OBJECTIVE: To describe the use of surveillance and forecasting models to predict and track epidemics (and, potentially, pandemics) of influenza. METHODS: We collected 5 years of historical data (2005-2009) on emergency department presentations and hospital admissions for influenza-like illnesses (International Classification of Diseases [ICD-10-AM] coding) from the Emergency Department Information System (EDIS) database of 27 Queensland public hospitals. The historical data were used to generate prediction and surveillance models, which were assessed across the 2009 southern hemisphere influenza season (June-September) for their potential usefulness in informing response policy. Three models are described: (i) surveillance monitoring of influenza presentations using adaptive cumulative sum (CUSUM) plan analysis to signal unusual activity; (ii) generating forecasts of expected numbers of presentations for influenza, based on historical data; and (iii) using Google search data as outbreak notification among a population. RESULTS: All hospitals, apart from one, had more than the expected number of presentations for influenza starting in late 2008 and continuing into 2009. (i) The CUSUM plan signalled an unusual outbreak in December 2008, which continued in early 2009 before the winter influenza season commenced. (ii) Predictions based on historical data alone underestimated the actual influenza presentations, with 2009 differing significantly from previous years, but represent a baseline for normal ED influenza presentations. (iii) The correlation coefficients between internet search data for Queensland and statewide ED influenza presentations indicated an increase in correlation since 2006 when weekly influenza search data became available. CONCLUSION: This analysis highlights the value of health departments performing surveillance monitoring to forewarn of disease outbreaks. The best system among the three assessed was a combination of routine forecasting methods coupled with an adaptive CUSUM method.
OBJECTIVE: To describe the use of surveillance and forecasting models to predict and track epidemics (and, potentially, pandemics) of influenza. METHODS: We collected 5 years of historical data (2005-2009) on emergency department presentations and hospital admissions for influenza-like illnesses (International Classification of Diseases [ICD-10-AM] coding) from the Emergency Department Information System (EDIS) database of 27 Queensland public hospitals. The historical data were used to generate prediction and surveillance models, which were assessed across the 2009 southern hemisphere influenza season (June-September) for their potential usefulness in informing response policy. Three models are described: (i) surveillance monitoring of influenza presentations using adaptive cumulative sum (CUSUM) plan analysis to signal unusual activity; (ii) generating forecasts of expected numbers of presentations for influenza, based on historical data; and (iii) using Google search data as outbreak notification among a population. RESULTS: All hospitals, apart from one, had more than the expected number of presentations for influenza starting in late 2008 and continuing into 2009. (i) The CUSUM plan signalled an unusual outbreak in December 2008, which continued in early 2009 before the winter influenza season commenced. (ii) Predictions based on historical data alone underestimated the actual influenza presentations, with 2009 differing significantly from previous years, but represent a baseline for normal ED influenza presentations. (iii) The correlation coefficients between internet search data for Queensland and statewide ED influenza presentations indicated an increase in correlation since 2006 when weekly influenza search data became available. CONCLUSION: This analysis highlights the value of health departments performing surveillance monitoring to forewarn of disease outbreaks. The best system among the three assessed was a combination of routine forecasting methods coupled with an adaptive CUSUM method.
Authors: Hannah C Moore; Nicholas de Klerk; Peter Jacoby; Peter Richmond; Deborah Lehmann Journal: BMC Public Health Date: 2012-08-28 Impact factor: 3.295
Authors: Andrea Freyer Dugas; Mehdi Jalalpour; Yulia Gel; Scott Levin; Fred Torcaso; Takeru Igusa; Richard E Rothman Journal: PLoS One Date: 2013-02-14 Impact factor: 3.240