Evidence map›Paper›PMID 30008483›Full record

ArticleThe Journal of applied ecology2018

Geostatistical models using remotely-sensed data predict savanna tsetse decline across the interface between protected and unprotected areas in Serengeti, Tanzania.

Jennifer S Lord, Stephen J Torr, Harriet K Auty, Paddy M Brock, Mechtilda Byamungu, John W Hargrove, Liam J Morrison, Furaha Mramba, Glyn A Vale, Michelle C Stanton

Abstract read
In one paragraph

Article in The Journal of applied ecology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
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

15 citing papers in PubMed.

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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

10 authors.

Jennifer S LordDepartment of Vector Biology Liverpool School of Tropical Medicine Liverpool UK.ORCID 0000-0001-6616-1526
Stephen J TorrDepartment of Vector Biology Liverpool School of Tropical Medicine Liverpool UK.
Harriet K AutyEpidemiology Research Unit SRUC Inverness UK.
Paddy M BrockInstitute of Biodiversity, Animal Health and Comparative Medicine College of Medical, Veterinary and Life Sciences University of Glasgow Glasgow UK.
Mechtilda ByamunguVector and Vector-Borne Diseases Research Institute Tanga Tanzania.
John W HargroveSACEMA University of Stellenbosch Stellenbosch South Africa.
Liam J MorrisonRoslin Institute Royal (Dick) School of Veterinary Studies University of Edinburgh Midlothian UK.
Furaha MrambaVector and Vector-Borne Diseases Research Institute Tanga Tanzania.
Glyn A ValeSACEMA University of Stellenbosch Stellenbosch South Africa.
Michelle C StantonDepartment of Vector Biology Liverpool School of Tropical Medicine Liverpool UK.

Funding

Biotechnology and Biological Sciences Research Council BB/L019035/1Medical Research Council MR/M014975/2
6 · The paper itself

Abstract

Monitoring abundance is essential for vector management, but it is often only possible in a fraction of managed areas. For vector control programmes, sampling to estimate abundance is usually carried out at a local-scale (10s km

Indexed as

diseasegeostatistical modelsGlossinapathogensprotected areasremote‐sensingsurveillancetrypanosomiasistsetsevector control

Identifiers

PMID30008483
PMCPMC6032868

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.