Terminating pandemics with smartwatches.

Abstract

Recent studies have demonstrated that wearable devices, such as smartwatches, can accurately detect infections in presymptomatic and asymptomatic individuals. Yet, the extent to which smartwatches can contribute to prevention and control of infectious diseases through a subsequent reduction in social contacts is not fully understood. We developed a multiscale modeling framework that integrates within-host viral dynamics and between-host interactions to estimate the risk of viral disease outbreaks within a given population. We used the model to evaluate the population-level effectiveness of smartwatch detection in reducing the transmission of three COVID-19 variants and seasonal and pandemic influenza. With a 66% reduction in contacts after smartwatch-based disease detection, we estimate that the reproduction number R would drop from 2.55 (interquartile range [IQR]: 2.09-2.97) to 1.37 (IQR: 1.00-1.55) for the ancestral COVID-19 variant; from 1.54 (IQR: 1.41-1.69) to 0.82 (IQR: 0.68-0.85) for the delta variant; from 4.15 (IQR: 3.38-4.91) to 2.20 (IQR: 1.57-2.52) for the omicron variant; from 1.55 (IQR: 1.34-1.74) to 0.81 (IQR: 0.63-0.87) for pandemic influenza; and from 1.28 (IQR: 1.18-1.35) to 0.74 (IQR: 0.64-0.79) for seasonal influenza. With a 75% reduction in contacts, R decreases below 1 for the delta variant and for pandemic and seasonal influenza. Sensitivity analyses across a wide array of parameter values confirm that self-isolation initiated shortly after smartwatch detection could significantly reduce R under diverse epidemiological conditions, different levels of smartwatch detection accuracy, and realistic self-isolation levels. Our study underscores the revolutionary potential of smartwatches to manage seasonal diseases and alter the course of future pandemics.