Epidemic management and control through risk-dependent individual contact interventions

Abstract

Testing, contact tracing, and isolation (TTI) is an epidemic management and control approach that is difficult to implement at scale because it relies on manual tracing of contacts. Exposure notification apps have been developed to digitally scale up TTI by harnessing contact data obtained from mobile devices; however, exposure notification apps provide users only with limited binary information when they have been directly exposed to a known infection source. Here we demonstrate a scalable improvement to TTI and exposure notification apps that uses data assimilation (DA) on a contact network. Network DA exploits diverse sources of health data together with the proximity data from mobile devices that exposure notification apps rely upon. It provides users with continuously assessed individual risks of exposure and infection, which can form the basis for targeting individual contact interventions. Simulations of the early COVID-19 epidemic in New York City are used to establish proof-of-concept. In the simulations, network DA identifies up to a factor 2 more infections than contact tracing when both harness the same contact data and diagnostic test data. This remains true even when only a relatively small fraction of the population uses network DA. When a sufficiently large fraction of the population (≳ 75%) uses network DA and complies with individual contact interventions, targeting contact interventions with network DA reduces deaths by up to a factor 4 relative to TTI. Network DA can be implemented by expanding the computational backend of existing exposure notification apps, thus greatly enhancing their capabilities. Implemented at scale, it has the potential to precisely and effectively control future epidemics while minimizing economic disruption.

Training Physics‐Based Machine‐Learning Parameterizations With Gradient‐Free Ensemble Kalman Methods

“…The normalization induced by Equation C1 also enables the use of isotropic regularization in Equations 25 and 26, even though the physical parameters ϕ may differ in order of magnitude. For more examples of parameter transformations in the context of EKI and UKI, see Huang, Schneider, and Stuart (2022), Schneider et al (2022), andDunbar et al (2022).…”

EnsembleKalmanProcesses.jl: Derivative-free ensemble-based model calibration

“…It does so by defining transformation maps under-the-hood from the constrained space to an unconstrained space where the optimization problem can be suitably defined. Constrained optimization using this framework has been successfully demonstrated in a variety of settings (Dunbar et al, 2022;Lopez-Gomez et al, 2022;Schneider, Dunbar, et al, 2022).…”

Modeling and forecasting age-specific drug overdose mortality in the United States

“…The EnKF, which we use to combine observation data with an age-structured model of overdose mortality, originated from research activities in the geophysical sciences and has found various applications in problems that require combining high-dimensional dynamical systems with observation data [ 25 ]. Kalman filtering and related data assimilation methods (e.g., Bayesian Markov chain Monte Carlo) have been used in computational biology and medicine to estimate model parameters [ 26 – 29 ], identify patients with antibiotic-resistant bacteria in hospital wards [ 30 ], and develop risk-dependent contact interventions in epidemic management [ 31 ]. Within computational social science, data assimilation methods have proven useful in combining mechanistic models with survey data, e.g., to study the evolution of political polarization in the United States [ 32 ].…”