tetano
Editor, Senior Moderator
Epidemiol Infect. 2019 Dec 2;147:e312. doi: 10.1017/S0950268819001948. [h=1]Predicting influenza-like illness-related emergency department visits by modelling spatio-temporal syndromic surveillance data.[/h]
Martin LJ[SUP]1[/SUP], Dong H[SUP]1[/SUP], Liu Q[SUP]1[/SUP], Talbot J[SUP]1[/SUP], Qiu W[SUP]1[/SUP], Yasui Y[SUP]1,[/SUP][SUP]2[/SUP].
[h=3]Author information[/h] 1 School of Public Health, University of Alberta, Edmonton, AB, Canada. 2 Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN, USA.
[h=3]Abstract[/h] Predicting the magnitude of the annual seasonal peak in influenza-like illness (ILI)-related emergency department (ED) visit volumes can inform the decision to open influenza care clinics (ICCs), which can mitigate pressure at the ED. Using ILI-related ED visit data from the Alberta Real Time Syndromic Surveillance Net for Edmonton, Alberta, Canada, we developed (training data, 1 August 2004-31 July 2008) and tested (testing data, 1 August 2008-19 February 2014) spatio-temporal statistical prediction models of daily ILI-related ED visits to estimate high visit volumes 3 days in advance. Our Main Model, based on a generalised linear mixed model with random intercept, incorporated prediction residuals over 14 days and captured increases in observed volume ahead of peaks. During seasonal influenza periods, our Main Model predicted volumes within ?30% of observed volumes for 67%-82% of high-volume days and within 0.3%-21% of observed seasonal peak volumes. Model predictions were not as successful during the 2009 H1N1 pandemic. Our model can provide early warning of increases in ILI-related ED visit volumes during seasonal influenza periods of differing intensities. These predictions may be used to support public health decisions, such as if and when to open ICCs, during seasonal influenza epidemics.
[h=4]KEYWORDS:[/h] Emergency medical services; epidemiologic methods; population surveillance; prediction modelling; respiratory tract infections
PMID: 31787127 DOI: 10.1017/S0950268819001948
Martin LJ[SUP]1[/SUP], Dong H[SUP]1[/SUP], Liu Q[SUP]1[/SUP], Talbot J[SUP]1[/SUP], Qiu W[SUP]1[/SUP], Yasui Y[SUP]1,[/SUP][SUP]2[/SUP].
[h=3]Author information[/h] 1 School of Public Health, University of Alberta, Edmonton, AB, Canada. 2 Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN, USA.
[h=3]Abstract[/h] Predicting the magnitude of the annual seasonal peak in influenza-like illness (ILI)-related emergency department (ED) visit volumes can inform the decision to open influenza care clinics (ICCs), which can mitigate pressure at the ED. Using ILI-related ED visit data from the Alberta Real Time Syndromic Surveillance Net for Edmonton, Alberta, Canada, we developed (training data, 1 August 2004-31 July 2008) and tested (testing data, 1 August 2008-19 February 2014) spatio-temporal statistical prediction models of daily ILI-related ED visits to estimate high visit volumes 3 days in advance. Our Main Model, based on a generalised linear mixed model with random intercept, incorporated prediction residuals over 14 days and captured increases in observed volume ahead of peaks. During seasonal influenza periods, our Main Model predicted volumes within ?30% of observed volumes for 67%-82% of high-volume days and within 0.3%-21% of observed seasonal peak volumes. Model predictions were not as successful during the 2009 H1N1 pandemic. Our model can provide early warning of increases in ILI-related ED visit volumes during seasonal influenza periods of differing intensities. These predictions may be used to support public health decisions, such as if and when to open ICCs, during seasonal influenza epidemics.
[h=4]KEYWORDS:[/h] Emergency medical services; epidemiologic methods; population surveillance; prediction modelling; respiratory tract infections
PMID: 31787127 DOI: 10.1017/S0950268819001948