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22 February 2021 Understanding Mosquito Surveillance Data for Analytic Efforts: A Case Study
Heidi E. Brown, Luigi Sedda, Chris Sumner, Elene Stefanakos, Irene Ruberto, Matthew Roach
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Mosquito surveillance data can be used for predicting mosquito distribution and dynamics as they relate to human disease. Often these data are collected by independent agencies and aggregated to state and national level portals to characterize broad spatial and temporal dynamics. These larger repositories may also share the data for use in mosquito and/or disease prediction and forecasting models. Assumed, but not always confirmed, is consistency of data across agencies. Subtle differences in reporting may be important for development and the eventual interpretation of predictive models. Using mosquito vector surveillance data from Arizona as a case study, we found differences among agencies in how trapping practices were reported. Inconsistencies in reporting may interfere with quantitative comparisons if the user has only cursory familiarity with mosquito surveillance data. Some inconsistencies can be overcome if they are explicit in the metadata while others may yield biased estimates if they are not changed in how data are recorded. Sharing of metadata and collaboration between modelers and vector control agencies is necessary for improving the quality of the estimations. Efforts to improve sharing, displaying, and comparing vector data from multiple agencies are underway, but existing data must be used with caution.

© The Author(s) 2021. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail:
Heidi E. Brown, Luigi Sedda, Chris Sumner, Elene Stefanakos, Irene Ruberto, and Matthew Roach "Understanding Mosquito Surveillance Data for Analytic Efforts: A Case Study," Journal of Medical Entomology 58(4), 1619-1625, (22 February 2021).
Received: 11 July 2020; Accepted: 14 January 2021; Published: 22 February 2021

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data sharing
disease prediction
mosquito-borne disease
vector surveillance
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