Can Crowdsourced Data on the Pandemic’s Spread Help Hospitals and Clinical Laboratories?

New COVID-19 data collection method provides potential valuable tool for clinical laboratories

A recent research initiative undertaken at Carnegie Mellon University (CMU) Delphi Research Center in collaboration with Facebook has led to the first large-scale “crowdsourced” data set that indicates the occurrence of COVID-19 symptoms and geographical distribution of these occurrences. This new initiative may affect how clinical laboratories approach epidemiological planning. It may also influence local governments to pursue additional testing, leading to a potential revenue source for laboratories that are prepared.

The data used to provide geographical prevalence of COVID-19 symptoms is obtained through a survey designed by CMU researchers. This survey asks people to self-report symptoms associated with COVID-19 or influenza that have occurred within the last 24 hours and is distributed by Facebook daily to samples of users across the United States.

“By distributing surveys to large numbers of people whose identities we know, we can quickly generate enough signal to correct for biases and ensure sampling is done properly,” Mark Zuckerberg, Facebook founder and CEO, wrote in a Washington Post op-ed.

The data is analyzed by Facebook for survey bias and weighted to ensure that the sample accurately represents the characteristics of the population that is represented in the data. The data are displayed in an interactive symptom map that identifies symptom density of influenza and of COVID-19 throughout the United States by county or by hospital referral regions.

Crowdsourced Data Tool has Implications for Clinical Laboratories

There are multiple implications that this new methodology of collecting epidemiological data will have for clinical laboratories and pathology groups. While at this immediate time, these effects primarily relate to the COVID-19 pandemic response, they do have the potential to have broader implications for other diseases if this powerful tool continues to be developed and implemented in new ways.

The first implication that clinical labs should consider is that this crowdsourced data may influence local governments and lead to a perceived need for increased testing in their area. Labs can monitor the demographic symptom data to evaluate trends in their areas.

Clinical laboratories may also benefit from utilizing this resource to monitor symptom trends and alert local governments to the potential need for increased testing in their localities. This can lead to increased revenue and public funding for laboratories, while further developing a collaborative relationship between these labs and their local governments.

Symptomology data from this study may also be coupled with local epidemiological data to better understand the effect in a specific laboratory’s locality and allow for a better comparison between that laboratory and collaborating entities in other locations. This allows labs to better adjust their response and better weight their local data, providing labs with a way to better allocate resources and plan capital expenses.