The ability to distinguish between erratic and systematic patterns of change in case count data is crucial for assessing and projecting the course of disease outbreaks. Here, it is shown that measuring the strength of trends can provide information that is not readily captured by commonly used descriptive indicators. In combination with the 7-day moving average, Bandt and Pompe's permutation entropy and Wilder's relative strength index were found to support the timely detection of coronavirus epidemic trends and transitions in data from various countries. The results demonstrate that measuring the strength of epidemic growth trends in addition to their magnitude can significantly enhance disease surveillance.
