tetano
Editor, Senior Moderator
Epidemics. 2019 Oct 10:100373. doi: 10.1016/j.epidem.2019.100373. [Epub ahead of print] [h=1]Transmission of and susceptibility to seasonal influenza in Switzerland from 2003 to 2015.[/h]
Brugger J[SUP]1[/SUP], Althaus CL[SUP]2[/SUP].
[h=3]Author information[/h] 1 Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland. Electronic address: jon.brugger@students.unibe.ch. 2 Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland. Electronic address: christian.althaus@alumni.ethz.ch.
[h=3]Abstract[/h] Understanding the seasonal patterns of influenza transmission is critical to help plan public health measures for the management and control of epidemics. Mathematical models of infectious disease transmission have been widely used to quantify the transmissibility of and susceptibility to past influenza seasons in many countries. The objective of this study was to obtain a detailed picture of the transmission dynamics of seasonal influenza in Switzerland from 2003 to 2015. To this end, we developed a compartmental influenza transmission model taking into account social mixing between different age groups and seasonal forcing. We applied a Bayesian approach using Markov chain Monte Carlo (MCMC) methods to fit the model to the reported incidence of influenza-like-illness (ILI) and virological data from Sentinella, the Swiss Sentinel Surveillance Network. The maximal basic reproduction number, R[SUB]0[/SUB], ranged from 1.46 to 1.81 (median). Median estimates of susceptibility to influenza ranged from 29% to 98% for different age groups, and typically decreased with age. We also found a decline in ascertainability of influenza cases with age. Our study illustrates how influenza surveillance data from Switzerland can be integrated into a Bayesian modeling framework in order to assess age-specific transmission of and susceptibility to influenza.
Copyright ? 2019 The Authors. Published by Elsevier B.V. All rights reserved.
[h=4]KEYWORDS:[/h] Basic reproduction number; Influenza; Mathematical model; Surveillance; Switzerland
PMID: 31635972 DOI: 10.1016/j.epidem.2019.100373
Brugger J[SUP]1[/SUP], Althaus CL[SUP]2[/SUP].
[h=3]Author information[/h] 1 Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland. Electronic address: jon.brugger@students.unibe.ch. 2 Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland. Electronic address: christian.althaus@alumni.ethz.ch.
[h=3]Abstract[/h] Understanding the seasonal patterns of influenza transmission is critical to help plan public health measures for the management and control of epidemics. Mathematical models of infectious disease transmission have been widely used to quantify the transmissibility of and susceptibility to past influenza seasons in many countries. The objective of this study was to obtain a detailed picture of the transmission dynamics of seasonal influenza in Switzerland from 2003 to 2015. To this end, we developed a compartmental influenza transmission model taking into account social mixing between different age groups and seasonal forcing. We applied a Bayesian approach using Markov chain Monte Carlo (MCMC) methods to fit the model to the reported incidence of influenza-like-illness (ILI) and virological data from Sentinella, the Swiss Sentinel Surveillance Network. The maximal basic reproduction number, R[SUB]0[/SUB], ranged from 1.46 to 1.81 (median). Median estimates of susceptibility to influenza ranged from 29% to 98% for different age groups, and typically decreased with age. We also found a decline in ascertainability of influenza cases with age. Our study illustrates how influenza surveillance data from Switzerland can be integrated into a Bayesian modeling framework in order to assess age-specific transmission of and susceptibility to influenza.
Copyright ? 2019 The Authors. Published by Elsevier B.V. All rights reserved.
[h=4]KEYWORDS:[/h] Basic reproduction number; Influenza; Mathematical model; Surveillance; Switzerland
PMID: 31635972 DOI: 10.1016/j.epidem.2019.100373