COVID-19 among people experiencing homelessness in England: a modelling study

This article has been Reviewed by the following groups

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Abstract

No abstract available

Article activity feed

  1. SciScore for 10.1101/2020.05.04.20079301: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.


    Results from OddPub: Thank you for sharing your code and data.


    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Strengths and limitations: To our knowledge this model is the first estimate of potential COVID-19 transmission in a homeless population. The impact of COVID-19 on people experiencing homelessness in the United States has been estimated, though this model uses a fixed ’peak infection’ of 40% and does not model transmission.9 We used a dynamic transmission model that allowed us to estimate the benefits of isolating vulnerable and infectious individuals. We used real-time data from the homeless population of London to calibrate our assumptions about transmission. Our scenario analysis identified key success factors for the intervention. We sought to account for real-world complexities including behavioural factors (such as non-acceptance of COVID-CARE or COVID-PROTECT and self-discharge), asymptomatic and pre-symptomatic cases being admitted to COVID-PROTECT and thereby increasing transmission in this setting, respiratory symptoms due to causes other than COVID-19 (which may lead to people who are susceptible to COVID-19 being admitted to COVID-CARE), and limits to accommodation and COVID-19 testing capacity. The main limitations of the model relate to uncertainties regarding COVID-19’s epidemiological and clinical characteristics, operational issues such as bed capacity, and residents’ behaviour within COVID-CARE and COVID-PROTECT. We used published evidence to support our assumptions where possible, and sought feedback from healthcare workers and programme managers who are st...

    Results from TrialIdentifier: No clinical trial numbers were referenced.


    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.


    Results from JetFighter: We did not find any issues relating to colormaps.


    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.