Todd is the CEO and Co-Founder of OmniSci, the pioneer in accelerated analytics that enables businesses to uncover important insights.
The first phase of the global Covid-19 response relied heavily on data and analytics. From the onset of the pandemic, state and federal governments have accessed data reports from numerous sources on the number of Covid-19 cases, deaths, recoveries and more.
At Johns Hopkins University, the Center for Systems Science and Engineering created a user-friendly Covid-19 dashboard to give researchers, public health authorities and the general public access to pandemic data in real time. Worldometer created a coronavirus disease statistics page with global infection data, and Nexstrain continues to offer the latest analyses of all Covid-19 strains thus far, compiled from the Nexstrain team and independent virologists across the globe.
As pandemic mitigation strategies mature, however, decision-making is increasingly done at the local level. Outbreaks may necessitate a temporary return to limited stay-at-home restrictions, and refinements to local retail and business operating practices are continually being made. Authorities are looking to measure the effectiveness of these policy changes to minimize community impact while optimizing public health measures.
Such decision-making requires access to large volumes of trustworthy data and from multiple sources. Officials need information that is easily integrated, location-specific and empowered for rich interrogation, regardless of its size or scale — which is not an easy task.
Insight Through Geospatial Data
By combining pandemic data with other datasets, such as point-of-interest data, population movement, publicly available geographic data (including demographic information and administrative boundaries) and anonymized cellphone location data, governments and public health agencies can fight the local fight more effectively through sensible business directives, detailed contact tracing and smart guidelines for personal behavior. Here are a few examples of how state, county and local authorities are putting geospatial data to work:
Apple and Google have taken on the formidable job of creating a way to trace the interpersonal spread of Covid-19 at the most granular level. Beginning with major contact points including border areas, ports and airports, tracing solutions must be able to examine travel and movement patterns to identify specific individuals and their social units.
Understanding how effective social distancing measures are depends on close measurement of both geographic and temporal dimensions. Policymakers need to know the answers to questions such as:
• How many people in a particular hot spot population traveled more than a given distance from their home?
• Is the potential for spread affected by the distance individuals traveled away from home, as measured by the baseline of cases and the population density?
• Are locations that have an increased difficulty in executing effective social distancing always at a greater risk of contracting Covid-19? Are rural areas more at risk?
In order to answer these questions, both demographic and movement data must be integrated. Moreover, policymakers must then have the ability to examine the integrated data at several levels. Analyses have shown that some regions have been slower at limiting movement away from home than others, which may lead officials to prepare for a rapid spread of the virus. By understanding the efficacy of various measures, authorities can apply them more broadly and combat transmission more efficiently.
As relief efforts take hold, it’s vital for leaders to ensure that community healthcare resources are never again placed at a deficit. Keeping new cases of coronavirus within local capacities requires the integration of several sources of data, including public data healthcare logistics and information about existing, chronic conditions.
Comorbidity of Acute Respiratory Distress Syndrome with Covid-19 within local and regional populations, for example, is a critical factor in determining whether local resources are sufficient to address changing needs.
Correct — or incorrect — mitigation decisions at workplaces can have a profound effect on surrounding communities. The experiences of meatpacking plants in Colorado, Nebraska and South Dakota underscore the importance of policies to prevent work facilities from becoming coronavirus hot spots.
Accelerated analytics can process billions of records from millions of sources to investigate Covid-19 outbreaks and, where necessary, tie them back to specific workplaces. Data from mobile devices (opt-in and anonymized) can be aggregated with point-of-interest data and cross-referenced with infection cases to determine sources, identify exposure patterns, and predict future outbreaks.
The Fight Is Local
If we have learned anything to date, it’s this: While the pandemic is global in nature, the battle must be fought locally. Federal policy guidelines can and must inform local decisions, but they cannot override thoughtful, scientifically grounded evaluation at community and regional levels.
This crisis does not allow for the luxury of ivory-tower analysis. Data must be made available to the thousands of practical decision-makers who need it every day, along with the tools necessary to accurately interrogate it. The Covid-19 challenge is dynamic, multidimensional and often unpredictable — which makes massive amounts of rich data, presented for granular geospatial interrogation, one of the most potent weapons we have in our mitigation arsenal.
Geospatial data at scale is not the same as other types of data at scale due to rendering. Organizations will want to look for tools that understand that difference natively. Look for public datasets that can add deeper geospatial relevance to your existing data, such as Placekey, which is a public standard that helps connect street addresses to geolocation to add relevance to existing data.
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