About: Monitoring Disease Trends using Hospital Traffic Data from High Resolution Satellite Imagery: A Feasibility Study

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About: Monitoring Disease Trends using Hospital Traffic Data from High Resolution Satellite Imagery: A Feasibility Study Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Monitoring Disease Trends using Hospital Traffic Data from High Resolution Satellite Imagery: A Feasibility Study
Creator	Ramakrishnan, Naren Brownstein, John Mekaru, Sumiko Butler, Patrick Nsoesie, Elaine
Source	Medline; PMC
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.
has issue date	2015-03-13(xsd:dateTime)
bibo:doi	10.1038/srep09112
bibo:pmid	25765943
has license	cc-by
sha1sum (hex)	9f234fa9b0d6d7c0809106111104ad8843f3a931
schema:url	https://doi.org/10.1038/srep09112
resource representing a document's title	Monitoring Disease Trends using Hospital Traffic Data from High Resolution Satellite Imagery: A Feasibility Study
has PubMed Central identifier	PMC4357853
has PubMed identifier	25765943
schema:publication	Sci Rep
resource representing a document's body	covid:9f234fa9b0d6d7c0809106111104ad8843f3a931#body_text
is schema:about of	named entity 'linear regression' named entity 'models' named entity 'events' named entity 'incidence' named entity 'augment' named entity 'DISEASE' named entity 'CHILE' named entity 'NORMALIZED' named entity 'DISEASE SURVEILLANCE' covid:arg/9f234fa9b0d6d7c0809106111104ad8843f3a931 named entity 'USING' named entity 'SAMPLE' named entity 'EVALUATE' named entity 'SIGNAL' named entity 'TRENDS' named entity 'PAN AMERICAN HEALTH ORGANIZATION' named entity 'COUNTRIES' named entity 'HOW' named entity 'ARCHIVED' named entity 'BETTER' named entity 'HEALTH DISEASE' named entity 'COLLECTED' named entity 'HIGH-RESOLUTION' named entity 'EARLY' named entity 'FEASIBILITY' named entity 'OTHER INFORMATION' named entity 'DIFFERENTIATING' named entity 'RESPIRATORY VIRUS' named entity 'NOISE' named entity 'LINEAR REGRESSION MODELS' named entity 'CHALLENGES' named entity 'HIGH RESOLUTION' named entity 'TRAFFIC' named entity 'SATELLITE IMAGERY' named entity 'REFERENCE' named entity 'STUDY' named entity 'COMBINED' named entity 'BASED' named entity 'DISRUPTION' named entity 'USING' named entity 'RESULTS' named entity 'DATA' named entity 'FEASIBILITY STUDY' named entity 'DISEASE' named entity 'ESTIMATE' named entity 'INCREASES' named entity 'INCIDENCE' named entity 'OBSERVATIONS' named entity 'USED' named entity 'EVENTS' named entity 'COULD BE' named entity 'ERRORS' named entity 'MODELS' named entity 'SETTINGS' named entity 'HOSPITAL' named entity 'ELASTIC NET' named entity 'PUBLIC HEALTH' named entity 'EXTRACTED' named entity 'RESULTING FROM' named entity 'SATELLITE IMAGERY' named entity 'INDICATORS' named entity 'PREDICTIONS' named entity 'ACROSS' named entity 'DATA' named entity 'JANUARY' named entity 'SERVE' named entity 'NOTED' named entity 'CONSISTENT' named entity 'ILLNESSES' named entity 'DATA SOURCES' named entity 'TRAFFIC' named entity 'FITS' named entity 'ASSESSED' named entity 'MONITORING' named entity 'FITTED' named entity 'ALTERNATIVE' named entity 'SOCIAL'

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