Evidence map›Paper›PMID 35306163›Full record

ArticleTravel medicine and infectious disease

Identifying mitigation strategies for COVID-19 superspreading on flights using models that account for passenger movement.

Sirish Namilae, Yuxuan Wu, Anuj Mubayi, Ashok Srinivasan, Matthew Scotch

Open access · greenAbstract read
In one paragraph

Article in Travel medicine and infectious disease. 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
4.6field-weighted citation impact, top 4% 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, 32 citations in OpenAlex.

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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

5 authors at 3 institutions in 1 country.

Sirish NamilaeEmbry-Riddle Aeronautical University, Daytona Beach, FL, USA.
Yuxuan WuEmbry-Riddle Aeronautical University, Daytona Beach, FL, USA.
Anuj MubayiThe Public Health Company, Goleta, CA, USA.
Ashok SrinivasanUniversity of West Florida, Pensacola, FL, USA. Electronic address: asrinivasan@uwf.edu.
Matthew ScotchArizona State University, Tempe, AZ, USA.
Embry–Riddle Aeronautical University · USArizona State University · USUniversity of West Florida · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDespite commercial airlines mandating masks, there have been multiple documented events of COVID-19 superspreading on flights. Conventional models do not adequately explain superspreading patterns on flights, with infection spread wider than expected from proximity based on passenger seating. An important reason for this is that models typically do not consider the movement of passengers during the flight, boarding, or deplaning. Understanding the risks for each of these aspects could provide insight into effective mitigation measures.

methodsWe modeled infection risk from seating and fine-grained movement patterns - boarding, deplaning, and inflight movement. We estimated infection model parameters from a prior superspreading event. We validated the model and the impact of interventions using available data from three flights, including cabin layout and seat locations of infected and uninfected passengers, to suggest interventions to mitigate COVID-19 superspreading events during air travel. Specifically, we studied: 1) London to Hanoi with 201 passengers, including 13 secondary infections among passengers; 2) Singapore to Hangzhou with 321 passengers, including 12 to 14 secondary infections; 3) a non-superspreading event on a private jet in Japan with 9 passengers and no secondary infections.

resultsOur results show that the inclusion of passenger movement better explains the infection spread patterns than conventional models do. We also found that FFP2/N95 mask usage would have reduced infection by 95-100%, while cloth masks would have reduced it by only 40-80%. Results indicate that leaving the middle seat vacant is effective in reducing infection, and the effectiveness increases when combined with good quality masks. However, with a good mask, the risk is quite low even without the middle seats being empty.

conclusionsOur results suggest the need for more stringent guidelines to reduce aviation-related superspreading events of COVID-19.

Indexed as

Air TravelCoinfectionCOVID-19AircraftHumansMovementAircraftInfectious disease transmissionMasksSARS-CoV-2Spatial interaction models

Identifiers

PMID35306163
PMCPMC8925197
OpenAlexW3173395103

What OpenQuestion holds

Textmetadata
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.