Smart testing and selective quarantine for the control of epidemics

Area of Science:

• Epidemiology
• Control Engineering
• Public Health

Background:

• The COVID-19 pandemic highlighted the critical role of testing strategies in managing selective confinement measures.
• Optimal sensor selection is a key challenge in control engineering with direct relevance to epidemic management.

Purpose of the Study:

• To propose an intelligent policy for selecting individuals for COVID-19 testing.
• To minimize the probability of undetected asymptomatic cases during an epidemic.

Main Methods:

• Modeling the epidemic as a stochastic dynamic system.
• Dynamically updating individual infection probabilities daily based on new data.
• Employing optimality criteria to guide test selection.

Main Results:

• Simulations on a 10,000-individual community demonstrated the policy's effectiveness.
• The proposed technique reduced disease spread compared to contact tracing and off-line strategies.
• Fewer individuals were confined when using the proposed smart testing and confinement policy.

Conclusions:

• A dynamic, model-based testing strategy can significantly improve epidemic control.
• Intelligent test selection optimizes resource allocation and minimizes societal disruption.
• This approach offers a more effective alternative to traditional contact tracing for managing infectious disease outbreaks.