Evidence map›Paper›PMID 30418968›Full record

ArticlePLoS neglected tropical diseases2018

Estimating the elimination feasibility in the 'end game' of control efforts for parasites subjected to regular mass drug administration: Methods and their application to schistosomiasis.

Arathi Arakala, Christopher M Hoover, John M Marshall, Susanne H Sokolow, Giulio A De Leo, Jason R Rohr, Justin V Remais, Manoj Gambhir

Abstract read
In one paragraph

Article in PLoS neglected tropical diseases, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
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

8 authors.

Arathi ArakalaDepartment of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia.
Christopher M HooverDivision of Environmental Health Sciences, School of Public Health, University of California, Berkeley, California, United States of America.
John M MarshallDivision of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, California, United States of America.
Susanne H SokolowDepartment of Biology-Hopkins Marine Station, Stanford University, Pacific Grove, California, United States of America.
Giulio A De LeoDepartment of Biology-Hopkins Marine Station, Stanford University, Pacific Grove, California, United States of America.
Jason R RohrDepartment of Integrative Biology, University of Southern Florida, Tampa, Florida, United States of America.
Justin V RemaisDivision of Environmental Health Sciences, School of Public Health, University of California, Berkeley, California, United States of America.
Manoj GambhirDepartment of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia.ORCID 0000-0002-4831-6261

Funding

Spatio-temporal data integration methods for infectious disease surveillanceR01AI125842 · NIAID · UNIVERSITY OF CALIFORNIA BERKELEY · PI CHANG, HOWARD H, REMAIS, JUSTIN V · 2017 to 2021
$3.6M
Effects of agricultural expansion and intensification on infectionsR01TW010286 · FIC · UNIVERSITY OF SOUTH FLORIDA · PI REMAIS, JUSTIN V, ROHR, JASON ROBERT · 2015 to 2019
$2.4M
FIC NIH HHS R01 TW010286NIAID NIH HHS R01 AI125842
6 · The paper itself

Abstract

Progress towards controlling and eliminating parasitic worms, including schistosomiasis, onchocerciasis, and lymphatic filariasis, is advancing rapidly as national governments, multinational NGOs, and pharmaceutical companies launch collaborative chemotherapeutic control campaigns. Critical questions remain regarding the potential for achieving elimination of these infections, and analytical methods can help to quickly estimate progress towards-and the probability of achieving-elimination over specific timeframes. Here, we propose the effective reproduction number, Reff, as a proxy of elimination potential for sexually reproducing worms that are subject to poor mating success at very low abundance (positive density dependence, or Allee effects). Reff is the number of parasites produced by a single reproductive parasite at a given stage in the transmission cycle, over the parasite's lifetime-it is the generalized form of the more familiar basic reproduction number, R0, which only applies at the beginning of an epidemic-and it can be estimated in a 'model-free' manner by an estimator ('ε'). We introduce ε, demonstrate its estimation using simulated data, and discuss how it may be used in planning and evaluation of ongoing elimination efforts for a range of parasitic diseases.

Indexed as

AnimalsBasic Reproduction NumberData CollectionDisease EradicationEpidemiologic MethodsHumansMass Drug AdministrationModels, TheoreticalOnchocerciasisParasitesParasitic DiseasesReproductionSchistosomaSchistosomiasis

Identifiers

PMID30418968
PMCPMC6258430

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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.