To isolate, or not to isolate: a theoretical framework for disease control via contact tracing

To isolate, or not to isolate: a theoretical framework for disease control via contact tracing

Publication date: May 26, 2020

Contact tracing is an essential tool in the public health battle for epidemiological control of infectious diseases. Contact tracing via case-by-case interviews is effective when contacts are known and outbreaks are small. Smartphone applications that keep track of contacts between users offer the possibility to scale contact tracing to larger outbreaks with minimal notification delays. While the benefits of reduced delays are widely recognised, it is less well understood how to best implement the tracing and notification protocol. The application will detect a multitude of contacts encountering an individual who later tests positive. Which of these contacts should be advised to self-isolate? The resolution hinges on an inherent trade-off: the more contacts notified, the greater the disease control, at the cost of more healthy individuals being instructed to self-isolate. In this study, based on a compartmental model tailored to the COVID-19 pandemic, we develop a framework to incorporate testing with limited resources coupled with a mechanistic description of digital contact tracing. Specifically, we employ a family of distributions characterising contact exposure and infection risk, and introduce a notification threshold that controls which level of exposure triggers notification. We detail how contact tracing can prevent disease outbreak, as a function of adoption rate, testing limitations, and other intervention methods such as social distancing and lockdown measures. We find an optimal notification threshold that balances the trade-off by minimising the number of healthy individuals instructed to self-isolate while preventing disease outbreak.

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Concepts Keywords
Acceleration Infectious diseases
Accounting Epidemiology
Agnostic RTT
Asymptomatic Medicine
Asymptotic Medical specialties
Bat Health
Bur Strain healthcare infrastructure
Cation Notification protocol
Compartmental Models Prevention
Controllability Healthcare-associated infections
Curse Contact tracing
Digital Ebola virus disease
Epidemic Social distancing
Epidemiological Notification protocol
Equilibrium Simulation
Exogenous Smartphone
Flattening
Force
Forthe
France
Healthcare
Heterogeneity
HIV
Iden
Iff
IKF
Infection
Infectious Diseases
Ing
Integral
Interplay
Load Testing
Lockdown
Magnitude
Main Plot
Nec
Onan
Palaiseau
Pandemic
PaPI
Paris
Peak
Pie
Population Densities
Prob
Probability
Probability Density
Protocol
Quarantine
Saclay
Sexually Transmitted Diseases
Simulation
Smartphone
Spectrum
Stochastic
Stochastic Differential Equations
True Positive
Tween
Virus

Semantics

Type Source Name
disease MESH infectious diseases
disease MESH infection
drug DRUGBANK L-Isoleucine
drug DRUGBANK Isoxaflutole
disease MESH sexually transmitted diseases
drug DRUGBANK Cysteamine
disease MESH secondary infections
disease MESH diagnoses
disease MESH separated
drug DRUGBANK Coenzyme M
drug DRUGBANK Naproxen
drug DRUGBANK Trestolone
disease MESH privacy
drug DRUGBANK Guanosine

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