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Citizen Science Provides an Efficient Method for Broad-Scale Tick-Borne Pathogen Surveillance of Ixodes pacificus and Ixodes scapularis across the United States.

W Tanner PorterJulie WacharaZachary A BarrandNathan C NietoDaniel J Salkeld
Published in: mSphere (2021)
Tick-borne diseases have expanded over the last 2 decades as a result of shifts in tick and pathogen distributions. These shifts have significantly increased the need for accurate portrayal of real-time pathogen distributions and prevalence in hopes of stemming increases in human morbidity. Traditionally, pathogen distribution and prevalence have been monitored through case reports or scientific collections of ticks or reservoir hosts, both of which have challenges that impact the extent, availability, and accuracy of these data. Citizen science tick collections and testing campaigns supplement these data and provide timely estimates of pathogen prevalence and distributions to help characterize and understand tick-borne disease threats to communities. We utilized our national citizen science tick collection and testing program to describe the distribution and prevalence of four Ixodes-borne pathogens, Borrelia burgdorferi sensu lato, Borrelia miyamotoi, Anaplasma phagocytophilum, and Babesia microti, across the continental United States. IMPORTANCE In the 21st century, zoonotic pathogens continue to emerge, while previously discovered pathogens continue to have changes within their distribution and prevalence. Monitoring these pathogens is resource intensive, requiring both field and laboratory support; thus, data sets are often limited within their spatial and temporal extents. Citizen science collections provide a method to harness the general public to collect samples, enabling real-time monitoring of pathogen distribution and prevalence.
Keyphrases
  • risk factors
  • public health
  • candida albicans
  • electronic health record
  • healthcare
  • gram negative
  • big data
  • mental health
  • antimicrobial resistance
  • high resolution
  • multidrug resistant
  • mass spectrometry
  • deep learning