magnitude of the COVID-19 pandemic have underscored the importance of having quality data, analyses, and models describing the potential trajectory of COVID-19 to help understand the effects of the disease in the U.S. The Centers for Disease Control and Prevention (CDC) is using multiple surveillance systems to collect data on COVID-19 in the U.S. in collaboration with state, local, and academic and other partners. The data from these surveillance systems can be useful for understanding the disease, but decision makers and analysts must understand their limitations in order to interpret them properly. For example, surveillance data on the number of reported COVID-19 cases are incomplete for a number of reasons, and they are an undercount the true number of cases, according to CDC and others.
Short title:
COVID-19: Data Quality and Considerations for Modeling and Analysis
Start date:
2020-07
End date:
2020-07
Homepage:
https://www.gao.gov/products/GAO-20-635SP
Project leader:
Science, Technology Assessment, and Analytics team of the U.S. Government Accountability Office (GAO) (STAA)
Country:
United States of America
Publications: