Predicting Lyme Disease: A One Health Approach | JoVE Visualize

Area of Science:

Epidemiology
Public Health
Veterinary Medicine

Background:

Lyme disease is a prevalent vector-borne illness in North America, necessitating accurate incidence prediction for public health preparedness.
Previous research established Google Trends search data as a viable tool for predicting monthly human Lyme disease incidence.
This study aimed to enhance predictive models by incorporating environmental and canine data alongside human data.

Purpose of the Study:

To evaluate the effectiveness of a One Health approach in predicting state-level human and canine Lyme disease incidence.
To compare the predictive performance of models integrating human, canine, and environmental data against models using single data types.
To identify optimal data streams for improving Lyme disease incidence forecasting.

Main Methods:

Acquired human Lyme disease data from state health departments and canine data from IDEXX Laboratories.
Developed predictive models incorporating environmental factors, human search trends, canine search trends, and case counts.
Compared the predictive accuracy (using Mean Absolute Error) of various models, including a comprehensive One Health model.

Main Results:

The One Health model demonstrated superior performance in predicting human Lyme disease incidence in 6 out of 16 states (MAE = 12.1) compared to other models.
For canine Lyme disease incidence, the One Health model (MAE = 330.5) performed worse than models utilizing environmental data (MAE = 221.5) or human data (MAE = 248.4).
Despite improvements, the best-performing models exhibited significant prediction errors, limiting their practical application.

Conclusions:

Integrating human, animal, and environmental data within a One Health framework shows promise for improving human Lyme disease prediction.
The predictive utility of different data streams varies between human and canine populations.
Future research should explore alternative data sources like electronic health records and insurance claims to enhance predictive accuracy for Lyme disease.