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ISDS 2012 Conference Abstracts

Potential use of multiple surveillance data in the forecast
of hospital admissions

Objective
This paper describes the potential use of multiple influenza sur-

veillance data to forecast hospital admissions for respiratory diseases.

Introduction
A sudden surge in hospital admissions in public hospital during in-

fluenza peak season has been a challenge to healthcare and manpower
planning. In Hong Kong, the timing of influenza peak seasons are
variable and early short-term indication of possible surge may facil-
itate preparedness which could be translated into strategies such as
early discharge or reallocation of extra hospital beds. In this study
we explore the potential use of multiple routinely collected syndromic
data in the forecast of hospital admissions.

Methods
A multivariate dynamic linear time series model was fitted to mul-

tiple syndromic data including influenza-like illness (ILI) rates
among networks of public and private general practitioners (GP), and
school absenteeism rates, plus drop-in fever count data from desig-
nated flu clinics (DFC) that were created during the pandemic. The
latent process derived from the model has been used as a measure of
the influenza activity [1]. We compare the cross-correlations between
estimated influenza level based on multiple surveillance data and GP
ILI data, versus accident and emergency hospital admissions with
principal diagnoses of respiratory diseases and pneumonia & in-
fluenza (P&I).

Results
The estimated influenza activity has higher cross-correlation with

respiratory and P&I admissions (!=0.66 and 0.73 respectively) com-
pared to that of GP ILI rates (Table 1). Cross correlations drop dis-
tinctly after lag 2 for both estimated influenza activity and GP ILI
rates.

Conclusions
The use of a multivariate method to integrate information from

multiple sources of influenza surveillance data may have the poten-
tial to improve forecasting of admission surge of respiratory diseases.

Table 1. Cross correlations between the estimated influenza activity based on
the multivariate dynamic linear model, GP ILI rate versus A&E respiratory
diseases and P&I admissions

*negative lags refer to correlations between lagged surveillance data and
hospital admissions

Keywords
influenza; surveillance; admission; respiratory

References

Lau EH, Cheng CK, Ip DK, Cowling BJ. Situational awareness of in-
fluenza activity based on multiple streams of surveillance data using
multivariate dynamic linear model. PLoS ONE 7(5): e38346.
doi:10.1371/journal.pone.0038346

*Eric H. Lau
E-mail: ehylau@hku.hk

Eric H.Y. Lau*¹, Dennis K.M. Ip¹ and Benjamin J. Cowling¹

¹School of Public Health, The University of Hong Kong, Hong Kong

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(1):e168, 2013