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Related Experiment Video

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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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Published on: May 15, 2020

Gastroenteritis Forecasting Assessing the Use of Web and Electronic Health Record Data With a Linear and a Nonlinear

Canelle Poirier^1,2,3,4,5, Guillaume Bouzillé^3,4,5, Valérie Bertaud^3,4,5

¹Department of Pediatrics, Harvard Medical School, Boston, MA, United States.

JMIR Public Health and Surveillance

|January 31, 2023

Summary

This study developed methods using internet search trends and electronic health records to forecast acute gastroenteritis (AG) incidence. These tools can predict AG activity up to 10 weeks in advance, aiding public health interventions.

Keywords:

acute gastroenteritis digital data forecasting infectious disease machine learning machine learning in public health modeling modeling disease outbreaks public health

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A Data-Driven Approach to Quantifying Immune States in Sepsis

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Published on: February 7, 2025

Area of Science:

Public Health
Epidemiology
Data Science

Background:

Current French disease surveillance for gastroenteritis has a 1-3 week lag, hindering timely public health interventions.
This delay prevents real-time epidemiological characterization and adaptive response to disease dynamics.

Purpose of the Study:

To assess the feasibility of using internet search query trends and electronic health records for near real-time prediction of acute gastroenteritis (AG) incidence.
To develop models for national and regional AG incidence forecasts, including short-term and long-term predictions (up to 10 weeks).

Main Methods:

Developed two distinct approaches (linear and nonlinear) to estimate and forecast AG activity.
Integrated diverse data sources: internet search activity, electronic health records, and historical disease data.
Applied methods at national and regional scales in France.

Main Results:

All utilized data sources improved gastroenteritis surveillance, particularly for long-term forecasts.
Historical disease data demonstrated significant predictive power due to strong seasonal patterns.
The developed models showed effectiveness in both linear and nonlinear approaches.

Conclusions:

The developed forecasting methods can anticipate increased AG activity up to 10 weeks in advance.
These tools have the potential to reduce the impact of AG outbreaks by enabling proactive public health measures.
Improved surveillance through novel data integration offers a pathway to more effective disease management.