As we continue to fight COVID-19 and understand the best way to track its spread, there have been many different approaches to identifying COVID-19 incidence and mortality.

Recently, this has been done by following testing, hospitalizations, and deaths. However, these methods can often be inaccurate because the data are not collected in a uniform manner on a national scale, and there are discrepancies in how cases are counted. Also, testing is severely limited and the amount positive testers may be affected but who can actually receive the test. The tests are also not always reliable, and can lead to a gap in knowledge.

We aim to address these concerns through surveillance-based tools, including the COVID Symptom Study app. We collect additional information on disease risk factors, outcomes, and symptom development by asking specialized questions about how users are feeling every day, and whether they have been tested or not, and other essential information on their background.

Digital reporting has its own fallbacks, because it cannot follow all demographics, there are many out on the market currently, and data sharing and privacy is certainly a concern in acquiring these data. Despite these concerns, this information could lead to robust models which can guide the investigation and help to plan out public health responses to the outbreak. 

Our new paper, published in the New England Journal of Medicine, discusses how the spread of COVID-19 is being analyzed, what makes the current approaches not always reliable, and how using digital record methods can help to aid the efforts being made as we fight the disease as a community.

Read the full paper:

Putting the Public Back in Public Health — Surveying Symptoms of Covid-19