Evidence map›Paper›PMID 34594501›Full record

ArticleEcology and evolution2021

Optimizing noninvasive sampling of a zoonotic bat virus.

John R Giles, Alison J Peel, Konstans Wells, Raina K Plowright, Hamish McCallum, Olivier Restif

Open access · goldAbstract read
In one paragraph

Article in Ecology and evolution, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 2 pooled it
2.9field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 28 citations in OpenAlex.

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  16. Sampling to elucidate the dynamics of infections in reservoir hosts.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 4 institutions in 3 countries.

John R GilesDepartment of Epidemiology Johns Hopkins University Bloomberg School of Public Health Baltimore MD USA.ORCID https://orcid.org/0000-0002-0954-4093
Alison J PeelEnvironmental Futures Research Institute Griffith University Brisbane Qld Australia.ORCID https://orcid.org/0000-0003-3538-3550
Konstans WellsDepartment of Biosciences Swansea University Swansea UK.ORCID https://orcid.org/0000-0003-0377-2463
Raina K PlowrightDepartment of Microbiology and Immunology Montana State University Bozeman MT USA.
Hamish McCallumEnvironmental Futures Research Institute Griffith University Brisbane Qld Australia.ORCID https://orcid.org/0000-0002-3493-0412
Olivier RestifDisease Dynamics Unit Department of Veterinary Medicine University of Cambridge Cambridge UK.
Griffith University · AUMontana State University · USSwansea University · GBUniversity of Cambridge · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Outbreaks of infectious viruses resulting from spillover events from bats have brought much attention to bat-borne zoonoses, which has motivated increased ecological and epidemiological studies on bat populations. Field sampling methods often collect pooled samples of bat excreta from plastic sheets placed under-roosts. However, positive bias is introduced because multiple individuals may contribute to pooled samples, making studies of viral dynamics difficult. Here, we explore the general issue of bias in spatial sample pooling using Hendra virus in Australian bats as a case study. We assessed the accuracy of different under-roost sampling designs using generalized additive models and field data from individually captured bats and pooled urine samples. We then used theoretical simulation models of bat density and under-roost sampling to understand the mechanistic drivers of bias. The most commonly used sampling design estimated viral prevalence 3.2 times higher than individual-level data, with positive bias 5-7 times higher than other designs due to spatial autocorrelation among sampling sheets and clustering of bats in roosts. Simulation results indicate using a stratified random design to collect 30-40 pooled urine samples from 80 to 100 sheets, each with an area of 0.75-1 m

Indexed as

bat virussampling biasunder roost samplingviral prevalence

Identifiers

PMID34594501
PMCPMC8462156
OpenAlexW3196908007

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.