Evidence map›Paper›PMID 30854281›Full record

ArticleEPJ data science2018

Inferences about spatiotemporal variation in dengue virus transmission are sensitive to assumptions about human mobility: a case study using geolocated tweets from Lahore, Pakistan.

Moritz U G Kraemer, D Bisanzio, R C Reiner, R Zakar, J B Hawkins, C C Freifeld, D L Smith, S I Hay, J S Brownstein, T Alex Perkins

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PubMed Full text at PMC Publisher
In one paragraph

Article in EPJ data science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 2 pooled it
–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

23 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  6. Climate, demography, immunology, and virology combine to drive two decades of dengue virus dynamics in Cambodia.Proceedings of the National Academy of Sciences of the United States of America · 2024
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  7. Human mobility and the infectious disease transmission: A systematic review.Geo-spatial information science = Diqui kongjian xinxi kexue xuebao · 2024
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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

10 authors.

Moritz U G Kraemer1Department of Pediatrics, Harvard Medical School, Boston, USA.
D Bisanzio4RTI International, Washington, USA.
R C Reiner6Institute for Health Metrics and Evaluation, University of Washington, Seattle, USA.
R Zakar7Department of Public Health, University of Punjab, Lahore, Pakistan.
J B Hawkins1Department of Pediatrics, Harvard Medical School, Boston, USA.
C C Freifeld2Computational Epidemiology Lab, Boston Children's Hospital, Boston, USA.
D L Smith6Institute for Health Metrics and Evaluation, University of Washington, Seattle, USA.
S I Hay6Institute for Health Metrics and Evaluation, University of Washington, Seattle, USA.
J S Brownstein1Department of Pediatrics, Harvard Medical School, Boston, USA.
T Alex Perkins10Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Billions of users of mobile phones, social media platforms, and other technologies generate an increasingly large volume of data that has the potential to be leveraged towards solving public health challenges. These and other big data resources tend to be most successful in epidemiological applications when utilized within an appropriate conceptual framework. Here, we demonstrate the importance of assumptions about host mobility in a framework for dynamic modeling of infectious disease spread among districts within a large urban area. Our analysis focused on spatial and temporal variation in the transmission of dengue virus (DENV) during a series of large seasonal epidemics in Lahore, Pakistan during 2011-2014. Similar to many directly transmitted diseases, DENV transmission occurs primarily where people spend time during daytime hours, given that DENV is transmitted by a day-biting mosquito. We inferred spatiotemporal variation in DENV transmission under five different assumptions about mobility patterns among ten districts of Lahore: no movement among districts, movement following patterns of geo-located tweets, movement proportional to district population size, and movement following the commonly used gravity and radiation models. Overall, we found that inferences about spatiotemporal variation in DENV transmission were highly sensitive to this range of assumptions about intra-urban human mobility patterns, although the three assumptions that allowed for a modest degree of intra-urban mobility all performed similarly in key respects. Differing inferences about transmission patterns based on our analysis are significant from an epidemiological perspective, as they have different implications for where control efforts should be targeted and whether conditions for transmission became more or less favorable over time. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1140/epjds/s13688-018-0144-x) contains supplementary material.

Indexed as

Big dataDisease dynamicsGeo-located tweetsGravity modelHuman mobilityRadiation modelSpatiotemporal analysisTwitter data

Identifiers

PMID30854281
PMCPMC6404370

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.