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Area Development - Site Selection, Facility Planning and Workforce Development

Data-Driven Operational Strategies in the Pandemic Era

As companies look to make decisions on ramping up operations post–COVID-19, they can look to data that provide insight on specific locations’ pandemic-related risks.

Q3 2020
As state economies struggle between surging cases of COVID-19 and intense pressure to reopen — albeit with varying degrees of restrictions — companies that reduced or halted operations in response to stay-at-home orders are torn between the urgent needs to both restore revenue streams and to ensure the safety of their employees and customers. Since every location has a different dynamic and risk profile, creating a return-to-work strategy is complicated, whether a company operates in five locations or 500. The solution to developing a sound, portfolio-wide strategy includes having ready access to data, analytics, and insights, including regional and community-specific risk factors. Much of this data is available free-of-charge and can be easily integrated with traditional location data and information around corporate real estate portfolios.

The Power of Pandemic-Relevant Data
Understanding where the hotspots are or could emerge and how pandemic-related risks will impact employees and communities across your portfolio locations is critical. Competing considerations for restoring operations include questions such as:
  • How likely is it that a COVID-19 surge will occur in a certain community?
  • Is the community prepared to handle an outbreak?
  • Is there adequate access to healthcare for my employees?
  • How could my workforce be affected, and what percent of my workforce should return to the office versus work from home?
Fortunately, there’s plentiful, free data that enable companies to evaluate pandemic-related risks. Until recently, much of that data resided in separate, difficult-to-navigate databases. Now, free platforms like ViralInsights.ai, among others, provide detailed views into a variety of critical demographic and behavioral data that companies can use to evaluate risk. For example:
  • What percent of the local population suffers from the range of pre-existing conditions that we now know contribute to higher rates of death and disability?
  • What level of local healthcare resources are available if there is an outbreak — such as local healthcare specialists who can intubate ventilators — and a host of other relevant data points that can increase the possibility of hospitalizations and fatalities in a given area should a surge in COVID cases occur?
  • What percent of the local population is most at risk because they take public transportation to work?
  • What percent of the local workforce worked from home prior to COVID-19?
Understanding where the hotspots are or could emerge and how pandemic-related risks will impact employees and communities across your portfolio locations is critical. Looking across these multiple data points can help to identify risks prior to an outbreak. For example, back in mid-May, the senior management of a major employer in Atlanta felt pressure to re-open office operations in that city after the state began allowing businesses to re-open in late April. Our analysis showed that even though COVID-19 was not yet prevalent in the local area, there were troubling behavioral risk factors in the local population that compared strikingly to New York and Boston. Further, the positive test rates, even with limited testing available, were at 10 percent. After examining the data, the company was strongly counseled to consider delaying their reopening. By June 20, Georgia was reporting over 1,000 COVID-19 cases per day, escalating to an average of over 3,500 cases per day by early August. On July 10, Atlanta rolled back its reopening to Phase One standards after the state peaked at 5,000 daily cases.

Understanding a Moving Target Through Moblity Data
We are learning more every day about the variables that impact the spread or reduction in COVID-19 transmission — it is truly a moving target. Some companies also are examining mobility data as part of their return-to-work strategy, measuring people’s movement around a community via anonymized cell phone data and correlating it to the number of COVID-19 cases over time.

We are learning more every day about the variables that impact the spread or reduction in COVID-19 transmission — it is truly a moving target. Data that tracks personal mobility can be an important tool in understanding relative risk. As local areas drop restrictions, we can look at how much time the population around our corporate locations and in the communities where our employees reside is spending on public activities, or how often people are visiting shops, restaurants, offices, and other places. Mobility patterns reveal the extent to which the new guidelines have changed personal movements and help to reveal the risk of community transmission of COVID-19.

How does movement align with outbreaks? MobilityMonitor.US is a free tool that tracks, summarizes, and visualizes where people are spending time, whether at home or out running errands, working, or otherwise interacting within their city. Using anonymous data generated by cellphones, we can see whether people are going out on the town or simply staying home. Mobility Monitor also enables us to correlate mobility over time (since early April) with the number of cases of COVID-19 and informs whether community levels are above or below average nationally. Custom reports and origin-and-destination studies are also available through the platform, helping to indicate employee risk at the neighborhood level and what level of risk employees may face from commuting to work.

The COVID-19 pandemic is unprecedented and requires us to use new sources of data in thoughtful and creative ways to help us consider cause and effect and relative risk. Fortunately, technology and data companies are constantly innovating new ways to aggregate and present data that can help us make more informed, data-rich decisions in this difficult time.

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