Optimizing COVID-19 testing resources use with wearable sensors.

Area of Science:

• Epidemiology
• Public Health
• Wearable Technology

Background:

• Timely identification of pre-symptomatic and asymptomatic infectious cases is crucial for controlling viral spread.
• Current routine testing programs are effective but costly and burdensome.
• Wearable technology offers a passive monitoring solution to identify high-risk individuals for testing.

Purpose of the Study:

• To model and compare the effectiveness of routine testing versus wearable-informed testing strategies.
• To estimate the number of tests required and the duration of infectious, undetected periods.
• To assess the potential of wearable technology in reducing viral transmission.

Main Methods:

• Development of a parametric Markov chain model.
• Simulation of routine testing and wearable-informed testing strategies.
• Estimation of infectious and undetected days under different testing scenarios.

Main Results:

• Routine testing resulted in 4.1 infectious and undetected days (with 2 tests/month).
• Wearable testing reduced infectious days by 46% (with symptoms) and 27% (without symptoms).
• Wearable technology can decrease required tests and costs while maintaining similar infectious day reduction.

Conclusions:

• Wearable technology integrated into testing strategies significantly reduces infectious and transmissible periods.
• This approach offers a cost-effective and less burdensome alternative to frequent routine testing.
• The model is adaptable for various viral illnesses by adjusting parameters.