tetano
Editor, Senior Moderator
Eur J Epidemiol. 2011 Mar 18. [Epub ahead of print]
Nowcasting pandemic influenza A/H1N1 2009 hospitalizations in the Netherlands.
Donker T, van Boven M, van Ballegooijen WM, Van't Klooster TM, Wielders CC, Wallinga J.
National Institute for Public Health and the Environment, PO Box 1, 3720 BA, Bilthoven, The Netherlands, tjibbe.donker@rivm.nl.
Abstract
During emerging epidemics of infectious diseases, it is vital to have up-to-date information on epidemic trends, such as incidence or health care demand, because hospitals and intensive care units have limited excess capacity. However, real-time tracking of epidemics is difficult, because of the inherent delay between onset of symptoms or hospitalizations, and reporting. We propose a robust algorithm to correct for reporting delays, using the observed distribution of reporting delays. We apply the algorithm to pandemic influenza A/H1N1 2009 hospitalizations as reported in the Netherlands. We show that the proposed algorithm is able to provide unbiased predictions of the actual number of hospitalizations in real-time during the ascent and descent of the epidemic. The real-time predictions of admissions are useful to adjust planning in hospitals to avoid exceeding their capacity.
PMID: 21416274 [PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/21416274
Nowcasting pandemic influenza A/H1N1 2009 hospitalizations in the Netherlands.
Donker T, van Boven M, van Ballegooijen WM, Van't Klooster TM, Wielders CC, Wallinga J.
National Institute for Public Health and the Environment, PO Box 1, 3720 BA, Bilthoven, The Netherlands, tjibbe.donker@rivm.nl.
Abstract
During emerging epidemics of infectious diseases, it is vital to have up-to-date information on epidemic trends, such as incidence or health care demand, because hospitals and intensive care units have limited excess capacity. However, real-time tracking of epidemics is difficult, because of the inherent delay between onset of symptoms or hospitalizations, and reporting. We propose a robust algorithm to correct for reporting delays, using the observed distribution of reporting delays. We apply the algorithm to pandemic influenza A/H1N1 2009 hospitalizations as reported in the Netherlands. We show that the proposed algorithm is able to provide unbiased predictions of the actual number of hospitalizations in real-time during the ascent and descent of the epidemic. The real-time predictions of admissions are useful to adjust planning in hospitals to avoid exceeding their capacity.
PMID: 21416274 [PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/21416274