Strategies for controlling non-transmissible infection outbreaks using a large human movement data set.

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• Quantifying human movement is crucial for predicting infectious disease transmission.
• Legionnaires' disease, a pneumonia caused by Legionella pneumophila, originates from localized environmental sources.

Purpose of the Study:

• To develop and apply a mathematical model for non-transmissible infections using human movement data.
• To assess the impact of movement heterogeneity on predicting disease source location.
• To evaluate the model's accuracy in identifying exposed populations and predicting residential locations of infected individuals for control strategies.

Main Methods:

• Developed a mathematical model for non-transmissible infections.
• Incorporated detailed human movement patterns of Great Britain's population.
• Utilized case-report data from three Legionnaires' disease outbreaks with identified sources.
• Compared predictions using reported travel histories versus simulated movement patterns.

Main Results:

• Individual-level heterogeneity in movement data significantly influences source prediction accuracy.
• Reported travel histories provided the most accurate source predictions.
• Detailed simulation models offer a fast, effective alternative for estimating movement patterns.
• The model accurately determined exposed populations and predicted residential locations of infected individuals post-source identification.

Conclusions:

• Human movement data is vital for predicting and controlling infectious disease outbreaks.
• Mathematical modeling, informed by detailed movement data, can accurately pinpoint disease sources and identify at-risk populations.
• The developed model provides a framework for effective, rapid control strategies during outbreaks.