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Detecting the Lyme Disease Spirochete, Borrelia Burgdorferi, in Ticks Using Nested PCR
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Lymelight: forecasting Lyme disease risk using web search data.

Adam Sadilek1, Yulin Hswen2,3, Shailesh Bavadekar1

  • 11Google, Mountain View, CA USA.

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|February 13, 2020
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Summary

Lymelight, a novel method using web searches, monitors Lyme disease spread in real-time. This approach correlates highly with official data and aids in timely public health interventions.

Keywords:
Computational scienceEpidemiologyInfectious diseases

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Area of Science:

  • Epidemiology
  • Public Health
  • Computational Biology

Background:

  • Lyme disease is a prevalent tick-borne illness in the Northern Hemisphere.
  • Current methods for estimating Lyme disease incidence have significant delays.
  • Real-time disease monitoring is crucial for effective public health response.

Purpose of the Study:

  • To introduce Lymelight, a new method for real-time Lyme disease incidence monitoring.
  • To evaluate the correlation between Lymelight estimates and official Lyme disease case counts.
  • To explore the potential of web search data for analyzing treatment-seeking behaviors and reducing misdiagnosis.

Main Methods:

  • Developed a machine-learned classifier to analyze web search sessions for Lyme disease symptoms.
  • Estimated Lyme disease incidence by identifying individuals searching for symptoms in specific geographic areas.
  • Validated the Lymelight method against official Centers for Disease Control and Prevention (CDC) case data for 2014-2015.

Main Results:

  • Lymelight demonstrated a strong 92% correlation (p < 0.001) with official Lyme disease case counts at the county level.
  • Web search data provided real-time insights into disease incidence, overcoming traditional reporting delays.
  • Analysis of treatment-related searches offered potential to assess and improve diagnostic accuracy.

Conclusions:

  • Lymelight offers a scalable and timely approach for monitoring vector-borne diseases like Lyme disease.
  • Real-time detection through Lymelight can facilitate more prompt public health interventions.
  • Utilizing web search data analysis can enhance disease surveillance and potentially reduce misdiagnosis rates.