Evidence map›Paper›PMID 25765943›Full record

ArticleScientific reports2015

Monitoring disease trends using hospital traffic data from high resolution satellite imagery: a feasibility study.

Elaine O Nsoesie, Patrick Butler, Naren Ramakrishnan, Sumiko R Mekaru, John S Brownstein

Abstract read
In one paragraph

Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Characterizing population-level changes in human behavior during the COVID-19 pandemic in the United States.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  2. Observational
  3. Article
  4. FluSense: A Contactless Syndromic Surveillance Platform for Influenza-Like Illness in Hospital Waiting Areas.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2020
    Article
  5. Article
  6. Article
  7. Article
  8. Article
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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.

Elaine O Nsoesie1] Children's Hospital Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA [2] Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA.
Patrick ButlerDepartment of Computer Science, Virginia Tech, Blacksburg, Virginia, USA.
Naren RamakrishnanDepartment of Computer Science, Virginia Tech, Blacksburg, Virginia, USA.
Sumiko R MekaruChildren's Hospital Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA.
John S Brownstein1] Children's Hospital Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA [2] Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA [3] Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada.

Funding

A Platform for Modeling the Global Impact of Climate Change on Infectious DiseaseR01LM010812 · NLM · BOSTON CHILDREN'S HOSPITAL · PI BROWNSTEIN, JOHN S. · 2010 to 2015
$2.5M
A Framework for Integrating Multiple Data Sources for Modeling and Forecasting of Infectious DiseasesK01ES025438 · NIEHS · UNIVERSITY OF WASHINGTON · PI NSOESIE, ELAINE O. · 2014 to 2016
$322k
NIEHS NIH HHS K01 ES025438NIEHS NIH HHS K01ES025438NLM NIH HHS 5R01LM010812-05NLM NIH HHS R01 LM010812
6 · The paper itself

Abstract

Challenges with alternative data sources for disease surveillance include differentiating the signal from the noise, and obtaining information from data constrained settings. For the latter, events such as increases in hospital traffic could serve as early indicators of social disruption resulting from disease. In this study, we evaluate the feasibility of using hospital parking lot traffic data extracted from high-resolution satellite imagery to augment public health disease surveillance in Chile, Argentina and Mexico. We used archived satellite imagery collected from January 2010 to May 2013 and data on the incidence of respiratory virus illnesses from the Pan American Health Organization as a reference. We developed dynamical Elastic Net multivariable linear regression models to estimate the incidence of respiratory virus illnesses using hospital traffic and assessed how to minimize the effects of noise on the models. We noted that predictions based on models fitted using a sample of observations were better. The results were consistent across countries with selected models having reasonably low normalized root-mean-squared errors and high correlations for both the fits and predictions. The observations from this study suggest that if properly procured and combined with other information, this data source could be useful for monitoring disease trends.

Indexed as

Datasets as TopicSatellite ImageryArgentinaChileFeasibility StudiesHospitalsHumansMexicoPopulation Surveillance

Identifiers

PMID25765943
PMCPMC4357853

What OpenQuestion holds

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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.