Using control charts to understand community variation in COVID-19

Area of Science:

• Public Health
• Epidemiology
• Statistical Analysis

Background:

• The COVID-19 pandemic necessitates timely data for effective decision-making.
• Existing data presentation methods may lack the granularity needed for local-level interventions.
• Statistical process control (SPC) is widely used in industries to monitor and manage process variations.

Purpose of the Study:

• To demonstrate a novel application of SPC for real-time COVID-19 data visualization.
• To provide interpretable displays that inform local mitigation and containment strategies.
• To highlight the value of SPC for policy-makers and communities during the pandemic.

Main Methods:

• Developed control charts at county and city/neighborhood levels in California.
• Utilized SPC principles to analyze and disaggregate COVID-19 data.
• Annotated time series presentations to link events and policies with data trends.

Main Results:

• COVID-19 rates exhibit significant regional and sub-regional variations.
• Identified periods of both exponential and non-exponential growth and decline in disease rates.
• Demonstrated that disaggregated data provides actionable granularity for decision-makers.

Conclusions:

• Control charts offer a valuable tool for real-time decision-making in public health.
• SPC facilitates interpretable communication of pandemic data to communities.
• This approach can effectively mobilize and direct stakeholder actions for pandemic response.