tetano
Editor, Senior Moderator
PeerJ. 2019 Jan 28;7:e6340. doi: 10.7717/peerj.6340. eCollection 2019.
[h=1]Optimizing influenza vaccine policies for controlling 2009-like pandemics and regular outbreaks.[/h] Chen SI[SUP]1[/SUP], Wu CY[SUP]1[/SUP], Wu YH[SUP]1[/SUP], Hsieh MW[SUP]1[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] [h=4]Background:[/h] This study examined the effectiveness of various vaccine policies against influenza. The transmission rate was calculated by use of the time-series influenza-like illness case during the year of 2009 and recent epidemics in Taiwan.
[h=4]Methods:[/h] We developed a stochastic compartmental model to analyze the transmission of influenza, where the population was stratified by location and age group, and the vaccine distribution was considered using the current policy. The simulation study compared the previous vaccine policy and a new policy with expanded coverage and various lengths of the vaccination campaign. The sensitivity analysis investigated different levels of vaccine efficacy to confirm the robustness of the recommended policies.
[h=4]Results:[/h] Doubling vaccine coverage can decrease the number of infections effectively in the regular epidemic scenario. However, a peak of infections occurs if the duration of implementing vaccination is too long. In the 2009-like pandemic scenario, both increasing vaccine doses and reducing the program's duration can mitigate infections, although the early outbreak restricts the effectiveness of vaccination programs.
[h=4]Conclusions:[/h] The finding indicates that only increasing vaccine coverage can reduce influenza infections. To avoid the peak of infections, it is also necessary to execute the vaccination activity immediately. Vaccine efficacy significantly impacts the vaccination policy's performance. When vaccine efficacy is low, neither increasing vaccination doses nor reducing vaccination timeframe prevents infections. Therefore, the variation in vaccine efficacy should be taken into account when making immunization policies against influenza.
[h=4]KEYWORDS:[/h] Disease model; Influenza; Simulation; Vaccine policy
PMID: 30713821 PMCID: PMC6354664 DOI: 10.7717/peerj.6340
[h=1]Optimizing influenza vaccine policies for controlling 2009-like pandemics and regular outbreaks.[/h] Chen SI[SUP]1[/SUP], Wu CY[SUP]1[/SUP], Wu YH[SUP]1[/SUP], Hsieh MW[SUP]1[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] [h=4]Background:[/h] This study examined the effectiveness of various vaccine policies against influenza. The transmission rate was calculated by use of the time-series influenza-like illness case during the year of 2009 and recent epidemics in Taiwan.
[h=4]Methods:[/h] We developed a stochastic compartmental model to analyze the transmission of influenza, where the population was stratified by location and age group, and the vaccine distribution was considered using the current policy. The simulation study compared the previous vaccine policy and a new policy with expanded coverage and various lengths of the vaccination campaign. The sensitivity analysis investigated different levels of vaccine efficacy to confirm the robustness of the recommended policies.
[h=4]Results:[/h] Doubling vaccine coverage can decrease the number of infections effectively in the regular epidemic scenario. However, a peak of infections occurs if the duration of implementing vaccination is too long. In the 2009-like pandemic scenario, both increasing vaccine doses and reducing the program's duration can mitigate infections, although the early outbreak restricts the effectiveness of vaccination programs.
[h=4]Conclusions:[/h] The finding indicates that only increasing vaccine coverage can reduce influenza infections. To avoid the peak of infections, it is also necessary to execute the vaccination activity immediately. Vaccine efficacy significantly impacts the vaccination policy's performance. When vaccine efficacy is low, neither increasing vaccination doses nor reducing vaccination timeframe prevents infections. Therefore, the variation in vaccine efficacy should be taken into account when making immunization policies against influenza.
[h=4]KEYWORDS:[/h] Disease model; Influenza; Simulation; Vaccine policy
PMID: 30713821 PMCID: PMC6354664 DOI: 10.7717/peerj.6340