ArticleJournal of the Royal Society, Interface2021
Estimating disease vector population size from citizen science data.
Article in Journal of the Royal Society, Interface, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Assessing the Effectiveness of Crowdsourced Data to Detect Established Tick Populations in Quebec, Canada: Retrospective Ecological Study.JMIR public health and surveillance · 2026Article
- Application of remote sensing and geospatial technologies in predicting vector-borne disease outbreaks.Royal Society open science · 2025Review
- The academic impact of Open Science: a scoping review.Royal Society open science · 2025Review
- Comparative ecological analysis and predictive modeling of tick-borne pathogens.Journal of medical entomology · 2025Article
- Models and data used to predict the abundance and distribution ofLancet regional health. Americas · 2024Article
- Assessing the impact of areal unit selection and the modifiable areal unit problem on associative statistics between cases of tick-borne disease and entomological indices.Journal of medical entomology · 2024Article
- Developing One Health surveillance systems.One health (Amsterdam, Netherlands) · 2023Review
- Modeling Tick Populations: An Ecological Test Case for Gradient Boosted Trees.bioRxiv : the preprint server for biology · 2023Article
- Comparison of acarological risk metrics derived from active and passive surveillance and their concordance with tick-borne disease incidence.Ticks and tick-borne diseases · 2023Article
- Emergence of Hyalomma marginatum and Hyalomma rufipes adults revealed by citizen science tick monitoring in Hungary.Transboundary and emerging diseases · 2022Article
- Article
- Community engaged tick surveillance and tickMAP as a public health tool to track the emergence of ticks and tick-borne diseases in New York.PLOS global public health · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Citizen science projects have the potential to address hypotheses requiring extremely large datasets that cannot be collected with the financial and labour constraints of most scientific projects. Data collection by the general public could expand the scope of scientific enquiry if these data accurately capture the system under study. However, data collection inconsistencies by the untrained public may result in biased datasets that do not accurately represent the natural world. In this paper, we harness the availability of scientific and public datasets of the Lyme disease tick vector to identify and account for biases in citizen science tick collections. Estimates of tick abundance from the citizen science dataset correspond moderately with estimates from direct surveillance but exhibit consistent biases. These biases can be mitigated by including factors that may impact collector participation or effort in statistical models, which, in turn, result in more accurate estimates of tick population sizes. Accounting for collection biases within large-scale, public participation datasets could update species abundance maps and facilitate using the wealth of citizen science data to answer scientific questions at scales that are not feasible with traditional datasets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.