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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

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Enhanced SARS-CoV-2 case prediction using public health data and machine learning models.

Bradley S Price^1,2, Maryam Khodaverdi², Brian Hendricks^2,3

¹Department of Management Information Systems, West Virginia University, Morgantown, WV 26505, United States.

|March 6, 2024

Summary

This study introduces a machine learning framework to predict COVID-19 cases using real-time public health data. The model accurately forecasts localized case counts, considering variants, testing, and vaccination rates.

Keywords:

SARS-CoV-2 prediction machine learning public health data

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Area of Science:

Epidemiology
Machine Learning
Public Health

Background:

Accurate forecasting of infectious disease outbreaks is crucial for public health.
Dynamic public health data significantly influences disease transmission patterns.

Purpose of the Study:

To develop and evaluate a scalable machine learning framework for predicting near-term COVID-19 cases.
To assess the impact of real-time public health data on prediction accuracy.

Main Methods:

Utilized a long-short-term memory (LSTM) network.
Incorporated patient-level SARS-CoV-2 test data from West Virginia (Jan 2021-Mar 2022).
Integrated dynamic public health metrics including variant data, vaccination rates, and testing information.

Main Results:

The framework improved prediction accuracy for localized case counts.
Demonstrated the impact of dynamic factors like viral variants and vaccination rates on predictions.
Achieved higher accuracy during Omicron and Delta periods, especially for county-level forecasting.

Conclusions:

The proposed framework effectively combines dynamic public health metrics with ML models for healthcare forecasting.
Highlights the importance of ML deployment in rural settings for public health insights.