Evidence map›Paper›PMID 31245391›Full record

ArticleFrontiers in veterinary science2019

Validation of Application SuperDuplicates (AS) Enumeration Tool for Free-Roaming Dogs (FRD) in Urban Settings of Panchkula Municipal Corporation in North India.

Harish Kumar Tiwari, Ian D Robertson, Mark O'Dea, Jully Gogoi-Tiwari, Pranav Panvalkar, Rajinder Singh Bajwa, Abi Tamim Vanak

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Article in Frontiers in veterinary science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

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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

7 authors.

Harish Kumar TiwariSchool of Veterinary Medicine, Murdoch University, Perth, WA, Australia.
Ian D RobertsonSchool of Veterinary Medicine, Murdoch University, Perth, WA, Australia.
Mark O'DeaSchool of Veterinary Medicine, Murdoch University, Perth, WA, Australia.
Jully Gogoi-TiwariSchool of Pharmacy and Biomedical Sciences, Curtin Health Innovation Research Institute, Curtin University, Perth, WA, Australia.
Pranav PanvalkarAshoka Trust for Research in Ecology and the Environment, Bangalore, India.
Rajinder Singh BajwaVeterinary Hospital, Union Territory of Chandigarh, Chandigarh, India.
Abi Tamim VanakAshoka Trust for Research in Ecology and the Environment, Bangalore, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A cost-effective estimation of the number of free-roaming dogs is an essential prerequisite for the control of rabies in countries where the disease is endemic, as vaccination of at least 70% of the population is recommended to effectively control the disease. Although estimating the population size through sight-resight based maximum likelihood methodology generates an estimate closest to the actual size, it requires at least five survey efforts to achieve this. In a rural setting in India, a reliable estimate of at least 70% of the likely true population of free-roaming dogs was obtained with the Application SuperDuplicates shinyapp online tool using a photographic sight-resight technique through just two surveys. We tested the wider applicability of this method by validating its use in urban settings in India. Sight-resight surveys of free-roaming dogs were conducted in 15 sectors of the Panchkula Municipal Corporation in north India during September- October 2016. A total of 1,408 unique dogs were identified through 3,465 sightings on 14 survey tracks. The estimates obtained by the Application SuperDuplicates shinyapp online tool after two surveys were compared with the maximum likelihood estimates and it was found that the former, after two surveys, provided an estimate that was at least 70% of that obtained by the latter after 5-6 surveys. Thus, the Application SuperDuplicates shinyapp online tool provides an efficient means for estimating the minimum number of free-roaming dogs to vaccinate with a considerably lower effort than the traditional mark-resight based methods. We recommend use of this tool for estimating the vaccination target of free-roaming dogs prior to undertaking mass vaccination efforts against rabies.

Indexed as

application superduplicatesdog-populationenumerationfree-roaming dogspopulation-controlrabies

Identifiers

PMID31245391
PMCPMC6562275

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Registered trials

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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.