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Related Experiment Video

Updated: Sep 24, 2025

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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Surveillance for endemic infectious disease outbreaks: Adaptive sampling using profile likelihood estimation.

Michael Fairley1, Isabelle J Rao1, Margaret L Brandeau1

  • 1Department of Management Science and Engineering, Stanford University, Stanford, California, USA.

Statistics in Medicine
|May 9, 2022
PubMed
Summary

This study introduces a new adaptive sampling algorithm to quickly find disease outbreaks. The profile likelihood sampling (PLS) method improves outbreak detection compared to random sampling, even with uncertain disease prevalence.

Keywords:
HIVadaptive samplingendemic diseaseprofile likelihoodquickest change detectionsurveillance

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Area of Science:

  • Epidemiology
  • Infectious Disease Dynamics
  • Public Health Surveillance

Background:

  • Endemic infectious diseases can cause significant outbreaks when introduced to susceptible populations or connected networks.
  • Recent HIV outbreaks among people who inject drugs highlight the need for effective outbreak detection strategies.

Purpose of the Study:

  • To develop and evaluate an adaptive sampling algorithm for allocating limited testing resources to rapidly detect endemic infectious disease outbreaks.
  • To compare the performance of the proposed profile likelihood sampling (PLS) method against uniform random sampling (URS) and Thompson sampling (TS).

Main Methods:

  • Developed an adaptive sampling algorithm using profile likelihood to estimate the probability of triggering an outbreak alarm in each location.
  • Sampling prioritized locations with the highest estimated probability of an outbreak alarm in the next time period.
  • Compared PLS against URS and TS using numerical simulations.

Main Results:

  • The PLS method demonstrated improved time to outbreak detection compared to TS in certain scenarios and was consistently better than URS.
  • URS and TS showed variable performance, with TS underperforming URS when outbreaks occurred in locations with lower initial prevalence.
  • PLS proved robust and effective, even when initial disease prevalence was uncertain across locations.

Conclusions:

  • Profile Likelihood Sampling (PLS) offers an effective and reliable approach for rapid endemic disease outbreak detection.
  • The PLS method is robust to uncertainties in initial disease prevalence, making it a valuable tool for public health surveillance.
  • Optimizing resource allocation through adaptive sampling is crucial for timely identification and response to infectious disease outbreaks.