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If you are not an epidemiologist, you may be overwhelmed by all of the data being presented about COVID-19.  What do the experts know about the data that you don’t know?  The article, Eleven Epidemiological Fallacies in COVID-19, Prevent Epidemics discusses misconceptions about COVID-19 data. It challenges you to consider how an appreciation of data analysis is just the first step toward being truly informed in decision-making during a pandemic. 

On the surface, the easiest COVID-19 indicators to absorb are case incidence, case trends, and deaths; however, these indicators are significantly affected by population, clustering, time intervals, estimation gaps, inaccurate proxies for risk, appealing but limited “one number” predictors, simplified testing, shortsighted measures of readiness capacity, and overlapping test positivity results that reduce the relevance of testing outcomes. Once you understand these misconceptions, you can begin to increase your fluency, or literacy, in evaluating the message-validity of unfamiliar data and data terminology that is now part of our daily communication.

Learn more about CCNY’s Data Analytics Toolkit: