New Guide Helps Policymakers Use COVID-19 Data to Make Decisions
The guide applies five criteria to seven types of COVID-19 data to support decision-making during the pandemic.
Source: Thinkstock
By Jessica Kent
- Decision-makers should take several criteria into account when assessing the usefulness of COVID-19 data points, according to a new guide by the National Academies of Sciences, Engineering and Medicine.
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More information about how COVID-19 is impacting the nation is now available, the authors noted, but this data often comes in various forms and is not always complete. Different facts and figures about the virus can paint different pictures of the pandemic, leaving officials unsure of when to open and close businesses, schools, and community facilities.
“The COVID-19 pandemic is generating many different types of data about this disease in communities — things like the number of confirmed cases or the number of deaths in a particular area,” said Adrian Raftery, professor of statistics and sociology at the University of Washington.
“None of these data sources on their own are perfect in terms of capturing a complete and accurate summary of the prevalence of COVID-19 and the risks of doing certain things like opening businesses or schools. All have their own strengths and weaknesses.”
Policymakers have several COVID-19 statistics to choose from when making decisions regarding the pandemic, including number of confirmed cases, hospitalizations, emergency department visits, reported confirmed COVID-19 deaths, and other metrics.
Because the utility of data for decision-making is affected by many factors, policymakers have to understand the limitations of this data when implementing new rules. For example, the number of positive test results for coronavirus is likely an underestimate of its true prevalence in a community. Many people who have the virus are asymptomatic and aren’t likely to seek out a test, and even people with symptoms may not have access to tests and medical care.
Additionally, the number of COVID-19 deaths in a region does not reflect the disease’s current prevalence because the number of deaths lag behind the number of cases by several weeks.
To ensure laws reflect the current state of the pandemic, the authors stated that officials should consider data representativeness and whether the reporting represents the population of interest.
Lawmakers should also consider whether the data is biased and whether there are systematic factors that could cause the reported values to be underestimates or overestimates of the actual values.
Other considerations include uncertainty, measurement, and sampling error; time lags in reporting the numbers and consistent updates to the numbers; and whether the numbers cover all geographic areas of interest.
“We intend for this guide to help these decision-makers and their advisors interpret the data on COVID-19 and understand the upsides and downsides of each data source,” said Raftery.
The authors noted that over time, it may be possible to collect more revealing data about COVID-19 from what are known as representative random samples within a population. In representative sampling, people are surveyed at random for a disease, and certain populations can be more heavily sampled than others based on what scientists and officials have learned about a disease’s prevalence and susceptibility.
Representative sampling avoids biases and can more accurately estimate the disease’s prevalence in a region.
“As we learn more about COVID-19, how it spreads, how different populations are more or less susceptible, we may be able to move more in the direction of representative sampling,” said Raftery.
“The State of Indiana has already done a survey of this kind, and others should follow suit. But there is also a lot that officials can do with the statistics and data sources that hospitals and agencies are providing right now — provided that officials can be made aware of the strengths and weaknesses of each piece of data.”
With this new guide, lawmakers can ensure they have the most accurate, timely information to make more informed decisions.
“The COVID-19 pandemic is a reminder, once again, of the importance of evidence and a robust public health data infrastructure. Decision making related to the pandemic requires the use of data often not designed for the task at hand,” the authors concluded.
“With greater understanding of the strengths and limitations of these data, decision makers can make better decisions. Continued investment in public health and its data surveillance structures is needed to meet the nation’s current and future public health challenges.”
