Optimizing COVID-19 testing resources use with wearable sensors

TL;DR

Wearable sensors can help reduce the spread of diseases like COVID-19 by identifying high-risk individuals earlier and reducing the number of tests needed.

Contribution

A Markov chain model is proposed to quantify how wearable technology improves testing strategies for viral illnesses.

Findings

Wearable testing reduces infectious and undetected days by 46% when combined with self-reported symptoms.

The model can be adapted for different viral illnesses by adjusting parameters.

Using wearables can lower the number of tests required and the overall cost of testing strategies.

Abstract

The timely identification of infectious pre-symptomatic and asymptomatic cases is key towards preventing the spread of a viral illness like COVID-19. Early identification has been done through routine testing programs, which are indeed costly and potentially burdensome for individuals who should be tested with high frequency. A supplemental tool is represented by wearable technology, that can passively monitor and identify individuals at high risk, alerting them to take a test. We designed a Markov chain model and simulated a routine testing and a wearable testing strategy to estimate the number of tests required and the average number of days in which an individual is infectious and undetected. According to our model, with 2 test per month available, we have that the number of infectious and undetected days is 4.1 in the case of routine testing, while it decreases by 46% and 27% with a wearable testing strategy in the presence or absence of self-reported symptoms. The proposed parametric model can be used for different viral illnesses by tuning its parameters. It shows that wearable technology informing a testing strategy can significantly reduce the number of infectious days in which an individuals can spread the virus. With the same number of infectious days, by using wearables we can potentially reduce the number of required tests and the cost of the testing strategy.

The early identification and quarantine of infected individuals are key to limit the diffusion of viral infections like COVID-19, which is responsible for more than 1 million deaths in the United States alone. While routine testing programs have shown to be effective, they are potentially burdensome for individuals who should be tested with high frequency. Wearable technology has been shown to be a supplemental tool for passive early identification of individuals at risk of infection, enabling an earlier alert to test and shortening the time in which they are infectious and undetected. By inviting individuals to take a test only when they have a higher probability of being infected, this strategy can also reduce the number of required tests.

We propose a model to quantify the effects of this strategy in reducing the spread of the virus. The results from our model suggest that the use of wearable technology can significantly improve the way in which we address the challenges imposed to public health by an ongoing viral illness, reducing the required cost and cognitive load to citizens, while limiting the spread of the virus.

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