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    Rights statement: This is a pre-copy-editing, author-produced PDF of an article accepted for publication in British Journal for the Philosophy of Science following peer review. The definitive publisher-authenticated version Heidi 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, Volume 58, Issue 4, July 2021, Pages 1619–1625, https://doi.org/10.1093/jme/tjab018 is available online at: https://academic.oup.com/jme/article-abstract/58/4/1619/6146055

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Understanding Mosquito Surveillance Data for Analytic Efforts: A Case Study

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published

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Understanding Mosquito Surveillance Data for Analytic Efforts : A Case Study. / Brown, Heidi; Sedda, Luigi; Sumner, Chris; Stefanakos, Elene; Ruberto, Irene; Roach, Matthew.

In: Journal of Medical Entomology, Vol. 58, No. 4, 31.07.2021, p. 1619-1625.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Brown, H, Sedda, L, Sumner, C, Stefanakos, E, Ruberto, I & Roach, M 2021, 'Understanding Mosquito Surveillance Data for Analytic Efforts: A Case Study', Journal of Medical Entomology, vol. 58, no. 4, pp. 1619-1625. https://doi.org/10.1093/jme/tjab018

APA

Brown, H., Sedda, L., Sumner, C., Stefanakos, E., Ruberto, I., & Roach, M. (2021). Understanding Mosquito Surveillance Data for Analytic Efforts: A Case Study. Journal of Medical Entomology, 58(4), 1619-1625. https://doi.org/10.1093/jme/tjab018

Vancouver

Brown H, Sedda L, Sumner C, Stefanakos E, Ruberto I, Roach M. Understanding Mosquito Surveillance Data for Analytic Efforts: A Case Study. Journal of Medical Entomology. 2021 Jul 31;58(4):1619-1625. https://doi.org/10.1093/jme/tjab018

Author

Brown, Heidi ; Sedda, Luigi ; Sumner, Chris ; Stefanakos, Elene ; Ruberto, Irene ; Roach, Matthew. / Understanding Mosquito Surveillance Data for Analytic Efforts : A Case Study. In: Journal of Medical Entomology. 2021 ; Vol. 58, No. 4. pp. 1619-1625.

Bibtex

@article{1ec9dd8b4f7d45a9911147f95a2ed3e0,
title = "Understanding Mosquito Surveillance Data for Analytic Efforts: A Case Study",
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.",
author = "Heidi Brown and Luigi Sedda and Chris Sumner and Elene Stefanakos and Irene Ruberto and Matthew Roach",
note = "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: 58, 4, 1619-1625 is available online at: https://academic.oup.com/jme/article/58/4/1619/6146055",
year = "2021",
month = jul,
day = "31",
doi = "10.1093/jme/tjab018",
language = "English",
volume = "58",
pages = "1619--1625",
journal = "Journal of Medical Entomology",
issn = "0022-2585",
publisher = "Entomological Society of America",
number = "4",

}

RIS

TY - JOUR

T1 - Understanding Mosquito Surveillance Data for Analytic Efforts

T2 - A Case Study

AU - Brown, Heidi

AU - Sedda, Luigi

AU - Sumner, Chris

AU - Stefanakos, Elene

AU - Ruberto, Irene

AU - Roach, Matthew

N1 - 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: 58, 4, 1619-1625 is available online at: https://academic.oup.com/jme/article/58/4/1619/6146055

PY - 2021/7/31

Y1 - 2021/7/31

N2 - 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.

AB - 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.

U2 - 10.1093/jme/tjab018

DO - 10.1093/jme/tjab018

M3 - Journal article

VL - 58

SP - 1619

EP - 1625

JO - Journal of Medical Entomology

JF - Journal of Medical Entomology

SN - 0022-2585

IS - 4

ER -