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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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A multi-city COVID-19 forecasting model utilizing wastewater-based epidemiology.

Naomi Rankin1, Samee Saiyed1, Hongru Du1

  • 1Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, USA.

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This study introduces a novel wastewater-based forecasting model to predict COVID-19 hospitalizations and healthcare capacity risks. The model utilizes wastewater epidemiology data for more reliable public health decision-making.

Keywords:
COVID-19Categorical forecastingCity-levelWastewater-based epidemiology

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Area of Science:

  • Environmental Science
  • Epidemiology
  • Public Health

Background:

  • COVID-19 forecasting models have shown limitations, particularly in performance during critical outbreak periods.
  • Reliable data inputs and alternative forecasting targets are crucial for effective public health decision-making.
  • Wastewater-based epidemiology (WBE) offers a promising, more consistent metric for tracking disease transmission compared to reported cases.

Purpose of the Study:

  • To develop and validate a multi-city, wastewater-based forecasting model for predicting COVID-19 hospitalizations.
  • To introduce novel categorization methods for assessing healthcare system burden and hospitalization trends.
  • To evaluate the impact of WBE data on forecasting accuracy, especially during outbreak change points.

Main Methods:

  • A Generalized Additive Model (GAM) was developed using hospitalization and COVID-19 WBE data from six US cities.
  • Two categorization types were generated: Hospitalization Capacity Risk (HCR) and Hospitalization Rate Trend (HRT).
  • Probabilistic forecasts were created for 1, 2, and 3-week windows, with a new methodology for assessing performance at outbreak change points.

Main Results:

  • The inclusion of wastewater data significantly improved the forecasting model's performance.
  • The model successfully generated probabilistic forecasts for hospitalization risk and trend categories across six cities over 20 months.
  • A novel method for evaluating model performance at critical outbreak change points was proposed and applied.

Conclusions:

  • Wastewater-based epidemiology provides a valuable and reliable data source for COVID-19 forecasting.
  • The developed categorical forecasting model offers a new tool for decision-makers to predict hospital capacity risk and disease trends.
  • This approach enhances public health preparedness and response strategies for infectious diseases.