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Moustris, K.P., Douros, K., Nastos, P.T., Larissi, I.K., Anthracopoulos, M.B., Paliatsos, A.G. and Priftis, K.N. (2011) Seven-days-ahead forecasting of childhood asthma admissions using artificial neural networks in Athens, Greece. International Journal of Environmental Health Research, 22, 93-104. doi:10.1080/09603123.2011.605876

has been cited by the following article:

• TITLE: Modelling and predicting low count child asthma hospital readmissions using General Additive Models

  AUTHORS: Don Vicendese, Andriy Olenko, Shyamali Dharmage, Mimi Tang, Michael Abramson, Bircan Erbas

  KEYWORDS: Asthma; Readmission; Semi-Parametric Models; Seasonality; Time Trend; Low Count; Time Series

  JOURNAL NAME: Open Journal of Epidemiology, Vol.3 No.3, August 9, 2013

  ABSTRACT: Background: Daily paediatric asthma readmissions within 28 days are a good example of a low count time series and not easily amenable to common time series methods used in studies of asthma seasonality and time trends. We sought to model and predict daily trends of childhood asthma readmissions over time inVictoria,Australia. Methods: We used a database of 75,000 childhood asthma admissions from the Department ofHealth,Victoria,Australiain 1997-2009. Daily admissions over time were modeled using a semi parametric Generalized Additive Model (GAM) and by sex and age group. Predictions were also estimated by using these models. Results: N = 2401 asthma readmissions within 28 days occurred during study period. Of these, n = 1358 (57%) were boys. Overall, seasonal peaks occurred in winter (30.5%) followed by autumn (28.6%) and then spring (24.6%) (p p