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Humans as Animal Sentinels for Forecasting Asthma Events: Helping Health Services Become More Responsive

Soyiri, Ireneous N.; Reidpath, Daniel D.

Authors

Daniel D. Reidpath



Contributors

Devendra Amre
Editor

Abstract

The concept of forecasting asthma using humans as animal sentinels is uncommon. This study explores the plausibility of predicting future asthma daily admissions using retrospective data in London (2005-2006). Negative binomial regressions were used in modeling; allowing the non-contiguous autoregressive components. Selected lags were based on partial autocorrelation function (PACF) plot with a maximum lag of 7 days. The model was contrasted with naïve historical and seasonal models. All models were cross validated. Mean daily asthma admission in 2005 was 27.9 and in 2006 it was 28.9. The lags 1, 2, 3, 6 and 7 were independently associated with daily asthma admissions based on their PACF plots. The lag model prediction of peak admissions were often slightly out of synchronization with the actual data, but the days of greater admissions were better matched than the days of lower admissions. A further investigation across various populations is necessary. © 2012 Soyiri, Reidpath.

Journal Article Type Article
Publication Date Oct 31, 2012
Journal PLoS ONE
Print ISSN 1932-6203
Electronic ISSN 1932-6203
Publisher Public Library of Science
Peer Reviewed Peer Reviewed
Volume 7
Issue 10
Pages e47823
APA6 Citation Soyiri, I. N., & Reidpath, D. D. (2012). Humans as Animal Sentinels for Forecasting Asthma Events: Helping Health Services Become More Responsive. PloS one, 7(10), e47823. https://doi.org/10.1371/journal.pone.0047823
DOI https://doi.org/10.1371/journal.pone.0047823
Keywords General Biochemistry, Genetics and Molecular Biology; General Agricultural and Biological Sciences; General Medicine

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Copyright Statement
© 2012 Soyiri, Reidpath. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.





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