MIT Sloan Health Systems Initiative
Catherine Tucker: The Role of Delayed Data in the COVID-19 Pandemic
COVID-19 policy, in part, relies on COVID-19 infections data that is gathered from state databases. Professor Tucker’s research shows that not accounting for delays in reporting leads to misguided policy decisions that may have a detrimental effect on public health.
There are several possibilities for reporting delays, but Tucker and her student Yifei Wang focus their research on delays caused by states’ reliance on outdated fax-based reporting, which leads to severe delay in reported actual number of cases and short-term fluctuations in reported numbers. The delay matters because, as Tucker shows, reporting delays lead to incorrect, ineffective policy decisions to deal appropriately with the pandemic. She concludes that if adjustments were made to correct for reporting delays, several major policies would be different.
Tucker focuses on six different policies:
- stay-at-home/shelter-in-place orders
- mandating face mask use by all individuals in public spaces
- closing non-essential businesses
- closing K-12 schools
- closing restaurants except take-out
- closing bars
As restaurants and bars are regarded as essential businesses in many states and therefore subject to separate closing and reopening schedules from non-essential businesses, the researchers treat them as separate policy variables from closing non-essential businesses orders in their analysis.
If policy makers assume no delays in reported numbers, the data analysis would lead to recommendations to shut down restaurants, open bars, and lift stay-at-home orders as soon as possible. They should also relax policies such as mask mandates and closing businesses. It seems that assuming some delay makes sense since assuming no delay leads to somewhat strange policy recommendations.
If policy makers assume that reporting delays are the same in every state, the analysis would lead to a strong recommendation for wearing masks and closing non-essential businesses., policies two and three in the researchers list of policies under review.
However, states use different methods and processes for testing and reporting, so it is more accurate to assume that the reporting delays are different for different states. With that more accurate assumption, the data analysis still would recommend mask mandates and closing non-essential business, but now the protective effect is even stronger than in the scenario above, where reporting delays are the same in every state. Furthermore, under these assumptions, there is also evidence of a large beneficial effect of closing K-12 schools.
On the other hand, policies such as stay-at-home orders and closing restaurants and bars are consistently estimated to have insignificant effects no matter how Tucker adjusted for reporting delay.
Tucker’s research clearly demonstrates the need to take reporting delays into considerations when analyzing COVID-19 data. Without doing so, policy makers may not recommend the most beneficial interventions. More generally, this research may lead to questioning the effectiveness of current data-management and reporting practices and the myriad differences among the states.