Understanding variation in reported covid-19 deaths with a novel Shewhart chart application

Abstract

Motivated by the coronavirus disease 2019 (covid-19) pandemic, we developed a novel Shewhart chart to visualize and learn from variation in reported deaths in an epidemic. Without a method to understand if a day-to-day variation in outcomes may be attributed to meaningful signals of change—rather than variability we would expect—care providers, improvement leaders, policy-makers, and the public will struggle to recognize if epidemic conditions are improving. We developed a novel hybrid C-chart and I-chart to detect within a geographic area the start and end of exponential growth in reported deaths. Reported deaths were the unit of analysis owing to erratic reporting of cases from variability in local testing strategies. We used simulation and case studies to assess chart performance and define technical parameters. This approach also applies to other critical measures related to a pandemic when high-quality data are available. The hybrid chart detected the start of exponential growth and identified early signals that the growth phase was ending. During a pandemic, timely reliable signals that an epidemic is waxing or waning may have mortal implications. This novel chart offers a practical tool, accessible to system leaders and frontline teams, to visualize and learn from daily reported deaths during an epidemic. Without Shewhart charts and, more broadly, a theory of variation in our epidemiological arsenal, we lack a scientific method for a real-time assessment of local conditions. Shewhart charts should become a standard method for learning from data in the context of a pandemic or epidemic.