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

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  • Ireneous N Soyiri
  • Daniel D Reidpath

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.

Suggested Citation

  • Ireneous N Soyiri & Daniel D Reidpath, 2012. "Humans as Animal Sentinels for Forecasting Asthma Events: Helping Health Services Become More Responsive," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-6, October.
    • Handle: RePEc:plo:pone00:0047823
    DOI: 10.1371/journal.pone.0047823
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    References listed on IDEAS

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    1. Koehler, Anne B. & Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian, 2012. "A study of outliers in the exponential smoothing approach to forecasting," International Journal of Forecasting, Elsevier, vol. 28(2), pages 477-484.
    2. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    3. J. Scott Long & Jeremy Freese, 2001. "Predicted probabilities for count models," Stata Journal, StataCorp LP, vol. 1(1), pages 51-57, November.
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    1. Ireneous N Soyiri & Daniel D Reidpath, 2013. "The Use of Quantile Regression to Forecast Higher Than Expected Respiratory Deaths in a Daily Time Series: A Study of New York City Data 1987-2000," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-1, October.

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