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Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Optimizing campus-wide COVID-19 test notifications with interpretable wastewater time-series features using machine

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Wastewater surveillance for SARS-CoV-2 effectively predicts COVID-19 infections in residences. A positive wastewater sample on at least 3 of the past 7 days is the best indicator for individual infections.

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Area of Science:

  • Environmental Science
  • Epidemiology
  • Public Health

Background:

  • Wastewater surveillance for SARS-CoV-2 is effective for population health monitoring.
  • High-resolution wastewater surveillance at the University of California, San Diego (UCSD) aims to detect undiagnosed COVID-19 cases and trigger resident notifications.
  • Optimal indicators for triggering these notifications remain undetermined.

Purpose of the Study:

  • To develop a data processing pipeline to identify key features in wastewater surveillance data that predict COVID-19 presence in associated residences.
  • To establish optimal determinants for triggering public health interventions based on wastewater data.

Main Methods:

  • Utilized time series data of wastewater SARS-CoV-2 results and individual testing results from UCSD students (11/2020-11/2021).
  • Developed hierarchical classification/decision tree models to identify informative wastewater features predicting individual infections.
  • Compared decision tree model performance against other statistical and machine learning models.

Main Results:

  • The most effective predictor of positive individual COVID-19 tests in residence buildings was wastewater samples testing positive on at least 3 of the preceding 7 days.
  • Hierarchical decision tree models demonstrated superior predictive performance compared to other models while maintaining interpretability.

Conclusions:

  • The study successfully identified key wastewater surveillance features for predicting individual COVID-19 infections within residential settings.
  • Findings have informed the refinement of campus-wide public health guidelines and notification systems at UCSD to enhance early detection and response to potential outbreaks.