Biomedical
Challenges in Predicting Lyme Disease Risk
Kiersten J. Kugeler,
Rebecca J. Eisen
Peer Reviewed
Abstract
The article discusses the challenges in predicting Lyme disease risk due to incomplete and inconsistent data. Although Lyme disease incidence is increasing, accurate prediction models are hindered by gaps in tick distribution, infection prevalence, and human disease surveillance. The authors emphasize the need for more accurate and standardized data, particularly regarding the presence of infected ticks and underreporting in disease cases. They highlight the CDC's efforts to improve surveillance through a national tick and tick-borne pathogen reporting program to better predict and prevent Lyme disease spread.
Key Questions and Answers
1. What are the challenges in predicting Lyme disease risk?
Predicting Lyme disease risk is challenging due to gaps in tick distribution data, underreporting of cases, and inconsistent surveillance methods. These factors hinder accurate models.
2. How can Lyme disease prediction models be improved?
Improvement requires better data on tick distribution, infection prevalence, and a more robust national surveillance program to support accurate risk predictions.
3. What is the role of the CDC's tick surveillance program?
The CDC’s tick surveillance program aims to provide accurate, up-to-date information on tick distribution and pathogens, improving the prediction and prevention of Lyme disease.
