Evidence map›Paper›PMID 37640718›Full record

ArticleScientific reports2023

Travel distance, frequency of return, and the spread of disease.

Cate Heine, Kevin P O'Keeffe, Paolo Santi, Li Yan, Carlo Ratti

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.4field-weighted citation impact, top 42% 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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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 2 institutions in 2 countries.

Cate HeineSenseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. cate.heine@gmail.com.
Kevin P O'KeeffeSenseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Paolo SantiSenseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Li YanSenseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Carlo RattiSenseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Massachusetts Institute of Technology · USInstitute of Informatics and Telematics · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human mobility is a key driver of infectious disease spread. Recent literature has uncovered a clear pattern underlying the complexity of human mobility in cities: [Formula: see text], the product of distance traveled r and frequency of return f per user to a given location, is invariant across space. This paper asks whether the invariant [Formula: see text] also serves as a driver for epidemic spread, so that the risk associated with human movement can be modeled by a unifying variable [Formula: see text]. We use two large-scale datasets of individual human mobility to show that there is in fact a simple relation between r and f and both speed and spatial dispersion of disease spread. This discovery could assist in modeling spread of disease and inform travel policies in future epidemics-based not only on travel distance r but also on frequency of return f.

Indexed as

EpidemicsCitiesHumansMovementPolicyTravel

Identifiers

PMID37640718
PMCPMC10462643
OpenAlexW4386223663

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.