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Related Experiment Video

Updated: Jul 12, 2025

Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples
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Analytical reference framework to analyze non-COVID-19 events.

María Del Pilar Villamil1, Nubia Velasco2, David Barrera3

  • 1Department of Systems and Computing Engineering, Universidad de Los Andes, Bogotá, Colombia. mavillam@uniandes.edu.co.

Population Health Metrics
|October 21, 2023
PubMed
Summary
This summary is machine-generated.

The ANE Framework forecasts non-COVID-19 diseases using time series analysis. This reliable tool helps predict patient numbers for conditions like tuberculosis, addressing healthcare system disruptions.

Keywords:
Forecasting modelsNo COVID-19 eventsSARIMASuicide attemptTuberculosis

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Area of Science:

  • Public Health
  • Epidemiology
  • Health Informatics

Background:

  • The COVID-19 pandemic significantly disrupted healthcare, delaying diagnoses for non-COVID-19 conditions.
  • Existing analytical models pre-2020 were disease-specific, and post-2020 models primarily focused on COVID-19.
  • A critical gap exists in disease forecasting frameworks for non-COVID-19 illnesses.

Purpose of the Study:

  • To introduce the Analytics for Non-COVID-19 Events (ANE) Framework.
  • To provide a reliable and user-friendly tool for forecasting non-COVID-19 patient numbers.
  • To address the need for adaptable disease forecasting models in public health.

Main Methods:

  • The ANE Framework employs time series analysis and SARIMA models.
  • It utilizes daily data from official government sources for forecasting.
  • The framework is designed for flexibility, incorporating new data and sources.

Main Results:

  • The ANE Framework demonstrated reliability across five non-COVID-19 events, including tuberculosis and suicide attempts.
  • Performance showed a Mean Absolute Percentage Error (MAPE) up to 20%, consistent across different event dynamics.
  • Significant gaps between expected and reported cases were identified (e.g., 17% for tuberculosis, 19% for suicide attempts), varying by region.

Conclusions:

  • The ANE Framework is a flexible and reliable tool for analyzing diverse disease data.
  • The model's adaptability allows for updates with new data, enhancing forecasting accuracy.
  • The framework is crucial for monitoring and managing non-COVID-19 disease trends amidst healthcare disruptions.