As of March 3rd, approximately 80 million vaccines have been administered in the United States. More than 28 million COVID-19 cases have been reported. Over 500 thousand deaths have been noted.
Statistics like these help both the public and officials gauge the severity of and progress towards halting the current global pandemic. Without statistical data and analysis, one wouldn’t know that COVID-19 is 7,900 times more deadly for elderly individuals in comparison to children, or that black Americans are 2.9 times more likely to get hospitalized for the disease in comparison to white Americans.
Statistics help officials recognize who is at greater risk—such as elderly, immunocompromised, or POC individuals, what policies are effective in curbing the spread of COVID-19, and play a critical role in the management of the pandemic’s effects. However, the surplus of statistical illiteracy in the United States makes it so that many are left either questioning the reliability of presented data or perpetuating malinformed interpretations of said data.
One major criticism of this pandemic’s statistical modeling is the plethora of erroneous predictions from epidemiologists. Despite models initially predicting massively overwhelmed hospitals in the first four weeks of lockdown, most hospitals didn’t experience major stress on their resources. There were also predictions of the US reaching 100 million cases within the first few months of 2020, yet the US still hasn’t reached that number of cases as of today. These overestimations have led people to believe that such predictions are dodgy and unstable at best, ignoring the fact that the whole purpose of statistics is to deal with uncertainty. Statistics don’t confirm, but they do correlate and approximate.
Additionally, the media and general population are quick to compare and employ data without looking at any overarching variables. In an effort to diminish the severity of the coronavirus pandemic, some individuals pointed out in early 2020 that there were more flu deaths per year than COVID-19 deaths, ignoring the fact that the influenza has been around for far longer than the coronavirus, which originated in 2019 and thus has affected less people. Since those early months, COVID-19 has been proven to be far more damaging than the flu, with a 249 percent greater likelihood of death.
Then there comes the issue of state and country comparisons. Yes, New York has more COVID-19 cases than Texas, but it also has a greater population and population density than Texas. China has fewer cases than the US, but the People’s Republic doesn’t account for positive asymptomatic cases like the US does. One cannot look at raw data alone; it is necessary to account for all variables such as population density, data accumulation methods, how COVID-19 deaths are determined, and more.
The general population doesn’t know what to look for when they observe models and data. Some see a 1.4 percent fatality rate and assume the virus isn’t deadly, ignoring the fact that 2020 ended with around 10 percent more deaths compared to 2019 and 2018, outpacing it. 78 percent population growth. Some look at COVID-19 surveys centered around elderly people and presume it extends to young Americans.
Both officials and individuals have to educate themselves and work to curb statistical illiteracy, which can lead to the spread of misinformation and underestimation of the severity of the pandemic. Officials need to ensure that their models not only present raw data, but also data in accordance with variables like compliance and population size. Individuals need to ensure that the studies they’re looking at are verifiable and generalizable, while also acknowledging that statistical predictions aren’t guaranteed to be accurate.
Data and analysis are duly important during times that rely on numbers and science. Ensuring that everyone understands that data is the key to communicating the importance of stopping the spread of COVID-19.