Evidence map›Paper›PMID 39627231›Full record

SynthesisNature communications2024

A systematic review of using population-level human mobility data to understand SARS-CoV-2 transmission.

Natalya Kostandova, Catherine Schluth, Rohan Arambepola, Fatumah Atuhaire, Sophie Bérubé, Taylor Chin, Eimear Cleary, Oscar Cortes-Azuero, Bernardo García-Carreras, Kyra H Grantz and 20 more

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
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

6 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Review
  5. Article
  6. 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

30 authors.

Natalya Kostandova *Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA. nkostan1@jh.edu.ORCID http://orcid.org/0000-0002-6184-531X
Catherine Schluth *Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
Rohan ArambepolaDepartment of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.ORCID http://orcid.org/0000-0003-2833-8786
Fatumah AtuhaireWorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK.ORCID http://orcid.org/0000-0002-2768-9472
Sophie BérubéDepartment of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.ORCID http://orcid.org/0000-0003-1307-4757
Taylor ChinDivision of Host-Microbe Systems & Therapeutics, Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA.ORCID http://orcid.org/0000-0001-6852-1169
Eimear ClearyWorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK.
Oscar Cortes-AzueroDepartment of Genetics, University of Cambridge, Cambridge, CB2 3EH, UK.ORCID http://orcid.org/0009-0004-7782-8129
Bernardo García-CarrerasDepartment of Biology, University of Florida, Gainesville, FL, USA.
Kyra H GrantzDepartment of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.ORCID http://orcid.org/0000-0002-2319-5010
Matt D T HitchingsDepartment of Biostatistics, University of Florida, Gainesville, USA.ORCID http://orcid.org/0000-0003-2327-3557
Angkana T HuangDepartment of Genetics, University of Cambridge, Cambridge, CB2 3EH, UK.ORCID http://orcid.org/0000-0002-9857-3506
Nishant KishoreDepartment of Epidemiology, Harvard TH Chan School of Public Health, Boston, Massachusetts, USA.
Shengjie LaiWorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK.ORCID http://orcid.org/0000-0001-9781-8148
Soren L LarsenProgram in Ecology, Evolution, and Conservation Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.ORCID http://orcid.org/0000-0003-3409-3447
Stacie LoisateDepartment of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
Pamela MartinezDepartment of Microbiology, University of Illinois Urbana-Champaign, Champaign, Illinois, USA.ORCID http://orcid.org/0000-0002-3607-0938
Hannah R MeredithDepartment of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
Ritika PurbeyPublic Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Tanjona RamiadantsoaDepartment of Integrative Biology, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Jonathan ReadHealth Information Computation and Statistics, Lancaster Medical School, Lancaster University, Lancaster, UK.
Benjamin L RiceDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.ORCID http://orcid.org/0000-0003-4844-0323
Lori RosmanInformationist Services, Welch Medical Library, Johns Hopkins University, Baltimore, MD, USA.
Nick RuktanonchaiWorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK.
Henrik SaljeDepartment of Genetics, University of Cambridge, Cambridge, CB2 3EH, UK.ORCID http://orcid.org/0000-0003-3626-4254
Kathryn L SchaberDepartment of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
Andrew J TatemWorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK.ORCID http://orcid.org/0000-0002-7270-941X
Jasmine WangDepartment of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
Derek A T CummingsDepartment of Biology, University of Florida, Gainesville, FL, USA.
Amy WesolowskiDepartment of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.ORCID http://orcid.org/0000-0001-6320-3575

Funding

Human mobility models to forecast disease dynamics and the effectiveness of public health interventionsR01AI160780 · NIAID · JOHNS HOPKINS UNIVERSITY · PI CUMMINGS, DEREK A, WESOLOWSKI, AMY · 2021 to 2025
$3.3M
Disease Emergence and Elimination: Using Mobility Data to Inform Spatial Disease DynamicsDP2LM013102 · NLM · JOHNS HOPKINS UNIVERSITY · PI WESOLOWSKI, AMY · 2018 to 2018
$2.5M
Research in practice: translating infectious disease epidemiology RIP-TIDET32AI165369 · NIAID · JOHNS HOPKINS UNIVERSITY · PI WILLIAM J MOSS · 2022 to 2026
$1.5M
Burroughs Wellcome Fund (BWF) 1015823.03NIAID NIH HHS R01 AI160780NIAID NIH HHS T32 AI165369NLM NIH HHS DP2 LM013102U.S. Department of Health & Human Services | National Institutes of Health (NIH) 1R01AI160780-01U.S. Department of Health & Human Services | National Institutes of Health (NIH) 1T32AI165369-01A1U.S. Department of Health & Human Services | National Institutes of Health (NIH) DP2LM013102U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID) 1R01AI160780-01
6 · The paper itself

Abstract

The emergence of SARS-CoV-2 into a highly susceptible global population was primarily driven by human mobility-induced introduction events. Especially in the early stages, understanding mobility was vital to mitigating the pandemic prior to widespread vaccine availability. We conducted a systematic review of studies published from January 1, 2020, to May 9, 2021, that used population-level human mobility data to understand SARS-CoV-2 transmission. Of the 5505 papers with abstracts screened, 232 were included in the analysis. These papers focused on a range of specific questions but were dominated by analyses focusing on the USA and China. The majority included mobile phone data, followed by Google Community Mobility Reports, and few included any adjustments to account for potential biases in population sampling processes. There was no clear relationship between methods used to integrate mobility and SARS-CoV-2 data and goals of analysis. When considering papers focused only on the estimation of the effective reproductive number within the US, there was no clear relationship identified between this measure and changes in mobility patterns. Our findings underscore the need for standardized, systematic ways to identify the source of mobility data, select an appropriate approach to using it in analysis, and reporting.

Indexed as

COVID-19SARS-CoV-2Basic Reproduction NumberCell PhoneChinaHumansPandemicsTravelUnited States

Identifiers

PMID39627231
PMCPMC11615209

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.