A multi-tiered time-series modelling approach to forecasting respiratory syncytial virus incidence at the local level

M. C. Spaeder, J. C. Fackler

Research output: Contribution to journalArticle

Abstract

Respiratory syncytial virus (RSV) is the most common cause of documented viral respiratory infections, and the leading cause of hospitalization, in young children. We performed a retrospective time-series analysis of all patients aged <18 years with laboratory-confirmed RSV within a network of multiple affiliated academic medical institutions. Forecasting models of weekly RSV incidence for the local community, inpatient paediatric hospital and paediatric intensive-care unit (PICU) were created. Ninety-five percent confidence intervals calculated around our models' 2-week forecasts were accurate to ±9•3, ±7•5 and ±1•5 cases/week for the local community, inpatient hospital and PICU, respectively. Our results suggest that time-series models may be useful tools in forecasting the burden of RSV infection at the local and institutional levels, helping communities and institutions to optimize distribution of resources based on the changing burden and severity of illness in their respective communities.

Original languageEnglish (US)
Pages (from-to)602-607
Number of pages6
JournalEpidemiology and infection
Volume140
Issue number4
DOIs
StatePublished - Apr 1 2012

Keywords

  • Modelling
  • respiratory infections
  • respiratory syncytial virus

ASJC Scopus subject areas

  • Epidemiology
  • Infectious Diseases

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