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Using Timeliness in Tracking Infections.

Melih Bastopcu¹, Sennur Ulukus²

¹Coordinated Science Laboratory, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA.

Entropy (Basel, Switzerland)

|June 24, 2022

Summary

This study optimizes real-time infection tracking by developing an algorithm that allocates unequal testing rates based on individual infection and recovery data. This approach improves detection speed and accuracy, especially in large, diverse populations.

Keywords:

Markovian infection spread model age of information timely infection tracking timely tracking of multiple processes

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Area of Science:

Epidemiology
Public Health
Data Science

Background:

Real-time tracking of infectious diseases like COVID-19 is crucial for public health.
Accurate and timely detection of infected and recovered individuals is a key challenge for healthcare providers.

Purpose of the Study:

To develop a method for real-time tracking of infection status.
To minimize the average difference between actual and estimated infection status.
To optimize testing strategies for improved timeliness and accuracy.

Main Methods:

Analytical expression for average difference based on infection, recovery, and test rates.
Alternating minimization algorithm to determine optimal test rates.
Analysis of noisy test measurements and dependent infection status.

Main Results:

Unequal testing rates, prioritizing individuals with higher infection/recovery rates, improve tracking efficiency under limited test capacity.
Increased population size and diversity enhance the efficiency of testing capacity allocation.
Adjustable test rate allocation can prioritize detection of infected or recovered individuals.
Careful selection of error metrics is vital for aligning testing strategies with healthcare provider priorities.

Conclusions:

Optimized, non-uniform testing strategies are more effective than uniform testing for real-time infection status tracking.
System performance improves with increased population size due to greater diversity.
The choice of error metric significantly impacts testing allocation and overall effectiveness.