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Theoretical basis to measure the impact of short-lasting control of an infectious disease on the epidemic peak
Authors
BJ Cowling
BJ Cowling
+53 more
BJ Cowling
C Fraser
Centers for Disease Control and Prevention Department of Health and Human Services
DG Kendall
E McBryde
E Vynnycky
F Brauer
G Rost
G Scalia-Tomba
H Andersson
H Nishiura
H Nishiura
H Nishiura
H Nishiura
H Nishiura
H Nishiura
H Nishiura
H Nishiura
H Nishiura
H Nishiura
H Nishiura
H Nishiura
H Risch
Hiroshi Nishiura
HJ Wearing
J Lessler
J Lessler
J Wallinga
JA Greenbaum
JC Heijne
JT Wu
K Dietz
LF White
M Igarashi
M Lipsitch
MG Roberts
N Halder
NM Ferguson
NTJ Bailey
NTJ Bailey
PY Boelle
RB Banks
Ryosuke Omori
S Cauchemez
S Cauchemez
S Cauchemez
T Reichert
TL Vincent
VE Pitzer
WO Kermack
World Health Organization
Y Itoh
Z Feng
Publication date
1 January 2011
Publisher
BioMed Central
Doi
Cite
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on
PubMed
Abstract
Background. While many pandemic preparedness plans have promoted disease control effort to lower and delay an epidemic peak, analytical methods for determining the required control effort and making statistical inferences have yet to be sought. As a first step to address this issue, we present a theoretical basis on which to assess the impact of an early intervention on the epidemic peak, employing a simple epidemic model. Methods. We focus on estimating the impact of an early control effort (e.g. unsuccessful containment), assuming that the transmission rate abruptly increases when control is discontinued. We provide analytical expressions for magnitude and time of the epidemic peak, employing approximate logistic and logarithmic-form solutions for the latter. Empirical influenza data (H1N1-2009) in Japan are analyzed to estimate the effect of the summer holiday period in lowering and delaying the peak in 2009. Results. Our model estimates that the epidemic peak of the 2009 pandemic was delayed for 21 days due to summer holiday. Decline in peak appears to be a nonlinear function of control-associated reduction in the reproduction number. Peak delay is shown to critically depend on the fraction of initially immune individuals. Conclusions. The proposed modeling approaches offer methodological avenues to assess empirical data and to objectively estimate required control effort to lower and delay an epidemic peak. Analytical findings support a critical need to conduct population-wide serological survey as a prior requirement for estimating the time of peak. © 2011 Omori and Nishiura; licensee BioMed Central Ltd.published_or_final_versio
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oai:hub.hku.hk:10722/133671
Last time updated on 01/06/2016
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