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Unlike pandemics in the past, COVID-19 has hit us in the midst of the information age. We have built vast capabilities to collect and store data of any kind that can be analyzed in myriad ways to help us mitigate the impact of this catastrophic disease. Specifically for COVID-19, data analysis can help local governments to plan the allocation of testing kits, testing stations, and primary care units, and it can help them in setting guidelines for residents, such as the need for social distancing, the use of face masks, and when to open local businesses that enable human contact. Further, it can also lead to a better understanding of pandemics in general and so inform policy makers on the regional and national level. All of this can save both cost and lives. In this article, we show the results of an ongoing study we conducted using a prominent regularly updated dataset. We used a pattern mining engine we developed to find specific characteristics of US counties that appear to expose them to higher COVID-19 mortality. Furthermore, we also show that these characteristics can be used to predict future COVID-19 mortality.