Evidence map›Paper›PMID 29579037›Full record

ArticlePLoS computational biology2018

Scabies in residential care homes: Modelling, inference and interventions for well-connected population sub-units.

Timothy Kinyanjui, Jo Middleton, Stefan Güttel, Jackie Cassell, Joshua Ross, Thomas House

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.6field-weighted citation impact, top 18% 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

8 citing papers in PubMed, 25 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. [Scabies in old age].Zeitschrift fur Gerontologie und Geriatrie · 2019
    Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 2 countries.

Timothy KinyanjuiSchool of Mathematics, University of Manchester, United Kingdom.ORCID 0000-0001-5461-4946
Jo MiddletonDepartment of Primary Care and Public Health Medicine, Brighton and Sussex Medical School, Falmer, Brighton, United Kingdom.
Stefan GüttelSchool of Mathematics, University of Manchester, United Kingdom.
Jackie CassellDepartment of Primary Care and Public Health Medicine, Brighton and Sussex Medical School, Falmer, Brighton, United Kingdom.
Joshua RossSchool of Mathematical Sciences, The University of Adelaide, Adelaide, Australia.
Thomas HouseSchool of Mathematics, University of Manchester, United Kingdom.ORCID 0000-0001-5835-8062
University of Manchester · GBBrighton and Sussex Medical School · GBThe University of Adelaide · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the context of an ageing population, understanding the transmission of infectious diseases such as scabies through well-connected sub-units of the population, such as residential care homes, is particularly important for the design of efficient interventions to mitigate against the effects of those diseases. Here, we present a modelling methodology based on the efficient solution of a large-scale system of linear differential equations that allows statistical calibration of individual-based random models to real data on scabies in residential care homes. In particular, we review and benchmark different numerical methods for the integration of the differential equation system, and then select the most appropriate of these methods to perform inference using Markov chain Monte Carlo. We test the goodness-of-fit of this model using posterior predictive intervals and propagate forward the resulting parameter uncertainty in a Bayesian framework to consider the economic cost of delayed interventions against scabies, quantifying the benefits of prompt action in the event of detection. We also revisit the previous methodology used to assess the safety of treatments in small population sub-units-in this context ivermectin-and demonstrate that even a very slight relaxation of the implicit assumption of homogeneous death rates significantly increases the plausibility of the hypothesis that ivermectin does not cause excess mortality based upon the data of Barkwell and Shields.

Indexed as

Bayes TheoremCommunicable Disease ControlCommunicable DiseasesHumansIvermectinMarkov ChainsMonte Carlo MethodResidential FacilitiesScabiesIvermectin

Identifiers

PMID29579037
PMCPMC5898763
OpenAlexW2789802022

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.