How to a Make Data-Informed Plan for Reopening School

August 5, 2020

By Navin Vembar

Over the past few weeks, we’ve seen schools announce reopening plans and quickly rescind or augment them as COVID-19 continues to wag its long tail across the US. In Indiana, for example, a single Coronavirus case forced a school-wide quarantine just hours after students had returned to school. Universities with students returning from multiple states face even more complex decisions about how to safely meet their obligation to educate, while keeping the students and their surrounding communities safe. 

According to EdWeek data, as of July 23, 9 of the 15 largest school districts are choosing remote learning-only as their back-to-school instructional model, affecting over 2 million students. Complicating this, the School Superintendents Association estimated that an average school district—which includes roughly 3,600 students, eight buildings, over 300 staff—would need $1.8 million just to meet basic reopening needs under pandemic conditions. 

Faced with the challenging task of developing strategic and safe reopening plans, school district leaders and university teams are most prepared to make the best decision for their student bodies when they’re equipped with the right data inputs. 

Key Inputs for an Informed Decision

In late July, the CDC issued their own guidelines for reopening schools. Though the guidelines provide some strategies for finding a solution that works for individual schools, districts, and communities, they lack the kind of scientific detail on outcomes that mobility data can provide when paired with other critical inputs. 

As leaders make these decisions, which aren’t likely to ease up for many months, they should consider each of the following inputs to develop a comprehensive approach to returning to school:

  • Mobility data. Mobility data can help decision-makers understand where to direct resources, risk areas, which areas are most likely to become hot spots, and where social distancing measures are and aren’t working. Ideally, mobility data is obtained at the state level, by the governor’s office, and then strategically deployed to local offices. 
  • Data on outcomes. Science Magazine’s publication details strategies, and their outcomes, as schools reopen across the globe. It’s important to understand what the results are of different measures as schools work to keep Coronavirus at bay.
  • Case counts. Local case counts are critical inputs as districts making reopening decisions. These can be found on county health department websites. 
  • Operational considerations. The school’s toolbox for developing operational measures that fit their return-to-school needs will include things like learning from home, using masks, and other social distancing measures.
  • Government data. Data like the CDC’s is critical in understanding emerging truths about the virus’s impact, recommended proactive measures for managing impact, and national trends. 

Why Use Mobility Data?

Every decision-maker should be armed with this data as one of the tools they have to manage their COVID-19 response. Insofar as we‘re able to identify hot spots using predictive analytics—because we understand movement and human connections to location—mobility data can be used by state and district governments to develop a return to school approach in a number of ways: 

  • Tracking statistical patterns of movement that flow around both schools and where students come from, especially as it may affect where the risk of spreading the disease may originate (though, to be clear, kids under 18 aren’t included in mobility data sets in general)
  • Surfacing areas of greater vulnerability and risk
  • Uncovering inequity gaps

Some universities are coming back part-time, while others are returning full-time or not at all. These decisions are often driven by economics. There’s a lot of nuances revealed by mobility metrics; patterns of movement will vary based on economic factors and the types of jobs people tend to have. 

State and Local Coordination

As we mentioned, mobility data is a key tool for a governor’s office. It lets state-level leadership understand that some districts could open with less risk than others. This may mean that a blanket state policy isn’t the best approach to reopening. However, states can only determine that when they have the right data. 

State governments should think through their return policies in terms of schools in a given district, or a given census tract. When states are able to get a clear picture of a local situation, they can allocate resources accordingly to higher risk areas. These areas can include those where parents don’t get to work from home and lower-income areas. This enables governments to quickly see which groups are vulnerable. When good mobility data is paired with inputs such as those described in this post, it will be plain to see what is needed, and where to direct help from the state. 

It’s important to understand that school districts are different from one another. It is unlikely that any state will have a one-size-fits-all solution that works. Mobility data tells you which districts and schools need more support and allows the state to make tactical decisions, such as allocating worker hours and masks. These things aren’t in the CDC guidelines, but they should be thought through by decision makers; that’s why we recommend gathering a number of different inputs to make the decision.

Mobility data helps governments understand risk and district-level insights, giving them one more layer of visibility to make a go or no-go decision. But an informed decision won’t be made with mobility data alone. Every decision maker faces a multivariate scenario. One of the most accessible pieces of information available is measuring mobility data, providing insights that are distinct and valuable. Mobility data can be a useful proxy for risk based on our scientific understanding of how to track this data.

Contact us to learn more about how mobility data can assist your state in guiding data-informed reopenings.

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