Amanda Peterson-Plunkett, CHANCE Executive Editor

It has now been more than a year since SARS-CoV-2 began spreading across the globe. Researchers understand the virus better, and vaccines are being distributed—but what do we know about the effects the pandemic has had on our economy and society? Authors Jon T., Nicholas B., and Thomas Middleton study this question as it pertains to the United Kingdom in the article, “Modeling the Economic and Societal Impact of Non-Pharmaceutical Interventions During the COVID-19 Pandemic.”

Keeping with the topic of COVID-19, we look at an application of Benford’s law, a tool that has successfully been used in applications such as detecting financial fraud and altering digital images. In “Benford’s Law and COVID-19 Data,” authors Chase Marchand and Dalton Maahs use it to analyze reported COVID-19 cases.

In “The Secret Career of Solomon Kullback,” historian Brenda McIntire details the US intelligence career of one of the developers of the Kullback-Leibler divergence. If you are intrigued by this article, you may also enjoy the recent PBS special The Codebreaker featuring one of Kullback and Leibler’s contemporaries, Elizabeth Friedman.

Societal benefits abound from sharing data and machine learning models built on those data, but data have the potential to be biased, resulting in biased models. What are the possible repercussions? Who should be held accountable? What methods can be employed to avoid data bias? Charna Parkey tackles these questions in “Who Is Accountable for Data Bias?”

In the February issue of CHANCE, we included an article about the connection between road familiarity and traffic accidents. In this issue, authors Adam Palayew, Sam Harper, and James Hanley consider a different angle on the topic of traffic accidents. They note the study of factors that influence accidents may be complicated by factors such as the season, day of the week, and time of day of the accident. The authors evaluate various methods to minimize the effect of these extraneous factors in “Toward Reducing the Possibility of False Positive Results in Epidemiologic Studies of Traffic Crashes.”

Listed as one of the 25 most-dangerous jobs in the US (based on 2019 data from the Bureau of Labor Statistics Census of Fatal Occupational Injuries), police work is not for the faint of heart. In “Police Officers Killed in the Line of Duty: A Correspondence Analysis of Circumstances and Time of Day,” Terry Allen investigates whether certain types of police activities are more dangerous at specific times of day.

Moved by the 2018 attack on the Pittsburgh Tree of Life synagogue, Howard Wainer and Richard Feinberg analyze data associated with hate crimes. In the Visual Revelations column article, “Looking at Reported Hate Crimes,” they zero in on statistics reported in New Jersey due to its uniqueness in having complete data going as far back as 1990.

In The Big Picture column article, “The Shape of Things: Topological Data Analysis,” Nicole Lazar and Hyunnam Ryu demonstrate the use of topological techniques for exploratory data analysis. If you work with complex data, this may be a technique to add to your toolbox.

Escape rooms aren’t just a social activity to enjoy with your friends. They can also be used in the classroom to reinforce new concepts. In the Taking a Chance in the Classroom column article, “The Data Science Instructional Escape Room: A Successful Experiment,” authors Valerie Nelson and Jason Crea explain their implementation of an escape room for a data science course.

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