Skip to main content
×
Home
    • Aa
    • Aa

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

  • M. C. SPAEDER (a1) and J. C. FACKLER (a2)
Summary
SUMMARY

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.

Copyright
Corresponding author
*Author for correspondence: M. C. Spaeder, M.D., M.S., Division of Critical Care Medicine, Children's National Medical Center, 111 Michigan Avenue, NW, Washington, DC 20010, USA (Email: mspaeder@cnmc.org)
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Epidemiology & Infection
  • ISSN: 0950-2688
  • EISSN: 1469-4409
  • URL: /core/journals/epidemiology-and-infection
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Keywords:

Metrics

Full text views

Total number of HTML views: 2
Total number of PDF views: 23 *
Loading metrics...

Abstract views

Total abstract views: 120 *
Loading metrics...

* Views captured on Cambridge Core between September 2016 - 18th October 2017. This data will be updated every 24 hours.