Understanding Mosquito Surveillance Data for Analytic Efforts:A Case Study

Brown, Heidi and Sedda, Luigi and Sumner, Chris and Stefanakos, Elene and Ruberto, Irene and Roach, Matthew (2021) Understanding Mosquito Surveillance Data for Analytic Efforts:A Case Study. Journal of Medical Entomology. ISSN 0022-2585

[img]
Text (JME-2020-0308.R2_Proof_hi)
JME_2020_0308.R2_Proof_hi.pdf - Accepted Version
Restricted to Repository staff only until 22 February 2022.
Available under License Creative Commons Attribution-NonCommercial.

Download (1MB)

Abstract

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.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Medical Entomology
Additional Information:
This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Journal of Medical Entomology following peer review. The definitive publisher-authenticated versionHeidi E Brown, Luigi Sedda, Chris Sumner, Elene Stefanakos, Irene Ruberto, Matthew Roach, Understanding Mosquito Surveillance Data for Analytic Efforts: A Case Study, Journal of Medical Entomology, 2021;, tjab018, https://doi.org/10.1093/jme/tjab018 is available online at: [url]
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2400/2405
Subjects:
ID Code:
152118
Deposited By:
Deposited On:
25 Feb 2021 12:10
Refereed?:
Yes
Published?:
Published
Last Modified:
11 Jun 2021 05:35