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

Scientific Reports
Elaine O NsoesieJohn S Brownstein

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 sugge...Continue Reading

References

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Citations

Mar 26, 2021·Scientific Reports·Elaine O NsoesieMartial L Ndeffo-Mbah
Apr 30, 2021·JMIR Public Health and Surveillance·Olubusola OladejiElaine O Nsoesie

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Methods Mentioned

BETA
Feature Extraction

Software Mentioned

Remote Sensing Metrics ( RS Metrics )
Bing Maps
R
Google Earth
Google Maps
Automated Feature Extraction ( AFE )

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