4.1 Article

RESCUING TROVES OF HIDDEN ECOLOGICAL DATA TO TACKLE EMERGING MOSQUITO-BORNE DISEASES

Journal

Publisher

AMER MOSQUITO CONTROL ASSOC
DOI: 10.2987/18-6781.1

Keywords

Arbovirus; Big Data; open data; surveillance; Zika

Categories

Funding

  1. NIH Directors Early Independence Award
  2. Office of the Director, National Institutes of Health [DP5OD023l00]
  3. Royal Society [NF140517]
  4. National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services [HHSN272201400029C]
  5. Centers for Disease Control and Prevention [U01CK000510]
  6. US National Science Foundation Postdoctoral Fellowship in Biology [1523757]
  7. Royal Society [NF140517] Funding Source: Royal Society
  8. Direct For Biological Sciences
  9. Div Of Biological Infrastructure [1523757] Funding Source: National Science Foundation

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Despite the major impact of mosquitoes on human health, knowledge gaps exist regarding their natural population dynamics. Even the most basic information-such as spatiotemporal abundance-is mostly unavailable. In the USA, municipalities have created agencies for mosquito control and monitoring, yet no national open-access repository for mosquito surveillance data exists. Vectors, and the pathogens they transmit, know no jurisdictions. We identify >1,000 mosquito control agencies and identify those which make their population abundance surveillance data publicly available. We directly survey Floridian mosquito districts to estimate, from one state alone, the potential amount of hidden data. We generate a large, standardized data set from publicly available online data and demonstrate that spatiotemporal population abundance can be reconstructed and analyzed across data generators. We propose that the ensemble of US mosquito control agencies can, and should, be used to develop a national-and potentially international-open-access repository of mosquito surveillance data, generating the data capital needed to gain a mechanistic understanding of vector population dynamics, and identify existing digital infrastructure that could be leveraged for digitizing and collating extant and future surveillance data for such a repository.

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