Quantifying the preventive effect of wearing face masks.

Area of Science:

• Epidemiology
• Public Health
• Infectious Disease Control

Background:

• Estimating the effectiveness of COVID-19 preventive measures is crucial.
• Existing studies use varied metrics (e.g., relative attack rate, incidence change) that are community-specific.
• A universal measure is needed for consistent comparison across different settings.

Purpose of the Study:

• To introduce a universal metric for quantifying the preventive effect of face masks.
• To establish the 'face mask effect' (E_FM) as the relative reduction in the reproduction number.
• To demonstrate converting existing effect measures to E_FM.

Main Methods:

• Developing a methodology to convert various COVID-19 preventive effect measures into a standardized metric.
• Applying the conversion methodology to four empirical studies.
• Calculating the face mask effect (E_FM) from diverse datasets.

Main Results:

• The study proposes the 'face mask effect' (E_FM) as a universal measure.
• Converted estimates of E_FM from four studies consistently ranged between 15% and 40%.
• This indicates a significant reduction in the reproduction number due to face mask usage.

Conclusions:

• Face masks are an effective preventive measure against COVID-19 transmission.
• The face mask effect (E_FM) provides a standardized and transferable measure of effectiveness.
• Mandatory face mask policies can reduce the reproduction number by an estimated 15-40%.