tetano
Editor, Senior Moderator
Int J Infect Dis
. 2020 Jun 16;S1201-9712(20)30478-1.
doi: 10.1016/j.ijid.2020.06.043. Online ahead of print.
On the Influenza Vaccination Policy Through Mathematical Modeling
Bin-Shenq Ho[SUP] 1 [/SUP], Kun-Mao Chao[SUP] 2 [/SUP]
Affiliations
Abstract
Objectives: Aiming at mitigating influenza transmission, the objective of the present study was to assess the timing of the vaccination program with vaccine capacity, strain mismatch, and priority group taken into consideration.
Methods: We fitted an age-structured dynamic transmission model to the laboratory data of the national influenza surveillance system to reconstruct a baseline scenario with which the vaccination scenarios of interest could be compared. Outcome measures were defined as the impacts on the seasonal epidemic: decompression of the epidemic peak, reduction of the epidemic burden, and change of the epidemic peak time.
Results: We found vaccine capacity building, though indispensable, could not guarantee substantial impacts on the seasonal influenza epidemic. Vaccine mismatch might greatly offset vaccine capacity building. Notably, advance vaccine distribution could compensate for some vaccine underperformance. In the case of a well-matched vaccine, advance vaccine distribution could even exploit its utility.
Conclusions: Our study indicates that timely vaccine distribution shall be put high on the agenda of seasonal influenza control policy. Besides, we provide a tangible platform for the policy makers to evaluate health policy impacts and to enhance risk communication with the public through mathematical modeling.
Keywords: Influenza; Modeling; Timing; Transmission; Vaccination.
. 2020 Jun 16;S1201-9712(20)30478-1.
doi: 10.1016/j.ijid.2020.06.043. Online ahead of print.
On the Influenza Vaccination Policy Through Mathematical Modeling
Bin-Shenq Ho[SUP] 1 [/SUP], Kun-Mao Chao[SUP] 2 [/SUP]
Affiliations
- PMID: 32561427
- DOI: 10.1016/j.ijid.2020.06.043
Abstract
Objectives: Aiming at mitigating influenza transmission, the objective of the present study was to assess the timing of the vaccination program with vaccine capacity, strain mismatch, and priority group taken into consideration.
Methods: We fitted an age-structured dynamic transmission model to the laboratory data of the national influenza surveillance system to reconstruct a baseline scenario with which the vaccination scenarios of interest could be compared. Outcome measures were defined as the impacts on the seasonal epidemic: decompression of the epidemic peak, reduction of the epidemic burden, and change of the epidemic peak time.
Results: We found vaccine capacity building, though indispensable, could not guarantee substantial impacts on the seasonal influenza epidemic. Vaccine mismatch might greatly offset vaccine capacity building. Notably, advance vaccine distribution could compensate for some vaccine underperformance. In the case of a well-matched vaccine, advance vaccine distribution could even exploit its utility.
Conclusions: Our study indicates that timely vaccine distribution shall be put high on the agenda of seasonal influenza control policy. Besides, we provide a tangible platform for the policy makers to evaluate health policy impacts and to enhance risk communication with the public through mathematical modeling.
Keywords: Influenza; Modeling; Timing; Transmission; Vaccination.