Evidence map›Paper›PMID 37146037›Full record

ArticlePloS one2023

Modelling the impact of non-pharmaceutical interventions on workplace transmission of SARS-CoV-2 in the home-delivery sector.

Carl A Whitfield, Martie van Tongeren, Yang Han, Hua Wei, Sarah Daniels, Martyn Regan, David W Denning, Arpana Verma, Lorenzo Pellis, Ian Hall and 1 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
1.1field-weighted citation impact, top 26% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
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

11 authors at 2 institutions in 1 country.

Carl A WhitfieldDepartment of Mathematics, University of Manchester, Manchester, England.ORCID 0000-0001-5913-735X
Martie van TongerenManchester Academic Health Science Centre, University of Manchester, Manchester, England.
Yang HanDepartment of Mathematics, University of Manchester, Manchester, England.
Hua WeiManchester Academic Health Science Centre, University of Manchester, Manchester, England.
Sarah DanielsManchester Academic Health Science Centre, University of Manchester, Manchester, England.
Martyn ReganManchester Academic Health Science Centre, University of Manchester, Manchester, England.ORCID 0000-0003-2962-9913
David W DenningDivision of Infection, Immunity & Respiratory Medicine, School of Biological Sciences, University of Manchester, Manchester, England.ORCID 0000-0001-5626-2251
Arpana VermaManchester Academic Health Science Centre, University of Manchester, Manchester, England.
Lorenzo PellisDepartment of Mathematics, University of Manchester, Manchester, England.
Ian HallDepartment of Mathematics, University of Manchester, Manchester, England.
with the University of Manchester COVID-19 Modelling Group
Manchester Academic Health Science Centre · GBUniversity of Manchester · GB

Funding

Department of Health PR-R17-0916-21001Medical Research Council MC_PC_19083Medical Research Council MR/V038613/1Wellcome TrustWellcome Trust 202562/Z/16/Z
6 · The paper itself

Abstract

objectiveWe aimed to use mathematical models of SARS-COV-2 to assess the potential efficacy of non-pharmaceutical interventions on transmission in the parcel delivery and logistics sector.

methodsWe devloped a network-based model of workplace contacts based on data and consultations from companies in the parcel delivery and logistics sectors. We used these in stochastic simulations of disease transmission to predict the probability of workplace outbreaks in this settings. Individuals in the model have different viral load trajectories based on SARS-CoV-2 in-host dynamics, which couple to their infectiousness and test positive probability over time, in order to determine the impact of testing and isolation measures.

resultsThe baseline model (without any interventions) showed different workplace infection rates for staff in different job roles. Based on our assumptions of contact patterns in the parcel delivery work setting we found that when a delivery driver was the index case, on average they infect only 0.14 other employees, while for warehouse and office workers this went up to 0.65 and 2.24 respectively. In the LIDD setting this was predicted to be 1.40, 0.98, and 1.34 respectively. Nonetheless, the vast majority of simulations resulted in 0 secondary cases among customers (even without contact-free delivery). Our results showed that a combination of social distancing, office staff working from home, and fixed driver pairings (all interventions carried out by the companies we consulted) reduce the risk of workplace outbreaks by 3-4 times.

conclusionThis work suggests that, without interventions, significant transmission could have occured in these workplaces, but that these posed minimal risk to customers. We found that identifying and isolating regular close-contacts of infectious individuals (i.e. house-share, carpools, or delivery pairs) is an efficient measure for stopping workplace outbreaks. Regular testing can make these isolation measures even more effective but also increases the number of staff isolating at one time. It is therefore more efficient to use these isolation measures in addition to social distancing and contact reduction interventions, rather than instead of, as these reduce both transmission and the number of people needing to isolate at one time.

Indexed as

COVID-19SARS-CoV-2Disease OutbreaksHumansModels, TheoreticalWorkplace

Identifiers

PMID37146037
PMCPMC10162531
OpenAlexW4372333011

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.